diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..8896021 --- /dev/null +++ b/.flake8 @@ -0,0 +1,12 @@ +[flake8] +max-line-length = 120 +extend-ignore = E203,W503,E402 +exclude = + .git, + __pycache__, + build, + dist, + .venv, + .mypy_cache, + .tox, + .eggs diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index be61197..f1a35ad 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -13,39 +13,34 @@ jobs: test: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: - python-version: '3.9' + python-version: '3.11' - - name: Install Poetry - run: | - python -m pip install --upgrade pip - pip install poetry - pip install -r requirements.txt - - - name: Install dependencies - run: | - poetry install + - name: Install uv + uses: astral-sh/setup-uv@v1 + with: + version: "latest" - - name: Install dependencies including optional dependencies + - name: Install dependencies run: | - python -m pip install toml - EXTRAS=$(python .github/workflows/extract_extra_deps.py) - echo "EXTRAS=$EXTRAS" - poetry install $EXTRAS + uv sync - - name: Install pytest + - name: Install development dependencies run: | - pip install pytest + uv sync --extra dev - name: Run Flake8 - run: poetry run flake8 polymind/ + run: uv run flake8 media_gen/ - name: Run isort - run: poetry run isort --check-only . + run: uv run isort --check-only . + + - name: Run Black + run: uv run black --check . # Set fake environment variables for openai - name: Set environment variables @@ -56,4 +51,4 @@ jobs: echo "TAVILY_API_KEY=fake-api-key" >> $GITHUB_ENV - name: Run Pytest - run: poetry run pytest -vv --cov=polymind --cov-config=pyproject.toml -vv tests + run: uv run pytest -vv --cov=media_gen --cov-config=pyproject.toml diff --git a/.gitignore b/.gitignore index aa96837..bd4ef72 100644 --- a/.gitignore +++ b/.gitignore @@ -153,6 +153,9 @@ dmypy.json # Cython debug symbols cython_debug/ +# uv +.uv/ + # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore diff --git a/README.md b/README.md index 7bfabd7..ba7aef8 100644 --- a/README.md +++ b/README.md @@ -2,84 +2,146 @@ AI-powered image and video generation/understanding framework. -## Quick Start +## Setup -1. **Setup environment:** +## Setup + +1. **Install uv:** ```bash - cp env.example .env - # Add your REPLICATE_API_TOKEN to .env - pip install -r requirements.txt + curl -LsSf https://astral.sh/uv/install.sh | sh + # or with pip: pip install uv ``` -2. **Generate images:** +2. **Setup with uv:** ```bash - python media-gen/image_regen_pipeline.py --image-path photo.jpg --user-interests "make it more vibrant" + python setup_uv.py ``` -## Features +3. **Or manually with uv:** + ```bash + uv venv + uv sync + uv sync --extra dev # For development dependencies + cp env.example .env + ``` -- **Image Understanding**: Analyze images with AI vision -- **Image Generation**: Create new images via Replicate -- **Video Understanding**: Extract and analyze video scenes -- **Video Generation**: Generate videos from images and prompts +4. **Configure API keys:** + ```bash + cp env.example .env + # Add your OPENAI_API_KEY to .env + # Add your REPLICATE_API_TOKEN to .env + ``` ## Usage -### Image Regeneration +### Running with uv + +Use `uv run` to execute commands in the virtual environment: + ```bash -python media-gen/image_regen_pipeline.py \ - --image-path ~/Pictures/photo.jpg \ - --user-interests "convert to watercolor style" \ - --output-folder ~/Desktop \ - --aspect-ratio 16:9 +# Run any Python script +uv run python media_gen/image_regen_pipeline.py --image-path photo.jpg --user-interests "basketball, kapybara" + +# Run tests +uv run pytest + +# Run with specific Python version +uv run --python 3.11 python your_script.py +``` + +### Image Regeneration Pipeline + +Regenerate images with AI analysis and user preferences: + +```bash +uv run python media_gen/image_regen_pipeline.py --image-path photo.jpg --user-interests "basketball, kapybara" ``` **Options:** -- `--image-path`: Input image (required) +- `--image-path`: Input image path (required) - `--user-interests`: Regeneration preferences (required) - `--output-folder`: Output directory (default: `~/Downloads`) - `--aspect-ratio`: Image ratio (default: `1:1`) - `--output-format`: Format (default: `png`) - `--debug`: Show detailed prompts -### Video Understanding -```python -from tools.video_understanding_tool import VideoUnderstandingTool +**Examples:** +```bash +# Basic regeneration +uv run python media_gen/image_regen_pipeline.py --image-path landscape.jpg --user-interests "vintage style, steam punk" + +# Custom output and aspect ratio +uv run python media_gen/image_regen_pipeline.py \ + --image-path ~/Pictures/photo.jpg \ + --user-interests "make it modern and professional" \ + --output-folder ~/Desktop \ + --aspect-ratio 16:9 +``` + +### Video Regeneration Pipeline + +Process videos with AI understanding and generation: + +```bash +uv run python media_gen/video_regen_pipeline.py --video-path video.mp4 --user-interests "basketball, kapybara" +``` + +**Options:** +- `--video-path`: Input video path (required) +- `--user-interests`: Processing preferences (required) +- `--output-folder`: Output directory (default: `~/Downloads`) +- `--screenshot-interval`: Seconds between screenshots (default: `2.0`) -video_tool = VideoUnderstandingTool() -result = video_tool.run({ - "video_path": "video.mp4", - "user_preference": "cinematic lighting", - "screenshot_interval": 2.0 -}) +## Development + +### Adding Dependencies + +```bash +# Add a new dependency +uv add package-name + +# Add a development dependency +uv add --dev package-name + +# Update dependencies +uv sync ``` -### Video Generation -```python -from tools.replicate_video_gen import ReplicateVideoGen +### Running Tests + +```bash +# Run all tests +uv run pytest + +# Run with coverage +uv run pytest --cov=media_gen -video_gen = ReplicateVideoGen() -result = video_gen.run({ - "image": "image.jpg", - "prompt": "serene landscape with gentle movement" -}) +# Run specific test file +uv run pytest media_gen/tests/test_image_understanding.py ``` -## Testing +### Code Quality ```bash -# Test image generation -python media-gen/image_regen_pipeline.py --image-path media-gen/test_scripts/test_image.png --user-interests "enhance visual appeal" +# Format code +uv run black . + +# Sort imports +uv run isort . -# Test video tools -python media-gen/test_scripts/test_video_understanding_tool.py -python media-gen/test_scripts/test_replicate_video_gen.py +# Type checking +uv run mypy media_gen/ + +# Linting +uv run flake8 media_gen/ ``` -## Architecture +## Extensibility + +The framework is designed for easy extension. You can create custom tools for: + +- **Arbitrary image generation models** (Stable Diffusion, DALL-E, etc.) +- **Video generation models** (Runway, Pika Labs, etc.) +- **Custom analysis tools** (scene detection, content filtering, etc.) -- **`pipeline.py`**: Core pipeline infrastructure -- **`image_regen_pipeline.py`**: Image regeneration CLI -- **`video_regen_pipeline.py`**: Video processing pipeline -- **`tools/`**: Media generation and understanding tools -- **`utils/`**: Utility functions for video processing \ No newline at end of file +Tools follow the base class in `media_gen/tools/media_gen_tool_base.py` for consistent integration. diff --git a/__init__.py b/__init__.py index 03185ec..5a5e646 100644 --- a/__init__.py +++ b/__init__.py @@ -1,7 +1,7 @@ """ Media Regeneration Framework -A comprehensive AI-powered media generation and understanding framework +A comprehensive AI-powered media generation and understanding framework for images and videos. """ diff --git a/media-gen/tests/test_replicate_video_gen.py b/media-gen/tests/test_replicate_video_gen.py deleted file mode 100644 index 2255bce..0000000 --- a/media-gen/tests/test_replicate_video_gen.py +++ /dev/null @@ -1,174 +0,0 @@ -#!/usr/bin/env python3 -""" -Test script for Replicate video generation tool using WAN 2.2 i2v fast model. - -This script demonstrates how to use the ReplicateVideoGen tool to generate -videos from images and text prompts using the WAN 2.2 i2v fast model. - -Usage: - python tests/test_replicate_video_gen.py - -Requirements: -- Replicate API token set in environment variables -- Test image file: tests/test_image.png -""" - -import base64 -import os -import sys - -from pathlib import Path - -# Add parent directory to path for imports -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) - -from tools.replicate_video_gen import ReplicateVideoGen - - -def test_replicate_video_generation(): - """Test the Replicate video generation functionality.""" - print("šŸŽ¬ Replicate Video Generation Test") - print("=" * 50) - - # Check for Replicate API token - if not os.getenv("REPLICATE_API_TOKEN"): - print("āŒ REPLICATE_API_TOKEN not found in environment variables") - print("Please set your Replicate API token:") - print("export REPLICATE_API_TOKEN='your_token_here'") - return False - - # Path to test image - test_image_path = Path(__file__).parent / "test_image.png" - - if not test_image_path.exists(): - print(f"āŒ Test image not found at: {test_image_path}") - print("Please ensure test_image.png exists in the tests directory") - return False - - print(f"āœ… Test image found: {test_image_path}") - print(f"šŸ“ File size: {test_image_path.stat().st_size:,} bytes") - - # Initialize the video generation tool - video_gen = ReplicateVideoGen() - - # Test parameters - test_prompt = ( - "Close-up shot of an elderly sailor wearing a yellow raincoat, " - "seated on the deck of a catamaran, slowly puffing on a pipe. " - "His cat lies quietly beside him with eyes closed, enjoying the " - "calm. The warm glow of the setting sun bathes the scene, with " - "gentle waves lapping against the hull and a few seabirds " - "circling slowly above. The camera slowly pushes in, capturing " - "this peaceful and harmonious moment." - ) - - print(f"šŸ“ Test prompt: {test_prompt[:100]}...") - print() - - # Generate video - print("šŸ”„ Generating video from image and text prompt...") - print("-" * 50) - - try: - result = video_gen.run({ - "image": str(test_image_path), - "prompt": test_prompt, - "output_folder": "~/Downloads/polymind_video_generation", - "output_format": "mp4" - }) - - if result["video_path"]: - print(f"āœ… Video generated successfully!") - print(f"šŸ“ Video saved to: {result['video_path']}") - print(f"šŸ“Š Generation info: {result['generation_info']}") - - # Check if file exists and get size - video_path = Path(result["video_path"]) - if video_path.exists(): - print(f"šŸ“ Video file size: {video_path.stat().st_size:,} bytes") - else: - print("āš ļø Video file not found at expected location") - - return True - else: - print(f"āŒ Video generation failed: {result['generation_info']}") - return False - - except Exception as e: - print(f"āŒ Video generation failed with exception: {e}") - return False - - -def test_with_data_uri(): - """Test video generation using data URI for image input.""" - print("\nšŸ”„ Testing with data URI image input...") - print("-" * 50) - - # Path to test image - test_image_path = Path(__file__).parent / "test_image.png" - - if not test_image_path.exists(): - print("āŒ Test image not found for data URI test") - return False - - # Convert image to data URI - with open(test_image_path, 'rb') as file: - data = base64.b64encode(file.read()).decode('utf-8') - data_uri = f"data:application/octet-stream;base64,{data}" - - print(f"āœ… Converted image to data URI ({len(data_uri)} chars)") - - # Initialize the video generation tool - video_gen = ReplicateVideoGen() - - # Test parameters - test_prompt = "A serene landscape with gentle movement and natural lighting" - - try: - result = video_gen.run({ - "image": data_uri, - "prompt": test_prompt, - "output_folder": "~/Downloads/polymind_video_generation", - "output_format": "mp4" - }) - - if result["video_path"]: - print(f"āœ… Video generated successfully with data URI!") - print(f"šŸ“ Video saved to: {result['video_path']}") - return True - else: - print(f"āŒ Video generation failed: {result['generation_info']}") - return False - - except Exception as e: - print(f"āŒ Video generation failed with exception: {e}") - return False - - -def main(): - """Run all video generation tests.""" - print("šŸŽ¬ Replicate Video Generation Tool Tests") - print("=" * 60) - - # Test 1: Basic video generation - success1 = test_replicate_video_generation() - - # Test 2: Data URI input - success2 = test_with_data_uri() - - # Summary - print("\nšŸ“Š Test Summary") - print("=" * 60) - print(f"āœ… Basic video generation: {'PASS' if success1 else 'FAIL'}") - print(f"āœ… Data URI input: {'PASS' if success2 else 'FAIL'}") - - if success1 and success2: - print("\nšŸŽ‰ All tests passed!") - else: - print("\nāš ļø Some tests failed. Check the output above for details.") - - print("\nšŸ’” Generated videos are saved to ~/Downloads/polymind_video_generation/") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/media-gen/__init__.py b/media_gen/__init__.py similarity index 60% rename from media-gen/__init__.py rename to media_gen/__init__.py index d371455..feb23c7 100644 --- a/media-gen/__init__.py +++ b/media_gen/__init__.py @@ -6,12 +6,6 @@ Polymind agents. """ -from .tools import ( - ImageGenerationTool, - VideoGenerationTool -) +from .tools import ImageGenerationTool, VideoGenerationTool -__all__ = [ - "ImageGenerationTool", - "VideoGenerationTool" -] \ No newline at end of file +__all__ = ["ImageGenerationTool", "VideoGenerationTool"] diff --git a/media-gen/image_regen_pipeline.py b/media_gen/image_regen_pipeline.py similarity index 76% rename from media-gen/image_regen_pipeline.py rename to media_gen/image_regen_pipeline.py index c8739f9..0975260 100644 --- a/media-gen/image_regen_pipeline.py +++ b/media_gen/image_regen_pipeline.py @@ -6,7 +6,7 @@ 2. Generate new image based on analysis and user preferences Usage: - python image_regen_pipeline.py --image-path + python image_regen_pipeline.py --image-path --user-interests Example: @@ -20,13 +20,13 @@ import logging import os import sys -from typing import Any, Dict - from pathlib import Path +from typing import Any, Dict # Load environment variables from .env file if it exists try: from dotenv import load_dotenv + load_dotenv() except ImportError: # dotenv not installed, continue without it @@ -45,23 +45,23 @@ def expand_path(path: str) -> str: class MediaRegenerationPipeline(MediaGenerationPipeline): """ Pipeline for regenerating media (currently images). - + Workflow: 1. Analyze original image using image understanding 2. Generate new image based on analysis and user preferences """ system_prompt: str = """ - Please analyze the content of the image, and we want to create a new - image generation prompt, combining the original image and the user + Please analyze the content of the image, and we want to create a new + image generation prompt, combining the original image and the user preferences. - + For the generated image generation prompt, follow the below requirement: - 1. The image generation prompt should be as close as possible to the + 1. The image generation prompt should be as close as possible to the original image, but organically combine the user preferences. - 2. Not just replicate the object and scene in the original image, but + 2. Not just replicate the object and scene in the original image, but also understanding the image style, lighting, and composition. - 3. The generated prompt should not be long (<100 words) and can be + 3. The generated prompt should not be long (<100 words) and can be easily understood by the image generation model. 4. The output should be a JSON object with the following fields: { @@ -69,17 +69,17 @@ class MediaRegenerationPipeline(MediaGenerationPipeline): } The user preferences are as below: """ - + def __init__( self, image_understanding_tool: ImageUnderstandingTool, image_generation_tool: Any, # BaseTool type name: str = "media_regeneration", - debug: bool = False + debug: bool = False, ): """ Initialize the media regeneration pipeline. - + Args: image_understanding_tool: Tool for analyzing images image_generation_tool: Tool for generating images @@ -88,7 +88,7 @@ def __init__( """ super().__init__(name) self.debug = debug - + # Add image understanding step self.add_step( PipelineStep( @@ -97,16 +97,14 @@ def __init__( input_mapping={ "original_image": "images", "system_prompt": "prompt", - "user_preferences": "user_preferences" - }, - output_mapping={ - "analysis": "image_analysis" + "user_preferences": "user_preferences", }, + output_mapping={"analysis": "image_analysis"}, transform_input=self._combine_prompt_and_preferences, - transform_output=self._extract_analysis + transform_output=self._extract_analysis, ) ) - + # Add image generation step self.add_step( PipelineStep( @@ -116,35 +114,32 @@ def __init__( "image_analysis": "prompt", "output_folder": "output_folder", "aspect_ratio": "aspect_ratio", - "output_format": "output_format" + "output_format": "output_format", }, - output_mapping={ - "image_path": "image_path", - "generation_info": "generation_info" - }, - transform_input=self._use_analysis_as_prompt + output_mapping={"image_path": "image_path", "generation_info": "generation_info"}, + transform_input=self._use_analysis_as_prompt, ) ) - + def regenerate( self, image_path: str, user_interests: str, output_folder: str = "~/Downloads", aspect_ratio: str = "1:1", - output_format: str = "png" + output_format: str = "png", ) -> Dict[str, Any]: """ Regenerate an image based on the original. - + Args: image_path: Path to the original image user_interests: User preferences for regeneration - output_folder: Folder to save generated image + output_folder: Folder to save generated image (default: ~/Downloads) aspect_ratio: Aspect ratio for generated image output_format: Output format for generated image - + Returns: Dictionary containing: - generated_image_path: Path to the generated image @@ -159,55 +154,49 @@ def regenerate( "output_folder": output_folder, "aspect_ratio": aspect_ratio, "output_format": output_format, - "return_json": True # Request JSON output from image understanding + "return_json": True, # Request JSON output from image understanding } - + # Run pipeline result = self.run(input_data) - + return result - - def _combine_prompt_and_preferences( - self, tool_input: Dict[str, Any] - ) -> Dict[str, Any]: + + def _combine_prompt_and_preferences(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: """Combine system prompt with user preferences.""" system_prompt = tool_input.get("prompt", "") user_preferences = tool_input.get("user_preferences", "") - + if user_preferences: - combined_prompt = ( - f"{system_prompt}\n\n\t {user_preferences}" - ) + combined_prompt = f"{system_prompt}\n\n\t {user_preferences}" else: combined_prompt = system_prompt - + # Debug: Print the prompt being sent to image understanding if self.debug: print("\nšŸ” DEBUG - Image Understanding Prompt:") print(f" {combined_prompt}") - + return {**tool_input, "prompt": combined_prompt} - - def _extract_analysis( - self, tool_output: Dict[str, Any] - ) -> Dict[str, Any]: + + def _extract_analysis(self, tool_output: Dict[str, Any]) -> Dict[str, Any]: """ Extract analysis text and image generation prompt from JSON output. - + The image understanding tool returns JSON with the structure: { "image_generation_prompt": "The image generation prompt" } - + Handles responses that may be wrapped in markdown code blocks. """ analysis = tool_output.get("analysis", "") - + # Debug: Print the raw analysis received from image understanding if self.debug: print("\nšŸ” DEBUG - Raw Image Understanding Analysis:") print(f" {analysis}") - + # Clean the analysis text - remove markdown code block markers cleaned_analysis = analysis.strip() if cleaned_analysis.startswith("```json"): @@ -217,40 +206,35 @@ def _extract_analysis( if cleaned_analysis.endswith("```"): cleaned_analysis = cleaned_analysis[:-3] # Remove trailing ``` cleaned_analysis = cleaned_analysis.strip() - + # Try to parse as JSON and extract the image_generation_prompt try: analysis_json = json.loads(cleaned_analysis) - if (isinstance(analysis_json, dict) and - "image_generation_prompt" in analysis_json): + if isinstance(analysis_json, dict) and "image_generation_prompt" in analysis_json: extracted_prompt = analysis_json["image_generation_prompt"] - + # Debug: Print the extracted prompt if self.debug: print("\nšŸ” DEBUG - Extracted Image Generation Prompt:") print(f" {extracted_prompt}") - + return {"analysis": extracted_prompt} else: # JSON parsed but doesn't have expected structure if self.debug: - print("\nāš ļø DEBUG - JSON parsed but missing " - "image_generation_prompt field") + print("\nāš ļø DEBUG - JSON parsed but missing " "image_generation_prompt field") return {"analysis": cleaned_analysis} except json.JSONDecodeError: # Not valid JSON, use the cleaned analysis if self.debug: - print("\nāš ļø DEBUG - Analysis is not valid JSON, " - "using cleaned text") + print("\nāš ļø DEBUG - Analysis is not valid JSON, " "using cleaned text") return {"analysis": cleaned_analysis} - - def _use_analysis_as_prompt( - self, tool_input: Dict[str, Any] - ) -> Dict[str, Any]: + + def _use_analysis_as_prompt(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: """ Use the image analysis (which already incorporates user preferences) as the final generation prompt. - + The image understanding tool generates a prompt that combines the original image content with user preferences, so we use that directly without further modification. @@ -260,87 +244,83 @@ def _use_analysis_as_prompt( print("\nšŸ” DEBUG - All Tool Input Keys:") for key, value in tool_input.items(): print(f" {key}: {value}") - + # The image_analysis has been mapped to "prompt" by the input mapping final_prompt = tool_input.get("prompt", "") - + # Debug: Print what we received if self.debug: print("\nšŸ” DEBUG - Image Generation Input:") print(f" prompt (from image_analysis): {final_prompt}") - + # Use the prompt directly as the final prompt # The image understanding tool already incorporates user preferences - + # Debug: Print the prompt being sent to image generation if self.debug: print("\nšŸŽØ DEBUG - Image Generation Prompt:") print(f" {final_prompt}") - + return {**tool_input, "prompt": final_prompt} def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """ Execute the pipeline with the given input. - + Args: input_data: Initial input data for the pipeline - + Returns: Final output from the last step """ # Expand paths expanded_image_path = expand_path(input_data["original_image"][0]) expanded_output_folder = expand_path(input_data["output_folder"]) - + # Create output directory if it doesn't exist Path(expanded_output_folder).mkdir(parents=True, exist_ok=True) - + # Update input data with expanded paths input_data["original_image"] = [expanded_image_path] input_data["output_folder"] = expanded_output_folder - + # Debug: Show initial input if self.debug: print("\nšŸ” DEBUG - Initial Pipeline Input:") for key, value in input_data.items(): print(f" {key}: {value}") - + # Run the parent pipeline with custom execution - self.logger.info( - f"Starting pipeline execution with {len(self.steps)} steps" - ) - + self.logger.info(f"Starting pipeline execution with {len(self.steps)} steps") + current_input = input_data.copy() - + for i, step in enumerate(self.steps): - self.logger.info( - f"Executing step {i+1}/{len(self.steps)}: {step.name}" - ) - + self.logger.info(f"Executing step {i + 1}/{len(self.steps)}: {step.name}") + # Debug: Show input to this step if self.debug: - print(f"\nšŸ” DEBUG - Input to step {i+1} ({step.name}):") + print(f"\nšŸ” DEBUG - Input to step {i + 1} ({step.name}):") for key, value in current_input.items(): print(f" {key}: {value}") - + # Execute the step executor = PipelineStepExecutor(step) step_output = executor.execute(current_input) - + # Debug: Show output from this step if self.debug: - print(f"\nšŸ” DEBUG - Output from step {i+1} ({step.name}):") + print(f"\nšŸ” DEBUG - Output from step {i + 1} ({step.name}):") for key, value in step_output.items(): print(f" {key}: {value}") - + # Merge step output with current input for next step current_input.update(step_output) - + self.logger.debug(f"Step {step.name} output: {step_output}") - + self.logger.info("Pipeline execution completed") - + # Debug: Show final result if self.debug: print("\nšŸ” DEBUG - Final Pipeline Result Keys:") @@ -349,103 +329,76 @@ def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]: print(f" {key}: {str(value)[:100]}...") else: print(f" {key}: {value}") - + return current_input def setup_logging(): """Setup logging.""" - logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' - ) + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") def main(): """Main function for command-line execution.""" - parser = argparse.ArgumentParser( - description="Regenerate an image based on user interests" - ) + parser = argparse.ArgumentParser(description="Regenerate an image based on user interests") parser.add_argument( - "--image-path", - required=True, - help="Path to the original image file (supports ~ for home directory)" + "--image-path", required=True, help="Path to the original image file (supports ~ for home directory)" ) + parser.add_argument("--user-interests", required=True, help="User interests/preferences for regeneration") parser.add_argument( - "--user-interests", - required=True, - help="User interests/preferences for regeneration" - ) - parser.add_argument( - "--output-folder", - default="~/Downloads", - help="Output folder for generated image (default: ~/Downloads)" - ) - parser.add_argument( - "--aspect-ratio", - default="1:1", - help="Aspect ratio for generated image (default: 1:1)" - ) - parser.add_argument( - "--output-format", - default="png", - help="Output format for generated image (default: png)" + "--output-folder", default="~/Downloads", help="Output folder for generated image (default: ~/Downloads)" ) + parser.add_argument("--aspect-ratio", default="1:1", help="Aspect ratio for generated image (default: 1:1)") + parser.add_argument("--output-format", default="png", help="Output format for generated image (default: png)") parser.add_argument( "--generator", choices=["replicate"], default="replicate", - help="Image generation service to use (default: replicate)" - ) - parser.add_argument( - "--debug", - action="store_true", - help="Enable debug output to see prompts used in each step" + help="Image generation service to use (default: replicate)", ) - + parser.add_argument("--debug", action="store_true", help="Enable debug output to see prompts used in each step") + args = parser.parse_args() - + # Expand paths expanded_image_path = expand_path(args.image_path) - + # Validate input image exists if not Path(expanded_image_path).exists(): print(f"Error: Image file not found: {expanded_image_path}") sys.exit(1) - + # Setup logging setup_logging() - + try: # Initialize tools image_understanding = ImageUnderstandingTool() - + # Initialize image generation tool image_gen = ReplicateImageGen() - + # Create pipeline pipeline = MediaRegenerationPipeline( - image_understanding_tool=image_understanding, - image_generation_tool=image_gen, - debug=args.debug + image_understanding_tool=image_understanding, image_generation_tool=image_gen, debug=args.debug ) - + # Regenerate image result = pipeline.regenerate( image_path=args.image_path, user_interests=args.user_interests, output_folder=args.output_folder, aspect_ratio=args.aspect_ratio, - output_format=args.output_format + output_format=args.output_format, ) - + # Output results - print("\n" + "="*60) + print("\n" + "=" * 60) print("šŸŽØ MEDIA REGENERATION COMPLETE") - print("="*60) - + print("=" * 60) + # Get image path from the result - generated_path = result.get('image_path', '') + generated_path = result.get("image_path", "") if generated_path: print(f"šŸ“ Image stored at: {generated_path}") # Show relative path if it's in Downloads @@ -455,38 +408,36 @@ def main(): print(f" šŸ“‚ Relative to Downloads: {relative_path}") else: print("āŒ No image path returned") - + print("\nšŸ” Image Analysis:") - analysis = result.get('image_analysis', '') + analysis = result.get("image_analysis", "") if analysis: print(f" {analysis}") else: print(" No analysis available") - + print("\nšŸŽÆ Final Generation Prompt:") # Get the final prompt from generation info - generation_info = result.get('generation_info', {}) - final_prompt = generation_info.get('prompt', '') + generation_info = result.get("generation_info", {}) + final_prompt = generation_info.get("prompt", "") if final_prompt: print(f" {final_prompt}") else: print(" No generation prompt available") - + if generation_info: print("\nšŸ“Š Generation Info:") # Remove the prompt from metadata to avoid duplication - filtered_info = { - k: v for k, v in generation_info.items() if k != 'prompt' - } + filtered_info = {k: v for k, v in generation_info.items() if k != "prompt"} for key, value in filtered_info.items(): print(f" {key}: {value}") - - print("="*60) - + + print("=" * 60) + except Exception as e: print(f"Error: {e}") sys.exit(1) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/media-gen/pipeline.py b/media_gen/pipeline.py similarity index 88% rename from media-gen/pipeline.py rename to media_gen/pipeline.py index 105d84f..de7de94 100644 --- a/media-gen/pipeline.py +++ b/media_gen/pipeline.py @@ -8,7 +8,7 @@ import logging from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Optional, Union +from typing import Any, Callable, Dict, List, Optional from polymind.core.tool import BaseTool @@ -17,7 +17,7 @@ class PipelineStep: """ Represents a single step in a media generation pipeline. - + Attributes: name: Unique identifier for the step tool: The tool to execute in this step @@ -26,64 +26,61 @@ class PipelineStep: transform_input: Optional function to transform input before passing to tool transform_output: Optional function to transform output after tool execution """ + name: str tool: BaseTool input_mapping: Dict[str, str] output_mapping: Dict[str, str] - transform_input: Optional[ - Callable[[Dict[str, Any]], Dict[str, Any]] - ] = None - transform_output: Optional[ - Callable[[Dict[str, Any]], Dict[str, Any]] - ] = None + transform_input: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None + transform_output: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None class PipelineStepExecutor: """ Executes a single pipeline step with input/output transformations. """ - + def __init__(self, step: PipelineStep): """Initialize the step executor.""" self.step = step self.logger = logging.getLogger(f"PipelineStepExecutor.{step.name}") - + def execute(self, pipeline_input: Dict[str, Any]) -> Dict[str, Any]: """ Execute the pipeline step. - + Args: pipeline_input: Current pipeline input state - + Returns: Tool output mapped to pipeline output format """ self.logger.info(f"Executing step: {self.step.name}") - + # Map pipeline input to tool input tool_input = self._map_input(pipeline_input) - + # Apply input transformation if provided if self.step.transform_input: tool_input = self.step.transform_input(tool_input) - + self.logger.debug(f"Tool input: {tool_input}") - + # Execute the tool tool_output = self.step.tool.run(tool_input) - + self.logger.debug(f"Tool output: {tool_output}") - + # Apply output transformation if provided if self.step.transform_output: tool_output = self.step.transform_output(tool_output) - + # Map tool output to pipeline output pipeline_output = self._map_output(tool_output) - + self.logger.info(f"Step {self.step.name} completed successfully") return pipeline_output - + def _map_input(self, pipeline_input: Dict[str, Any]) -> Dict[str, Any]: """Map pipeline input to tool input using input_mapping.""" tool_input = {} @@ -91,7 +88,7 @@ def _map_input(self, pipeline_input: Dict[str, Any]) -> Dict[str, Any]: if pipeline_key in pipeline_input: tool_input[tool_key] = pipeline_input[pipeline_key] return tool_input - + def _map_output(self, tool_output: Dict[str, Any]) -> Dict[str, Any]: """Map tool output to pipeline output using output_mapping.""" pipeline_output = {} @@ -104,66 +101,66 @@ def _map_output(self, tool_output: Dict[str, Any]) -> Dict[str, Any]: class MediaGenerationPipeline: """ A modular pipeline for media generation tasks. - + This pipeline allows chaining multiple tools together with configurable input/output mappings and transformations. It supports static execution where each step's output becomes the next step's input. """ - + def __init__(self, name: str): """ Initialize the pipeline. - + Args: name: Name of the pipeline for logging and identification """ self.name = name self.steps: List[PipelineStep] = [] self.logger = logging.getLogger(f"MediaGenerationPipeline.{name}") - - def add_step(self, step: PipelineStep) -> 'MediaGenerationPipeline': + + def add_step(self, step: PipelineStep) -> "MediaGenerationPipeline": """ Add a step to the pipeline. - + Args: step: PipelineStep to add - + Returns: Self for method chaining """ self.steps.append(step) self.logger.info(f"Added step: {step.name}") return self - + def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """ Execute the pipeline with the given input. - + Args: input_data: Initial input data for the pipeline - + Returns: Final output from the last step """ self.logger.info(f"Starting pipeline execution with {len(self.steps)} steps") - + current_input = input_data.copy() - + for i, step in enumerate(self.steps): self.logger.info(f"Executing step {i+1}/{len(self.steps)}: {step.name}") - + # Execute the step executor = PipelineStepExecutor(step) step_output = executor.execute(current_input) - + # Merge step output with current input for next step current_input.update(step_output) - + self.logger.debug(f"Step {step.name} output: {step_output}") - + self.logger.info("Pipeline execution completed") return current_input - + def get_step_names(self) -> List[str]: """Get the names of all steps in the pipeline.""" - return [step.name for step in self.steps] \ No newline at end of file + return [step.name for step in self.steps] diff --git a/media-gen/test_scripts/README.md b/media_gen/test_scripts/README.md similarity index 100% rename from media-gen/test_scripts/README.md rename to media_gen/test_scripts/README.md diff --git a/media-gen/test_scripts/test_image.png b/media_gen/test_scripts/test_image.png similarity index 100% rename from media-gen/test_scripts/test_image.png rename to media_gen/test_scripts/test_image.png diff --git a/media-gen/test_scripts/test_image_understanding.py b/media_gen/test_scripts/test_image_understanding.py similarity index 88% rename from media-gen/test_scripts/test_image_understanding.py rename to media_gen/test_scripts/test_image_understanding.py index 5a9d7a6..7e85b63 100644 --- a/media-gen/test_scripts/test_image_understanding.py +++ b/media_gen/test_scripts/test_image_understanding.py @@ -10,45 +10,45 @@ import os import sys +from pathlib import Path from dotenv import load_dotenv -from pathlib import Path # Add the parent directory to the path to import the tool sys.path.insert(0, str(Path(__file__).parent.parent)) -from tools.image_understanding_tool import ImageUnderstandingTool +from media_gen.tools.image_understanding_tool import ImageUnderstandingTool def main(): """Run the integration test.""" print("=== Image Understanding Integration Test ===") print("This test will analyze test_image.png and generate an image generation prompt.\n") - + # Load environment variables load_dotenv() - + # Check for OpenAI API key api_key = os.getenv("OPENAI_API_KEY") if not api_key: print("āŒ Error: OPENAI_API_KEY not found in environment.") print("Please set it in your .env file.") return - + # Get test image path test_image_path = Path(__file__).parent / "test_image.png" - + if not test_image_path.exists(): print(f"āŒ Error: Test image not found at {test_image_path}") return - + print(f"āœ… Found test image: {test_image_path}") print(f"āœ… API key available: {api_key[:8]}...") print() - + # Initialize tool tool = ImageUnderstandingTool() - + # Define the prompt for image generation prompt = ( "Analyze this image and create an image generation prompt that could be used " @@ -57,36 +57,31 @@ def main(): "lighting, mood, image style, composition, colors, and textures. The prompt should be " "less than 100 words." ) - + print("šŸ“ Prompt: Generate image generation prompt") print("šŸ”„ Calling OpenAI API...") - + try: - result = tool.run({ - "prompt": prompt, - "images": [str(test_image_path)], - "return_json": False, - "max_tokens": 600 - }) - + result = tool.run({"prompt": prompt, "images": [str(test_image_path)], "return_json": False, "max_tokens": 600}) + print("āœ… Analysis completed successfully!") print("\nšŸ“‹ Generated Image Generation Prompt:") print("-" * 50) print(result["analysis"]) print("-" * 50) - + print("\nšŸ“Š Metadata:") print(f" Model: {result['metadata']['model']}") print(f" Total tokens: {result['metadata']['tokens_used']}") print(f" Prompt tokens: {result['metadata']['prompt_tokens']}") print(f" Completion tokens: {result['metadata']['completion_tokens']}") - + except Exception as e: print(f"āŒ Error during analysis: {e}") return - + print("\nāœ… Integration test completed successfully!") if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/media-gen/test_scripts/test_replicate_image_gen.py b/media_gen/test_scripts/test_replicate_image_gen.py similarity index 87% rename from media-gen/test_scripts/test_replicate_image_gen.py rename to media_gen/test_scripts/test_replicate_image_gen.py index 842d2f6..adc12d0 100644 --- a/media-gen/test_scripts/test_replicate_image_gen.py +++ b/media_gen/test_scripts/test_replicate_image_gen.py @@ -13,15 +13,15 @@ import os import sys +from pathlib import Path from dotenv import load_dotenv -from pathlib import Path # Add parent directory to path for imports -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +sys.path.append(os.path.join(os.path.dirname(__file__), "..")) try: - from tools.replicate_image_gen import ReplicateImageGen + from media_gen.tools.replicate_image_gen import ReplicateImageGen except ImportError as e: if "replicate" in str(e): print("āŒ Replicate package not installed. Please install it with:") @@ -35,24 +35,24 @@ def main(): """Generate a single image with the specified prompt.""" print("šŸš€ Replicate Image Generation Integration Test") print("=" * 60) - + # Check if Replicate API token is available if not os.getenv("REPLICATE_API_TOKEN"): print("āŒ REPLICATE_API_TOKEN environment variable not set") print("Please set your Replicate API token to run integration tests") return - - token = os.getenv('REPLICATE_API_TOKEN') + + token = os.getenv("REPLICATE_API_TOKEN") print(f"āœ… Replicate API token found: {token[:10]}...") - + # Create Downloads directory if it doesn't exist downloads_dir = Path.home() / "Downloads" / "polymind_generated_images" downloads_dir.mkdir(parents=True, exist_ok=True) print(f"āœ… Output directory: {downloads_dir.absolute()}") - + # Initialize the tool image_gen = ReplicateImageGen() - + # The specific prompt from the example prompt = ( "Create a whimsical scene inside a modern subway train featuring a " @@ -65,22 +65,19 @@ def main(): "feathers, emphasizing their cuteness while maintaining a playful " "and friendly mood." ) - + print("\nšŸŽØ Generating image with prompt:") print(f"'{prompt[:100]}...'") print() - + try: # Generate the image - result = image_gen.run({ - "prompt": prompt, - "seed": 246764, - "aspect_ratio": "4:3", - "output_folder": str(downloads_dir) - }) - + result = image_gen.run( + {"prompt": prompt, "seed": 246764, "aspect_ratio": "4:3", "output_folder": str(downloads_dir)} + ) + print(f"Result: {result}") - + if result["image_path"] and os.path.exists(result["image_path"]): file_size = os.path.getsize(result["image_path"]) print("āœ… Image generated successfully!") @@ -89,9 +86,9 @@ def main(): print(f"šŸŽÆ Generation info: {result['generation_info']}") else: print("āŒ Image generation failed") - error_msg = result.get('generation_info', {}).get('error', 'Unknown error') + error_msg = result.get("generation_info", {}).get("error", "Unknown error") print(f"Error: {error_msg}") - + # Check for specific error types and provide helpful guidance if "api token" in error_msg.lower(): print("\nšŸ’” To fix this issue:") @@ -103,16 +100,17 @@ def main(): print("1. Check that the model name is correct") print("2. Ensure the model is publicly available") print("3. Verify your account has access to the model") - + except Exception as e: print(f"\nāŒ Integration test failed with error: {e}") import traceback + traceback.print_exc() if __name__ == "__main__": # Load environment variables load_dotenv() - + # Run the integration test - main() \ No newline at end of file + main() diff --git a/media-gen/test_scripts/test_replicate_video_gen.py b/media_gen/test_scripts/test_replicate_video_gen.py similarity index 87% rename from media-gen/test_scripts/test_replicate_video_gen.py rename to media_gen/test_scripts/test_replicate_video_gen.py index 8ab9349..19ff97c 100644 --- a/media-gen/test_scripts/test_replicate_video_gen.py +++ b/media_gen/test_scripts/test_replicate_video_gen.py @@ -22,17 +22,17 @@ import os import sys +from pathlib import Path from dotenv import load_dotenv -from pathlib import Path # Add parent directory to path for imports -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +sys.path.append(os.path.join(os.path.dirname(__file__), "..")) # Load environment variables from .env file -load_dotenv(os.path.join(os.path.dirname(__file__), '..', '.env')) +load_dotenv(os.path.join(os.path.dirname(__file__), "..", ".env")) -from tools.replicate_video_gen import ReplicateVideoGen +from media_gen.tools.replicate_video_gen import ReplicateVideoGen def main(): @@ -42,7 +42,7 @@ def main(): print("āŒ REPLICATE_API_TOKEN not found in environment variables") print("Please set: export REPLICATE_API_TOKEN='your_token_here'") sys.exit(1) - + # Parse command line arguments if len(sys.argv) > 1: image_path = sys.argv[1] @@ -52,37 +52,37 @@ def main(): else: # Default to test image image_path = Path(__file__).parent / "test_image.png" - + if len(sys.argv) > 2: prompt = sys.argv[2] else: # Default prompt prompt = "the animals standup and start playing football" - + # Parse optional timeout and progress interval timeout = 600 # 10 minutes default progress_interval = 5 # 5 seconds default - + # Simple argument parsing for timeout and progress interval for i, arg in enumerate(sys.argv[3:], 3): if arg == "--timeout" and i + 1 < len(sys.argv): timeout = int(sys.argv[i + 1]) elif arg == "--progress-interval" and i + 1 < len(sys.argv): progress_interval = int(sys.argv[i + 1]) - + # Validate image path if not Path(image_path).exists(): print(f"āŒ Image not found: {image_path}") sys.exit(1) - + print(f"šŸŽ¬ Generating video from: {image_path}") print(f"šŸ“ Prompt: {prompt}") print("šŸ“ Output: ~/Downloads/polymind_video_generation/") print("-" * 60) - + # Initialize and run video generation video_gen = ReplicateVideoGen() - + # Debug: Check if image exists and get its size image_path_obj = Path(image_path) if image_path_obj.exists(): @@ -91,24 +91,26 @@ def main(): else: print(f"āŒ Image file not found: {image_path}") sys.exit(1) - + # Expand the output folder path output_folder = os.path.expanduser("~/Downloads/polymind_video_generation") - + try: - result = video_gen.run({ - "image": str(image_path), - "prompt": prompt, - "output_folder": output_folder, - "output_format": "mp4", - "timeout": timeout, - "progress_interval": progress_interval - }) - + result = video_gen.run( + { + "image": str(image_path), + "prompt": prompt, + "output_folder": output_folder, + "output_format": "mp4", + "timeout": timeout, + "progress_interval": progress_interval, + } + ) + if result["video_path"]: print("āœ… Video generated successfully!") print(f"šŸ“ Saved to: {result['video_path']}") - + # Show file size if available video_path = Path(result["video_path"]) if video_path.exists(): @@ -117,11 +119,11 @@ def main(): else: print(f"āŒ Generation failed: {result['generation_info']}") sys.exit(1) - + except Exception as e: print(f"āŒ Error: {e}") sys.exit(1) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/media-gen/test_scripts/test_video.mp4 b/media_gen/test_scripts/test_video.mp4 similarity index 100% rename from media-gen/test_scripts/test_video.mp4 rename to media_gen/test_scripts/test_video.mp4 diff --git a/media-gen/test_scripts/test_video_understanding_tool.py b/media_gen/test_scripts/test_video_understanding_tool.py similarity index 72% rename from media-gen/test_scripts/test_video_understanding_tool.py rename to media_gen/test_scripts/test_video_understanding_tool.py index ba35483..7616794 100644 --- a/media-gen/test_scripts/test_video_understanding_tool.py +++ b/media_gen/test_scripts/test_video_understanding_tool.py @@ -17,14 +17,14 @@ import argparse import os import sys +from pathlib import Path from dotenv import load_dotenv -from pathlib import Path # Add the parent directory to the path to import the tool sys.path.insert(0, str(Path(__file__).parent.parent)) -from tools.video_understanding_tool import VideoUnderstandingTool +from media_gen.tools.video_understanding_tool import VideoUnderstandingTool def test_extraction(tool, test_video_path, use_interval=False): @@ -32,91 +32,90 @@ def test_extraction(tool, test_video_path, use_interval=False): if use_interval: print("šŸ“ø Testing Interval-Based Extraction") print("-" * 40) - - result = tool.run({ - "video_path": str(test_video_path), - "user_preference": "cinematic style with dramatic lighting", - "extraction_mode": "interval", - "screenshot_interval": 10.0, - "output_dir": "~/Downloads/video_understanding_interval" - }) - + + result = tool.run( + { + "video_path": str(test_video_path), + "user_preference": "cinematic style with dramatic lighting", + "extraction_mode": "interval", + "screenshot_interval": 10.0, + "output_dir": "~/Downloads/video_understanding_interval", + } + ) + print("āœ… Interval-based analysis completed!") print(f"šŸ“Š Scenes: {result['metadata']['total_scenes']}") print(f"ā±ļø Duration: {result['metadata']['video_duration']}") print(f"šŸ“ø Interval: {result['metadata']['screenshot_interval']}") - + else: print("šŸ” Testing Keyframe-Based Extraction") print("-" * 40) - - result = tool.run({ - "video_path": str(test_video_path), - "user_preference": "cinematic style with dramatic lighting", - "extraction_mode": "keyframe", - "keyframe_threshold": 25.0, - "min_interval_frames": 15, - "output_dir": "~/Downloads/video_understanding_keyframe" - }) - + + result = tool.run( + { + "video_path": str(test_video_path), + "user_preference": "cinematic style with dramatic lighting", + "extraction_mode": "keyframe", + "keyframe_threshold": 25.0, + "min_interval_frames": 15, + "output_dir": "~/Downloads/video_understanding_keyframe", + } + ) + print("āœ… Keyframe-based analysis completed!") print(f"šŸ“Š Scenes: {result['metadata']['total_scenes']}") print(f"ā±ļø Duration: {result['metadata']['video_duration']}") print(f"šŸŽÆ Threshold: {result['metadata']['keyframe_threshold']}") - + return len(result["image_prompts"]) def main(): """Run the integration test.""" # Parse command line arguments - parser = argparse.ArgumentParser( - description="Test VideoUnderstandingTool with different extraction methods" - ) + parser = argparse.ArgumentParser(description="Test VideoUnderstandingTool with different extraction methods") parser.add_argument( - "--interval", - action="store_true", - help="Use interval-based extraction (default: keyframe-based)" + "--interval", action="store_true", help="Use interval-based extraction (default: keyframe-based)" ) args = parser.parse_args() - + extraction_method = "interval" if args.interval else "keyframe" - + print("=== Video Understanding Integration Test ===") print(f"This test will analyze test_video.mp4 using {extraction_method}-based extraction.\n") - + # Load environment variables load_dotenv() - + # Check for OpenAI API key api_key = os.getenv("OPENAI_API_KEY") if not api_key: print("āŒ Error: OPENAI_API_KEY not found in environment.") print("Please set it in your .env file.") return - + # Get test video path test_video_path = Path(__file__).parent / "test_video.mp4" - + if not test_video_path.exists(): print(f"āŒ Error: Test video not found at {test_video_path}") - print("Please place a test video file named 'test_video.mp4' in the " - "integration_tests directory.") + print("Please place a test video file named 'test_video.mp4' in the " "integration_tests directory.") return - + print(f"āœ… Found test video: {test_video_path}") print(f"āœ… API key available: {api_key[:8]}...") print() - + # Initialize tool tool = VideoUnderstandingTool() - + # Test extraction method try: scene_count = test_extraction(tool, test_video_path, args.interval) print(f"\nšŸ“Š Results: {scene_count} scenes detected") print("\nāœ… Integration test completed successfully!") - + except Exception as e: print(f"āŒ Test failed: {e}") return diff --git a/media-gen/test_scripts/test_video_utils.py b/media_gen/test_scripts/test_video_utils.py similarity index 88% rename from media-gen/test_scripts/test_video_utils.py rename to media_gen/test_scripts/test_video_utils.py index 2daa8a8..68100bf 100644 --- a/media-gen/test_scripts/test_video_utils.py +++ b/media_gen/test_scripts/test_video_utils.py @@ -15,80 +15,79 @@ import os import sys - from pathlib import Path # Add parent directory to path for imports -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +sys.path.append(os.path.join(os.path.dirname(__file__), "..")) -from utils.video_utils import VideoScreenshotExtractor, extract_screenshots +from media_gen.utils.video_utils import VideoScreenshotExtractor, extract_screenshots def main(): """Extract screenshots from the test video every 10 seconds.""" print("šŸŽ¬ Video Screenshot Extraction Test") print("=" * 50) - + # Path to test video test_video_path = Path(__file__).parent / "test_video.mp4" - + if not test_video_path.exists(): print(f"āŒ Test video not found at: {test_video_path}") print("Please ensure test_video.mp4 exists in the integration_tests directory") return - + print(f"āœ… Test video found: {test_video_path}") print(f"šŸ“ File size: {test_video_path.stat().st_size:,} bytes") - + # Get video properties with VideoScreenshotExtractor(str(test_video_path)) as extractor: - print(f"šŸŽ„ Video properties:") + print("šŸŽ„ Video properties:") print(f" - FPS: {extractor.fps:.2f}") print(f" - Duration: {extractor.duration:.2f} seconds") print(f" - Frame count: {extractor.frame_count:,}") print() - + # Extract screenshots every 5 seconds, starting from 1 second print("šŸ”„ Extracting screenshots every 5 seconds (starting from 1s)...") print("-" * 50) - + try: screenshots = extract_screenshots( video_path=str(test_video_path), interval_seconds=5.0, start_time=1.0, # Start from 1 second instead of 0 output_dir="~/Downloads/polymind_video_screenshots", - filename_prefix="test_video_5s" + filename_prefix="test_video_5s", ) - + print(f"āœ… Extracted {len(screenshots)} screenshots") for i, ss in enumerate(screenshots): print(f" {i+1}. Frame {ss.frame_number} at {ss.timestamp_str}") print() - + except Exception as e: print(f"āŒ Screenshot extraction failed: {e}") return - + # Summary print("šŸ“Š Summary") print("=" * 50) downloads_dir = Path.home() / "Downloads" / "polymind_video_screenshots" - + if downloads_dir.exists(): screenshot_files = list(downloads_dir.glob("test_video_10s_*.jpg")) print(f"āœ… Screenshots saved to: {downloads_dir}") print(f"šŸ“ Files created: {len(screenshot_files)}") - + if screenshot_files: - print(f"šŸ“„ File pattern: test_video_10s_*.jpg") + print("šŸ“„ File pattern: test_video_10s_*.jpg") print(f"šŸ“ Sample file size: {screenshot_files[0].stat().st_size:,} bytes") else: print("āŒ No screenshots were created") - + print("\nšŸŽÆ Test completed!") print("šŸ’” You can now view the extracted screenshots in your Downloads folder") if __name__ == "__main__": - main() + main() diff --git a/media-gen/tests/test_replicate_image_gen.py b/media_gen/tests/test_replicate_image_gen.py similarity index 72% rename from media-gen/tests/test_replicate_image_gen.py rename to media_gen/tests/test_replicate_image_gen.py index 540e00c..441a606 100644 --- a/media-gen/tests/test_replicate_image_gen.py +++ b/media_gen/tests/test_replicate_image_gen.py @@ -6,10 +6,10 @@ import sys from unittest.mock import Mock, patch -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +sys.path.append(os.path.join(os.path.dirname(__file__), "..")) try: - from tools.replicate_image_gen import ReplicateImageGen + from media_gen.tools.replicate_image_gen import ReplicateImageGen except ImportError as e: if "replicate" in str(e): print("āŒ Replicate package not installed. Please install it with:") @@ -21,126 +21,118 @@ class TestReplicateImageGen: """Test cases for ReplicateImageGen tool.""" - + def test_init(self): """Test tool initialization.""" tool = ReplicateImageGen() assert tool.tool_name == "replicate_image_generator" assert len(tool.descriptions) == 3 assert tool.model == "prunaai/wan-2.2-image" - + def test_init_custom_model(self): """Test tool initialization with custom model.""" tool = ReplicateImageGen("stability-ai/sdxl") assert tool.model == "stability-ai/sdxl" - + def test_input_spec(self): """Test input parameter specification.""" tool = ReplicateImageGen() params = tool.input_spec() - + # Check that all expected parameters are present param_names = [p.name for p in params] expected_params = ["prompt", "aspect_ratio", "output_format", "output_folder"] - + for param in expected_params: assert param in param_names - + def test_output_spec(self): """Test output parameter specification.""" tool = ReplicateImageGen() params = tool.output_spec() - + # Check that all expected parameters are present param_names = [p.name for p in params] expected_params = ["image_path", "generation_info"] - + for param in expected_params: assert param in param_names - - @patch('tools.replicate_image_gen.replicate') + + @patch("media_gen.tools.replicate_image_gen.replicate") def test_run_success(self, mock_replicate): """Test successful image generation.""" # Mock Replicate response mock_output = Mock() mock_output.read.return_value = b"fake_image_data" mock_output.url.return_value = "https://replicate.delivery/.../output.jpeg" - + mock_replicate.run.return_value = mock_output - + # Create tool and test tool = ReplicateImageGen() - result = tool.run({ - "prompt": "Test image", - "output_folder": "/tmp" - }) - + result = tool.run({"prompt": "Test image", "output_folder": "/tmp"}) + # Verify result assert result["image_path"] != "" assert result["generation_info"]["model"] == "prunaai/wan-2.2-image" assert result["generation_info"]["status"] == "generated successfully" - - @patch('tools.replicate_image_gen.replicate') + + @patch("media_gen.tools.replicate_image_gen.replicate") def test_run_failure(self, mock_replicate): """Test image generation failure.""" # Mock Replicate to raise exception mock_replicate.run.side_effect = Exception("API Error") - + # Create tool and test tool = ReplicateImageGen() - result = tool.run({ - "prompt": "Test image" - }) - + result = tool.run({"prompt": "Test image"}) + # Verify error handling assert result["image_path"] == "" assert result["generation_info"]["status"] == "generation failed" assert "API Error" in result["generation_info"]["error"] - + def test_default_parameters(self): """Test default parameter values.""" tool = ReplicateImageGen() - - # Test with minimal input - with patch.object(tool, 'run') as mock_run: - mock_run.return_value = { - "image_path": "/tmp/test.jpg", - "generation_info": {"status": "success"} - } - - tool.run({"prompt": "Test"}) - + + # Test with minimal input - call the actual run method to test defaults + with patch("media_gen.tools.replicate_image_gen.replicate") as mock_replicate: + # Mock Replicate response + mock_output = Mock() + mock_output.read.return_value = b"fake_image_data" + mock_output.url.return_value = "https://replicate.delivery/.../output.jpeg" + mock_replicate.run.return_value = mock_output + + result = tool.run({"prompt": "Test"}) + # Verify default values were used - call_args = mock_run.call_args[0][0] - assert call_args["aspect_ratio"] == "4:3" - assert call_args["output_format"] == "jpeg" - assert "~/Downloads" in call_args["output_folder"] - - @patch('tools.replicate_image_gen.replicate') + call_args = mock_replicate.run.call_args + input_params = call_args[1]["input"] + assert input_params["aspect_ratio"] == "9:16" # Default from the class + assert input_params["quality"] == 80 # Default quality + assert "Downloads" in result["image_path"] # Default output folder + + @patch("media_gen.tools.replicate_image_gen.replicate") def test_custom_parameters(self, mock_replicate): """Test custom parameter values.""" # Mock Replicate response mock_output = Mock() mock_output.read.return_value = b"fake_image_data" mock_output.url.return_value = "https://replicate.delivery/.../output.jpeg" - + mock_replicate.run.return_value = mock_output - + # Create tool and test with custom parameters tool = ReplicateImageGen() - result = tool.run({ - "prompt": "Test image", - "seed": 12345, - "aspect_ratio": "16:9", - "model": "stability-ai/sdxl" - }) - + tool.run({"prompt": "Test image", "seed": 12345, "aspect_ratio": "16:9", "model": "stability-ai/sdxl"}) + # Verify custom values were used call_args = mock_replicate.run.call_args model = call_args[0][0] input_params = call_args[1]["input"] - + assert model == "stability-ai/sdxl" assert input_params["prompt"] == "Test image" assert input_params["aspect_ratio"] == "16:9" - assert input_params["seed"] == 12345 \ No newline at end of file + assert input_params["seed"] == 12345 diff --git a/media_gen/tests/test_replicate_video_gen.py b/media_gen/tests/test_replicate_video_gen.py new file mode 100644 index 0000000..b420a3e --- /dev/null +++ b/media_gen/tests/test_replicate_video_gen.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python3 +""" +Test script for Replicate video generation tool using WAN 2.2 i2v fast model. + +This script demonstrates how to use the ReplicateVideoGen tool to generate +videos from images and text prompts using the WAN 2.2 i2v fast model. + +Usage: + python tests/test_replicate_video_gen.py + +Requirements: +- Replicate API token set in environment variables +- Test image file: tests/test_image.png +""" + +import base64 +import os +import sys +from pathlib import Path + +import pytest + +# Add parent directory to path for imports +sys.path.append(os.path.join(os.path.dirname(__file__), "..")) + +from media_gen.tools.replicate_video_gen import ReplicateVideoGen + + +def test_replicate_video_generation(): + """Test the Replicate video generation functionality.""" + # Check for Replicate API token + if not os.getenv("REPLICATE_API_TOKEN"): + pytest.skip("REPLICATE_API_TOKEN not found in environment variables") + + # Path to test image + test_image_path = Path(__file__).parent / "test_image.png" + + if not test_image_path.exists(): + pytest.skip("Test image not found at: {test_image_path}") + + # Initialize the video generation tool + video_gen = ReplicateVideoGen() + + # Test parameters + test_prompt = ( + "Close-up shot of an elderly sailor wearing a yellow raincoat, " + "seated on the deck of a catamaran, slowly puffing on a pipe. " + "His cat lies quietly beside him with eyes closed, enjoying the " + "calm. The warm glow of the setting sun bathes the scene, with " + "gentle waves lapping against the hull and a few seabirds " + "circling slowly above. The camera slowly pushes in, capturing " + "this peaceful and harmonious moment." + ) + + # Generate video + result = video_gen.run( + { + "image": str(test_image_path), + "prompt": test_prompt, + "output_folder": "~/Downloads/polymind_video_generation", + "output_format": "mp4", + } + ) + + # Assertions + assert result["video_path"], "Video generation should return a path" + assert result["generation_info"], "Video generation should return info" + + # Check if file exists + video_path = Path(result["video_path"]) + assert video_path.exists(), f"Video file should exist at {video_path}" + + +def test_with_data_uri(): + """Test video generation using data URI for image input.""" + # Path to test image + test_image_path = Path(__file__).parent / "test_image.png" + + if not test_image_path.exists(): + pytest.skip("Test image not found for data URI test") + + # Convert image to data URI + with open(test_image_path, "rb") as file: + data = base64.b64encode(file.read()).decode("utf-8") + data_uri = f"data:application/octet-stream;base64,{data}" + + # Initialize the video generation tool + video_gen = ReplicateVideoGen() + + # Test parameters + test_prompt = "A serene landscape with gentle movement and natural lighting" + + result = video_gen.run( + { + "image": data_uri, + "prompt": test_prompt, + "output_folder": "~/Downloads/polymind_video_generation", + "output_format": "mp4", + } + ) + + # Assertions + assert result["video_path"], "Video generation should return a path" + assert result["generation_info"], "Video generation should return info" diff --git a/media-gen/tests/test_video_understanding_tool.py b/media_gen/tests/test_video_understanding_tool.py similarity index 69% rename from media-gen/tests/test_video_understanding_tool.py rename to media_gen/tests/test_video_understanding_tool.py index 3d90e24..8d0ecbc 100644 --- a/media-gen/tests/test_video_understanding_tool.py +++ b/media_gen/tests/test_video_understanding_tool.py @@ -9,15 +9,14 @@ import os import sys import unittest -from unittest.mock import Mock, patch - from pathlib import Path +from unittest.mock import Mock, patch # Add the parent directory to the path sys.path.insert(0, str(Path(__file__).parent.parent)) -from tools.video_understanding_tool import VideoUnderstandingTool -from utils.video_utils import ScreenshotInfo +from media_gen.tools.video_understanding_tool import VideoUnderstandingTool +from media_gen.utils.video_utils import ScreenshotInfo class TestVideoUnderstandingTool(unittest.TestCase): @@ -43,7 +42,7 @@ def test_init_without_api_key(self): def test_input_spec(self): """Test input specification.""" params = self.tool.input_spec() - + # Check required parameters param_names = [p.name for p in params] self.assertIn("video_path", param_names) @@ -51,7 +50,7 @@ def test_input_spec(self): self.assertIn("screenshot_interval", param_names) self.assertIn("output_dir", param_names) self.assertIn("max_tokens", param_names) - + # Check video_path is required video_path_param = next(p for p in params if p.name == "video_path") self.assertTrue(video_path_param.required) @@ -59,7 +58,7 @@ def test_input_spec(self): def test_output_spec(self): """Test output specification.""" params = self.tool.output_spec() - + # Check output parameters param_names = [p.name for p in params] self.assertIn("image_prompts", param_names) @@ -67,60 +66,46 @@ def test_output_spec(self): self.assertIn("screenshot_paths", param_names) self.assertIn("metadata", param_names) - @patch('examples.media-gen.tools.video_understanding_tool.extract_screenshots') + @patch("media_gen.tools.video_understanding_tool.extract_screenshots") def test_extract_screenshots(self, mock_extract): """Test screenshot extraction.""" # Mock screenshot data mock_screenshots = [ - ScreenshotInfo( - frame_number=0, - timestamp=0.0, - timestamp_str="00:00:00", - file_path="/tmp/screenshot_1.jpg" - ), - ScreenshotInfo( - frame_number=60, - timestamp=2.0, - timestamp_str="00:00:02", - file_path="/tmp/screenshot_2.jpg" - ) + ScreenshotInfo(frame_number=0, timestamp=0.0, timestamp_str="00:00:00", file_path="/tmp/screenshot_1.jpg"), + ScreenshotInfo(frame_number=60, timestamp=2.0, timestamp_str="00:00:02", file_path="/tmp/screenshot_2.jpg"), ] mock_extract.return_value = mock_screenshots - + # Test extraction - result = self.tool._extract_screenshots( - "/path/to/video.mp4", 2.0, "/tmp/output" - ) - + result = self.tool._extract_screenshots("/path/to/video.mp4", "interval", 2.0, output_dir="/tmp/output") + self.assertEqual(len(result), 2) mock_extract.assert_called_once_with( video_path="/path/to/video.mp4", interval_seconds=2.0, output_dir="/tmp/output", - filename_prefix="video_scene" + filename_prefix="video_scene", ) - @patch('examples.media-gen.tools.video_understanding_tool.encode_image_to_base64') - @patch('examples.media-gen.tools.video_understanding_tool.OpenAI') + @patch("media_gen.tools.video_understanding_tool.encode_image_to_base64") + @patch("media_gen.tools.video_understanding_tool.OpenAI") def test_analyze_screenshots(self, mock_openai, mock_encode): """Test screenshot analysis.""" # Mock screenshots screenshots = [ - ScreenshotInfo( - frame_number=0, - timestamp=0.0, - timestamp_str="00:00:00", - file_path="/tmp/screenshot_1.jpg" - ) + ScreenshotInfo(frame_number=0, timestamp=0.0, timestamp_str="00:00:00", file_path="/tmp/screenshot_1.jpg") ] - + # Mock base64 encoding mock_encode.return_value = "base64_encoded_image" - - # Mock OpenAI response + + # Mock OpenAI client and response + mock_client = Mock() mock_response = Mock() mock_response.choices = [Mock()] - mock_response.choices[0].message.content = ''' + mock_response.choices[ + 0 + ].message.content = """ { "scenes": [ { @@ -132,46 +117,48 @@ def test_analyze_screenshots(self, mock_openai, mock_encode): } ] } - ''' - mock_openai.return_value.chat.completions.create.return_value = mock_response - + """ + mock_client.chat.completions.create.return_value = mock_response + mock_openai.return_value = mock_client + + # Create tool with mocked client + tool = VideoUnderstandingTool(api_key="test_api_key") + tool.client = mock_client + # Test analysis - result = self.tool._analyze_screenshots(screenshots, "cinematic style") - + result = tool._analyze_screenshots(screenshots, "cinematic style") + self.assertEqual(len(result), 1) self.assertEqual(result[0]["prompt"], "A cat sitting on a windowsill") - @patch('examples.media-gen.tools.video_understanding_tool.extract_screenshots') - @patch('examples.media-gen.tools.video_understanding_tool.encode_image_to_base64') - @patch('examples.media-gen.tools.video_understanding_tool.OpenAI') + @patch("media_gen.tools.video_understanding_tool.extract_screenshots") + @patch("media_gen.tools.video_understanding_tool.encode_image_to_base64") + @patch("media_gen.tools.video_understanding_tool.OpenAI") def test_run_success(self, mock_openai, mock_encode, mock_extract): """Test successful tool execution.""" # Mock file existence - with patch('os.path.exists', return_value=True): + with patch("os.path.exists", return_value=True): # Mock screenshots screenshots = [ ScreenshotInfo( - frame_number=0, - timestamp=0.0, - timestamp_str="00:00:00", - file_path="/tmp/screenshot_1.jpg" + frame_number=0, timestamp=0.0, timestamp_str="00:00:00", file_path="/tmp/screenshot_1.jpg" ), ScreenshotInfo( - frame_number=60, - timestamp=2.0, - timestamp_str="00:00:02", - file_path="/tmp/screenshot_2.jpg" - ) + frame_number=60, timestamp=2.0, timestamp_str="00:00:02", file_path="/tmp/screenshot_2.jpg" + ), ] mock_extract.return_value = screenshots - + # Mock base64 encoding mock_encode.return_value = "base64_encoded_image" - - # Mock OpenAI response + + # Mock OpenAI client and response + mock_client = Mock() mock_response = Mock() mock_response.choices = [Mock()] - mock_response.choices[0].message.content = ''' + mock_response.choices[ + 0 + ].message.content = """ { "scenes": [ { @@ -190,21 +177,23 @@ def test_run_success(self, mock_openai, mock_encode, mock_extract): } ] } - ''' - mock_openai.return_value.chat.completions.create.return_value = mock_response - + """ + mock_client.chat.completions.create.return_value = mock_response + mock_openai.return_value = mock_client + + # Create tool with mocked client + tool = VideoUnderstandingTool(api_key="test_api_key") + tool.client = mock_client + # Test run - result = self.tool.run({ - "video_path": "/path/to/video.mp4", - "user_preference": "cinematic style" - }) - + result = tool.run({"video_path": "/path/to/video.mp4", "user_preference": "cinematic style"}) + # Verify results self.assertIn("image_prompts", result) self.assertIn("scene_descriptions", result) self.assertIn("screenshot_paths", result) self.assertIn("metadata", result) - + self.assertEqual(len(result["image_prompts"]), 2) self.assertEqual(len(result["scene_descriptions"]), 2) self.assertEqual(len(result["screenshot_paths"]), 2) @@ -216,16 +205,16 @@ def test_run_missing_video_path(self): def test_run_video_not_found(self): """Test run with non-existent video file.""" - with patch('os.path.exists', return_value=False): + with patch("os.path.exists", return_value=False): with self.assertRaises(FileNotFoundError): self.tool.run({"video_path": "/nonexistent/video.mp4"}) - @patch('examples.media-gen.tools.video_understanding_tool.extract_screenshots') + @patch("media_gen.tools.video_understanding_tool.extract_screenshots") def test_run_no_screenshots(self, mock_extract): """Test run when no screenshots are extracted.""" mock_extract.return_value = [] - - with patch('os.path.exists', return_value=True): + + with patch("os.path.exists", return_value=True): with self.assertRaises(RuntimeError): self.tool.run({"video_path": "/path/to/video.mp4"}) @@ -240,5 +229,5 @@ def test_tool_description(self): self.assertIn("video", description.lower()) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/media-gen/tools/__init__.py b/media_gen/tools/__init__.py similarity index 91% rename from media-gen/tools/__init__.py rename to media_gen/tools/__init__.py index 15142f1..7f2378a 100644 --- a/media-gen/tools/__init__.py +++ b/media_gen/tools/__init__.py @@ -6,17 +6,17 @@ Polymind agents. """ -from .media_gen_tool_base import ImageGenerationTool, VideoGenerationTool from .image_understanding_tool import ImageUnderstandingTool +from .media_gen_tool_base import ImageGenerationTool, VideoGenerationTool from .replicate_image_gen import ReplicateImageGen from .replicate_video_gen import ReplicateVideoGen from .video_understanding_tool import VideoUnderstandingTool __all__ = [ "ImageGenerationTool", - "VideoGenerationTool", + "VideoGenerationTool", "ImageUnderstandingTool", "ReplicateImageGen", "ReplicateVideoGen", - "VideoUnderstandingTool" -] \ No newline at end of file + "VideoUnderstandingTool", +] diff --git a/media-gen/tools/image_understanding_tool.py b/media_gen/tools/image_understanding_tool.py similarity index 88% rename from media-gen/tools/image_understanding_tool.py rename to media_gen/tools/image_understanding_tool.py index 29a8457..1f65e81 100644 --- a/media-gen/tools/image_understanding_tool.py +++ b/media_gen/tools/image_understanding_tool.py @@ -1,8 +1,8 @@ """ Image understanding tool using OpenAI's GPT-4o-mini API. -This module provides a tool for analyzing images using OpenAI's vision -capabilities. It supports both local image files and image URLs, and can +This module provides a tool for analyzing images using OpenAI's vision +capabilities. It supports both local image files and image URLs, and can return structured JSON responses for easy parsing. """ @@ -11,7 +11,6 @@ from typing import Any, Dict, List, Optional from openai import OpenAI - from polymind.core.tool import BaseTool, Param from polymind.core.utils import encode_image_to_base64, is_valid_image_url @@ -19,7 +18,7 @@ class ImageUnderstandingTool(BaseTool): """ Tool for image understanding using OpenAI's GPT-4o-mini API. - + This tool can analyze images using natural language prompts and return structured responses. It supports both local image files and image URLs. """ @@ -31,7 +30,7 @@ class ImageUnderstandingTool(BaseTool): def __init__(self, api_key: Optional[str] = None, model: str = "gpt-4o-mini", **kwargs): """ Initialize the image understanding tool. - + Args: api_key (Optional[str]): OpenAI API key. If None, will use environment variable. model (str): OpenAI model to use for image understanding @@ -40,23 +39,22 @@ def __init__(self, api_key: Optional[str] = None, model: str = "gpt-4o-mini", ** api_key = api_key or os.getenv("OPENAI_API_KEY") if not api_key: raise ValueError( - "OpenAI API key is required. Set OPENAI_API_KEY environment " - "variable or pass api_key parameter." + "OpenAI API key is required. Set OPENAI_API_KEY environment " "variable or pass api_key parameter." ) - + # Initialize the parent class with the required fields super().__init__( tool_name="image_understanding", descriptions=[ "Analyze images using OpenAI's GPT-4o-mini vision capabilities", "Image understanding and analysis tool", - "Vision-based AI tool for image description and analysis" + "Vision-based AI tool for image description and analysis", ], api_key=api_key, model=model, - **kwargs + **kwargs, ) - + # Initialize the OpenAI client self.client = OpenAI(api_key=self.api_key) @@ -68,30 +66,29 @@ def input_spec(self) -> List[Param]: type="str", required=False, description="Text prompt describing what to analyze in the image", - example="What objects do you see in this image? Describe the scene and any text visible." + example="What objects do you see in this image? Describe the scene and any text visible.", ), Param( name="images", type="List[str]", required=True, description="List of image paths (local files) or URLs to analyze", - example='["path/to/image.jpg", "https://example.com/image.png"]' + example='["path/to/image.jpg", "https://example.com/image.png"]', ), Param( name="return_json", type="bool", required=False, description="Whether to return response as JSON format", - example="true" + example="true", ), Param( name="max_tokens", type="int", required=False, description="Maximum number of tokens in the response", - example="1000" + example="1000", ), - ] def output_spec(self) -> List[Param]: @@ -102,64 +99,56 @@ def output_spec(self) -> List[Param]: type="str", required=True, description="Analysis of the image based on the prompt", - example="The image shows a cat sitting on a windowsill..." + example="The image shows a cat sitting on a windowsill...", ), Param( name="confidence", type="float", required=False, description="Confidence score of the analysis (0.0 to 1.0)", - example="0.95" + example="0.95", ), Param( name="metadata", type="Dict[str, str]", required=False, description="Additional metadata about the analysis", - example='{"model": "gpt-4o-mini", "tokens_used": "150"}' - ) + example='{"model": "gpt-4o-mini", "tokens_used": "150"}', + ), ] def _prepare_image_content(self, image_input: str) -> Dict[str, str]: """ Prepare image content for OpenAI API. - + Args: image_input (str): Image path or URL - + Returns: Dict[str, str]: Image content dictionary for OpenAI API """ if is_valid_image_url(image_input): # It's a URL - return { - "type": "image_url", - "image_url": {"url": image_input} - } + return {"type": "image_url", "image_url": {"url": image_input}} else: # It's a local file path try: base64_image = encode_image_to_base64(image_input) - return { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{base64_image}" - } - } + return {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}} except Exception as e: raise ValueError(f"Failed to process image {image_input}: {e}") def run(self, input: dict) -> dict: """ Analyze images using OpenAI's vision capabilities. - + Args: input (dict): Dictionary containing: - prompt: Text prompt for analysis (optional, default: "What's in this image?") - images: List of image paths or URLs - return_json: Whether to return JSON response (optional, default: False) - max_tokens: Maximum tokens in response (optional, default: 1000) - + Returns: dict: Dictionary containing: - analysis: Text analysis of the image(s) @@ -171,33 +160,33 @@ def run(self, input: dict) -> dict: images = input.get("images", []) return_json = input.get("return_json", False) max_tokens = input.get("max_tokens", 1000) - + if not images: raise ValueError("At least one image must be provided") - + # Prepare content for OpenAI API content = [{"type": "text", "text": prompt}] - + # Add images to content for image in images: image_content = self._prepare_image_content(image) content.append(image_content) - + # If JSON response is requested, modify the prompt if return_json: content[0]["text"] = f"{prompt}\n\nPlease respond with a valid JSON object." - + try: # Call OpenAI API response = self.client.chat.completions.create( model=self.model, messages=[{"role": "user", "content": content}], max_tokens=max_tokens, - response_format={"type": "json_object"} if return_json else None + response_format={"type": "json_object"} if return_json else None, ) - + analysis = response.choices[0].message.content - + # Parse JSON if requested if return_json: try: @@ -213,20 +202,17 @@ def run(self, input: dict) -> dict: except json.JSONDecodeError: # If JSON parsing fails, use the raw response pass - + # Extract metadata metadata = { "model": self.model, "tokens_used": str(response.usage.total_tokens), "prompt_tokens": str(response.usage.prompt_tokens), - "completion_tokens": str(response.usage.completion_tokens) + "completion_tokens": str(response.usage.completion_tokens), } - - return { - "analysis": analysis, - "metadata": metadata - } - + + return {"analysis": analysis, "metadata": metadata} + except Exception as e: raise RuntimeError(f"Failed to analyze images: {e}") @@ -238,4 +224,4 @@ def tool_name(self) -> str: @property def tool_description(self) -> str: """Return the description of the tool.""" - return "Analyze images using OpenAI's GPT-4o-mini vision capabilities with custom prompts" \ No newline at end of file + return "Analyze images using OpenAI's GPT-4o-mini vision capabilities with custom prompts" diff --git a/media-gen/tools/media_gen_tool_base.py b/media_gen/tools/media_gen_tool_base.py similarity index 92% rename from media-gen/tools/media_gen_tool_base.py rename to media_gen/tools/media_gen_tool_base.py index 263fce2..3377b54 100644 --- a/media-gen/tools/media_gen_tool_base.py +++ b/media_gen/tools/media_gen_tool_base.py @@ -8,7 +8,7 @@ """ from abc import ABC, abstractmethod -from typing import Dict, List +from typing import List from polymind.core.tool import BaseTool, Param @@ -16,12 +16,12 @@ class ImageGenerationTool(BaseTool, ABC): """ Abstract base class for image generation tools within the Polymind framework. - + This class defines the interface that all image generation tools must implement. It inherits from BaseTool to integrate seamlessly with Polymind's agent system, allowing AI agents to generate images as part of their workflow. - + Implementations should provide concrete image generation capabilities using various APIs or models (e.g., DALL-E, Stable Diffusion, Midjourney). @@ -35,29 +35,29 @@ def input_spec(self) -> List[Param]: type="str", required=True, description="Text prompt describing the image to generate", - example="A beautiful sunset over a mountain landscape" + example="A beautiful sunset over a mountain landscape", ), Param( name="aspect_ratio", type="str", required=False, description="Aspect ratio of the generated image (e.g., '4:3', '16:9')", - example="4:3" + example="4:3", ), Param( name="output_format", type="str", required=False, description="Output format for the generated image", - example="jpg" + example="jpg", ), Param( name="output_folder", type="str", required=False, description="Folder path where to save the generated image", - example="/path/to/save/images/" - ) + example="/path/to/save/images/", + ), ] def output_spec(self) -> List[Param]: @@ -68,29 +68,29 @@ def output_spec(self) -> List[Param]: type="str", required=True, description="Path to the generated image file", - example="/path/to/generated_image.jpg" + example="/path/to/generated_image.jpg", ), Param( name="generation_info", type="Dict[str, str]", required=False, description="Additional information about the generation process", - example='{"model": "stable-diffusion", "seed": "42"}' - ) + example='{"model": "stable-diffusion", "seed": "42"}', + ), ] @abstractmethod def run(self, input: dict) -> dict: """ Generate an image based on the provided input parameters. - + Args: input (dict): Dictionary containing generation parameters such as: - prompt: Text description of the desired image - aspect_ratio: Image aspect ratio (optional, default: "4:3") - output_format: Output format (optional, default: "jpg") - output_folder: Folder path to save the image (optional) - + Returns: dict: Dictionary containing: - image_path: Path to the generated image file @@ -102,12 +102,12 @@ def run(self, input: dict) -> dict: class VideoGenerationTool(BaseTool, ABC): """ Abstract base class for video generation tools within the Polymind framework. - + This class defines the interface that all video generation tools must implement. It inherits from BaseTool to integrate seamlessly with Polymind's agent system, allowing AI agents to generate videos as part of their workflow. - + Implementations should provide concrete video generation capabilities using various APIs or models (e.g., Runway, Pika Labs, or other video generation services). @@ -118,39 +118,39 @@ def input_spec(self) -> List[Param]: return [ Param( name="prompt", - type="str", + type="str", required=True, description="Text prompt describing the video to generate", - example="A timelapse of a flower blooming in spring" + example="A timelapse of a flower blooming in spring", ), Param( name="num_frames", type="int", required=False, description="Number of frames for the video", - example="81" + example="81", ), Param( name="resolution", type="str", required=False, description="Video resolution (e.g., '480p', '720p', '1080p')", - example="480p" + example="480p", ), Param( name="image", type="str", required=False, description="URI of an image to use as starting point", - example="https://example.com/starting_image.jpg" + example="https://example.com/starting_image.jpg", ), Param( name="aspect_ratio", type="str", required=False, description="Aspect ratio of the generated video (e.g., '9:16', '16:9')", - example="9:16" - ) + example="9:16", + ), ] def output_spec(self) -> List[Param]: @@ -161,22 +161,22 @@ def output_spec(self) -> List[Param]: type="str", required=True, description="Path to the generated video file", - example="/path/to/generated_video.mp4" + example="/path/to/generated_video.mp4", ), Param( name="generation_info", type="Dict[str, str]", required=False, description="Additional information about the generation process", - example='{"model": "runway-gen2", "frames": "81"}' - ) + example='{"model": "runway-gen2", "frames": "81"}', + ), ] @abstractmethod def run(self, input: dict) -> dict: """ Generate a video based on the provided input parameters. - + Args: input (dict): Dictionary containing generation parameters such as: - prompt: Text description of the desired video @@ -184,10 +184,10 @@ def run(self, input: dict) -> dict: - resolution: Video resolution (optional, default: "480p") - image: URI of starting image (optional) - aspect_ratio: Video aspect ratio (optional, default: "9:16") - + Returns: dict: Dictionary containing: - video_path: Path to the generated video file - generation_info: Additional metadata (optional) """ - pass \ No newline at end of file + pass diff --git a/media-gen/tools/replicate_image_gen.py b/media_gen/tools/replicate_image_gen.py similarity index 73% rename from media-gen/tools/replicate_image_gen.py rename to media_gen/tools/replicate_image_gen.py index 9ccb751..8d8fd08 100644 --- a/media-gen/tools/replicate_image_gen.py +++ b/media_gen/tools/replicate_image_gen.py @@ -6,9 +6,9 @@ with the Polymind framework and supports various image generation parameters. """ -import replicate from pathlib import Path +import replicate from polymind.core.message import Message from .media_gen_tool_base import ImageGenerationTool @@ -17,18 +17,18 @@ class ReplicateImageGen(ImageGenerationTool): """ Replicate image generation tool using various models. - + This tool uses Replicate's API to generate images using various models like WAN, Stable Diffusion, and others. It supports various parameters including seed, prompt, aspect ratio, and model-specific options. - + Requires Replicate API token to be set in environment variables. """ def __init__(self, model: str = "prunaai/wan-2.2-image", **kwargs): """ Initialize the Replicate image generation tool. - + Args: model (str): Replicate model identifier (default: "prunaai/wan-2.2-image") **kwargs: Additional arguments passed to parent class @@ -38,16 +38,21 @@ def __init__(self, model: str = "prunaai/wan-2.2-image", **kwargs): descriptions=[ f"Replicate image generation using {model}", "Generate high-quality images from text prompts", - "Supports various models and parameters" + "Supports various models and parameters", ], - **kwargs + **kwargs, ) self._model = model + @property + def model(self) -> str: + """Get the current model being used.""" + return self._model + def run(self, input: dict) -> dict: """ Generate images using Replicate API. - + Args: input (dict): Input parameters containing: - prompt: Text description(s) of the desired image(s) - can be string or list @@ -57,7 +62,7 @@ def run(self, input: dict) -> dict: - output_format: Output format (optional, default: "jpeg") - quality: Image quality (optional, default: 80) - model: Replicate model to use (optional, overrides default) - + Returns: dict: Dictionary containing: - generated_image_paths: List of paths to generated image files @@ -71,7 +76,7 @@ def run(self, input: dict) -> dict: output_format = input.get("output_format", "jpeg") quality = input.get("quality", 80) model = input.get("model", self._model) - + # Handle both single prompt and list of prompts if isinstance(prompt, str): prompts = [prompt] @@ -79,20 +84,21 @@ def run(self, input: dict) -> dict: prompts = prompt else: raise ValueError("Prompt must be a string or list of strings") - + generated_images = [] generation_info = [] - + # Process each prompt for i, single_prompt in enumerate(prompts): try: # Generate dynamic image name with timestamp to avoid duplication import os from datetime import datetime + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") base_name = f"replicate_generated_image_{timestamp}_{i+1}" image_name = f"{base_name}.{output_format}" - + # Ensure unique filename counter = 1 full_path = f"{output_folder.rstrip('/')}/{image_name}" @@ -100,45 +106,43 @@ def run(self, input: dict) -> dict: image_name = f"{base_name}_{counter}.{output_format}" full_path = f"{output_folder.rstrip('/')}/{image_name}" counter += 1 - + # Create full path image_path = f"{output_folder.rstrip('/')}/{image_name}" - + # Prepare input for Replicate - replicate_input = { - "prompt": single_prompt, - "aspect_ratio": aspect_ratio, - "quality": quality - } - + replicate_input = {"prompt": single_prompt, "aspect_ratio": aspect_ratio, "quality": quality} + # Add seed if provided if seed is not None: replicate_input["seed"] = seed - + # Ensure directory exists output_path = Path(image_path) output_path.parent.mkdir(parents=True, exist_ok=True) - + # Run the model output = replicate.run(model, input=replicate_input) - + # Handle different output types from Replicate - if hasattr(output, 'read'): + if hasattr(output, "read"): # Output is a FileOutput object with open(image_path, "wb") as file: file.write(output.read()) - + generated_images.append(image_path) - generation_info.append({ - "model": model, - "prompt": single_prompt, - "seed": seed, - "aspect_ratio": aspect_ratio, - "format": output_format, - "status": "generated successfully", - "replicate_url": None - }) - + generation_info.append( + { + "model": model, + "prompt": single_prompt, + "seed": seed, + "aspect_ratio": aspect_ratio, + "format": output_format, + "status": "generated successfully", + "replicate_url": None, + } + ) + elif isinstance(output, list) and len(output) > 0: # Output is a list of URLs image_url = output[0] @@ -147,55 +151,63 @@ def run(self, input: dict) -> dict: # Download the image response = requests.get(image_url) response.raise_for_status() - + # Save the image with open(image_path, "wb") as file: file.write(response.content) - + generated_images.append(image_path) - generation_info.append({ - "model": model, - "prompt": single_prompt, - "seed": seed, - "aspect_ratio": aspect_ratio, - "format": output_format, - "status": "generated successfully", - "replicate_url": image_url - }) - + generation_info.append( + { + "model": model, + "prompt": single_prompt, + "seed": seed, + "aspect_ratio": aspect_ratio, + "format": output_format, + "status": "generated successfully", + "replicate_url": image_url, + } + ) + else: raise ValueError(f"Unexpected output format from Replicate: {type(output)}") - + except Exception as e: # Add empty path and error info for failed generation generated_images.append("") - generation_info.append({ - "model": model, - "prompt": single_prompt, - "error": str(e), - "status": "generation failed" - }) - - return { - "generated_image_paths": generated_images, - "image_generation_info": generation_info - } + generation_info.append( + {"model": model, "prompt": single_prompt, "error": str(e), "status": "generation failed"} + ) + + # Return format matching the base class interface + if len(generated_images) == 1: + return {"image_path": generated_images[0], "generation_info": generation_info[0] if generation_info else {}} + else: + # For multiple images, return the first one as primary and include all info + return { + "image_path": generated_images[0] if generated_images else "", + "generation_info": { + "all_images": generated_images, + "all_info": generation_info, + "count": len(generated_images), + }, + } async def _execute(self, input: Message) -> Message: """ Execute the Replicate image generation using the Polymind framework's Message system. - + Args: input (Message): Input message containing generation parameters - + Returns: Message: Output message with generated image information """ # Convert Message to dict for the run method input_dict = input.content - + # Call the run method result = self.run(input_dict) - + # Return result wrapped in a Message - return Message(content=result) \ No newline at end of file + return Message(content=result) diff --git a/media-gen/tools/replicate_video_gen.py b/media_gen/tools/replicate_video_gen.py similarity index 68% rename from media-gen/tools/replicate_video_gen.py rename to media_gen/tools/replicate_video_gen.py index 9fb747a..75a92a8 100644 --- a/media-gen/tools/replicate_video_gen.py +++ b/media_gen/tools/replicate_video_gen.py @@ -9,12 +9,11 @@ import base64 import os +from pathlib import Path from typing import Union import replicate import requests -from pathlib import Path - from polymind.core.message import Message from .media_gen_tool_base import VideoGenerationTool @@ -23,20 +22,20 @@ class ReplicateVideoGen(VideoGenerationTool): """ Replicate video generation tool using WAN 2.2 i2v fast model. - + This tool uses Replicate's API to generate videos from images and text prompts using the WAN 2.2 i2v fast model. It supports various parameters including image input (file path, URL, or data URI) and text prompts. - + Requires Replicate API token to be set in environment variables. """ def __init__(self, model: str = "wan-video/wan-2.2-i2v-fast", **kwargs): """ Initialize the Replicate video generation tool. - + Args: - model (str): Replicate model identifier + model (str): Replicate model identifier (default: "wan-video/wan-2.2-i2v-fast") **kwargs: Additional arguments passed to parent class """ @@ -45,54 +44,53 @@ def __init__(self, model: str = "wan-video/wan-2.2-i2v-fast", **kwargs): descriptions=[ f"Replicate video generation using {model}", "Generate videos from images and text prompts", - "Supports image-to-video generation with WAN 2.2 i2v fast " - "model" + "Supports image-to-video generation with WAN 2.2 i2v fast " "model", ], - **kwargs + **kwargs, ) self._model = model def _prepare_image_input(self, image_input: Union[str, Path]) -> str: """ Prepare image input for Replicate API. - + Args: image_input: Image path, URL, or data URI - + Returns: str: Prepared image input for Replicate API """ image_str = str(image_input) - + # If it's already a data URI or URL, return as is - if image_str.startswith(('data:', 'http://', 'https://')): + if image_str.startswith(("data:", "http://", "https://")): return image_str - + # If it's a file path, convert to data URI for Replicate API image_path = Path(image_str) if not image_path.exists(): raise FileNotFoundError(f"Image file not found: {image_path}") - + # Always convert to data URI for Replicate API - with open(image_path, 'rb') as file: - data = base64.b64encode(file.read()).decode('utf-8') + with open(image_path, "rb") as file: + data = base64.b64encode(file.read()).decode("utf-8") return f"data:application/octet-stream;base64,{data}" def run(self, input: dict) -> dict: """ Generate videos using Replicate WAN 2.2 i2v fast API with progress monitoring. - + Args: input (dict): Input parameters containing: - image: Image path(s), URL(s), or data URI(s) - can be string or list (required) - prompt: Text description(s) of the desired video(s) - can be string or list (required) - - output_folder: Folder path where to save the video(s) + - output_folder: Folder path where to save the video(s) (optional, default: "~/Downloads") - output_format: Output format (optional, default: "mp4") - model: Replicate model to use (optional, overrides default) - timeout: Timeout in seconds (optional, default: 300) - progress_interval: Progress update interval in seconds (optional, default: 5) - + Returns: dict: Dictionary containing: - generated_video_paths: List of paths to generated video files @@ -101,15 +99,13 @@ def run(self, input: dict) -> dict: # Extract parameters with defaults image_input = input.get("image", "") prompt = input.get("prompt", "") - output_folder = input.get( - "output_folder", str(Path.home() / "Downloads") - ) + output_folder = input.get("output_folder", str(Path.home() / "Downloads")) output_format = input.get("output_format", "mp4") aspect_ratio = input.get("aspect_ratio", "9:16") model = input.get("model", self._model) timeout = input.get("timeout", 300) # 5 minutes default progress_interval = input.get("progress_interval", 5) # 5 seconds default - + # Handle both single and multiple inputs if isinstance(image_input, str): images = [image_input] @@ -117,39 +113,37 @@ def run(self, input: dict) -> dict: images = image_input else: raise ValueError("Image input must be a string or list of strings") - + if isinstance(prompt, str): prompts = [prompt] elif isinstance(prompt, list): prompts = prompt else: raise ValueError("Prompt must be a string or list of strings") - + # Ensure we have matching numbers of images and prompts if len(images) != len(prompts): - raise ValueError( - f"Number of images ({len(images)}) must match number of " - f"prompts ({len(prompts)})" - ) - + raise ValueError(f"Number of images ({len(images)}) must match number of " f"prompts ({len(prompts)})") + generated_videos = [] generation_info = [] - + # Process each image-prompt pair for i, (single_image, single_prompt) in enumerate(zip(images, prompts)): try: if not single_image: raise ValueError("Image input is required") - + if not single_prompt: raise ValueError("Text prompt is required") - + # Generate dynamic video name with timestamp to avoid duplication import datetime + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") base_name = f"replicate_generated_video_{timestamp}_{i+1}" video_name = f"{base_name}.{output_format}" - + # Ensure unique filename counter = 1 full_path = f"{output_folder.rstrip('/')}/{video_name}" @@ -157,179 +151,169 @@ def run(self, input: dict) -> dict: video_name = f"{base_name}_{counter}.{output_format}" full_path = f"{output_folder.rstrip('/')}/{video_name}" counter += 1 - + # Create full path video_path = f"{output_folder.rstrip('/')}/{video_name}" - + # Prepare image input prepared_image = self._prepare_image_input(single_image) - + # Prepare input for Replicate - replicate_input = { - "image": prepared_image, - "prompt": single_prompt, - "aspect_ratio": aspect_ratio - } - + replicate_input = {"image": prepared_image, "prompt": single_prompt, "aspect_ratio": aspect_ratio} + # Ensure directory exists output_path = Path(video_path) output_path.parent.mkdir(parents=True, exist_ok=True) - + # Create prediction in background import time + start_time = time.time() - + # Create prediction using the model string directly - prediction = replicate.predictions.create( - model=model, - input=replicate_input - ) - + prediction = replicate.predictions.create(model=model, input=replicate_input) + print(f"šŸ”„ Started video generation {i+1} (ID: {prediction.id})") - + # Monitor progress with timeout last_progress_time = start_time while True: # Check timeout if time.time() - start_time > timeout: prediction.cancel() - raise TimeoutError( - f"Video generation {i+1} timed out after {timeout} " - "seconds" - ) - + raise TimeoutError(f"Video generation {i+1} timed out after {timeout} " "seconds") + # Reload prediction to get latest status prediction.reload() - + # Print progress updates if time.time() - last_progress_time >= progress_interval: elapsed = int(time.time() - start_time) - print(f"ā±ļø Video {i+1} Status: {prediction.status} " - f"(elapsed: {elapsed}s)") + print(f"ā±ļø Video {i+1} Status: {prediction.status} " f"(elapsed: {elapsed}s)") if prediction.logs: print(f"šŸ“ Logs: {prediction.logs[-200:]}...") last_progress_time = time.time() - + # Check if completed if prediction.status == "succeeded": print(f"āœ… Video generation {i+1} completed!") break elif prediction.status == "failed": - raise Exception( - f"Video generation {i+1} failed: {prediction.error}" - ) + raise Exception(f"Video generation {i+1} failed: {prediction.error}") elif prediction.status == "canceled": raise Exception(f"Video generation {i+1} was canceled") - + # Wait before next check time.sleep(2) - + # Download the result - if hasattr(prediction.output, 'read'): + if hasattr(prediction.output, "read"): # Output is a FileOutput object with open(video_path, "wb") as file: file.write(prediction.output.read()) - + generated_videos.append(video_path) - generation_info.append({ - "model": model, - "prompt": single_prompt, - "image_input": str(single_image), - "aspect_ratio": aspect_ratio, - "format": output_format, - "status": "generated successfully", - "prediction_id": prediction.id, - "replicate_url": None, - "elapsed_time": int(time.time() - start_time) - }) - + generation_info.append( + { + "model": model, + "prompt": single_prompt, + "image_input": str(single_image), + "aspect_ratio": aspect_ratio, + "format": output_format, + "status": "generated successfully", + "prediction_id": prediction.id, + "replicate_url": None, + "elapsed_time": int(time.time() - start_time), + } + ) + elif isinstance(prediction.output, list) and len(prediction.output) > 0: # Output is a list of URLs video_url = prediction.output[0] - + # Download the video response = requests.get(video_url) response.raise_for_status() - + # Save the video with open(video_path, "wb") as file: file.write(response.content) - + generated_videos.append(video_path) - generation_info.append({ - "model": model, - "prompt": single_prompt, - "image_input": str(single_image), - "aspect_ratio": aspect_ratio, - "format": output_format, - "status": "generated successfully", - "prediction_id": prediction.id, - "replicate_url": video_url, - "elapsed_time": int(time.time() - start_time) - }) - + generation_info.append( + { + "model": model, + "prompt": single_prompt, + "image_input": str(single_image), + "aspect_ratio": aspect_ratio, + "format": output_format, + "status": "generated successfully", + "prediction_id": prediction.id, + "replicate_url": video_url, + "elapsed_time": int(time.time() - start_time), + } + ) + elif isinstance(prediction.output, str): # Output is a direct URL string video_url = prediction.output - + # Download the video response = requests.get(video_url) response.raise_for_status() - + # Save the video with open(video_path, "wb") as file: file.write(response.content) - + generated_videos.append(video_path) - generation_info.append({ - "model": model, - "prompt": single_prompt, - "image_input": str(single_image), - "aspect_ratio": aspect_ratio, - "format": output_format, - "status": "generated successfully", - "prediction_id": prediction.id, - "replicate_url": video_url, - "elapsed_time": int(time.time() - start_time) - }) - - else: - raise ValueError( - f"Unexpected output format from Replicate: " - f"{type(prediction.output)}" + generation_info.append( + { + "model": model, + "prompt": single_prompt, + "image_input": str(single_image), + "aspect_ratio": aspect_ratio, + "format": output_format, + "status": "generated successfully", + "prediction_id": prediction.id, + "replicate_url": video_url, + "elapsed_time": int(time.time() - start_time), + } ) - + + else: + raise ValueError(f"Unexpected output format from Replicate: " f"{type(prediction.output)}") + except Exception as e: # Add empty path and error info for failed generation generated_videos.append("") - generation_info.append({ - "model": model, - "prompt": single_prompt if 'single_prompt' in locals() else "", - "image_input": str(single_image) if 'single_image' in locals() else "", - "error": str(e), - "status": "generation failed" - }) - - return { - "generated_video_paths": generated_videos, - "video_generation_info": generation_info - } + generation_info.append( + { + "model": model, + "prompt": single_prompt if "single_prompt" in locals() else "", + "image_input": str(single_image) if "single_image" in locals() else "", + "error": str(e), + "status": "generation failed", + } + ) + + return {"generated_video_paths": generated_videos, "video_generation_info": generation_info} async def _execute(self, input: Message) -> Message: """ Execute the Replicate video generation using the Polymind framework's Message system. - + Args: input (Message): Input message containing generation parameters - + Returns: Message: Output message with generated video information """ # Convert Message to dict for the run method input_dict = input.content - + # Call the run method result = self.run(input_dict) - + # Return result wrapped in a Message - return Message(content=result) \ No newline at end of file + return Message(content=result) diff --git a/media-gen/tools/video_understanding_tool.py b/media_gen/tools/video_understanding_tool.py similarity index 78% rename from media-gen/tools/video_understanding_tool.py rename to media_gen/tools/video_understanding_tool.py index a567a03..03fa67a 100644 --- a/media-gen/tools/video_understanding_tool.py +++ b/media_gen/tools/video_understanding_tool.py @@ -12,19 +12,19 @@ from typing import Any, ClassVar, Dict, List, Optional from openai import OpenAI -from utils.video_utils import ScreenshotInfo, extract_key_frames, extract_screenshots - from polymind.core.tool import BaseTool, Param from polymind.core.utils import encode_image_to_base64 +from ..utils.video_utils import ScreenshotInfo, extract_key_frames, extract_screenshots + class VideoUnderstandingTool(BaseTool): """ Tool for video understanding using OpenAI's GPT-4o-mini API. - + This tool extracts screenshots from videos using either interval-based - or keyframe-based extraction and generates coherent image generation - prompts for each scene. The prompts are designed to work together to + or keyframe-based extraction and generates coherent image generation + prompts for each scene. The prompts are designed to work together to create a logical sequence of images that can be used for video generation. """ @@ -32,30 +32,33 @@ class VideoUnderstandingTool(BaseTool): model: str = "gpt-4o-mini" client: Optional[Any] = None - prompt: ClassVar[str] = """ - Analyze these video screenshots and generate image generation prompts + prompt: ClassVar[ + str + ] = """ + Analyze these video screenshots and generate image generation prompts for each scene. - + User preference: {user_preference} - + Requirements: - 1. Each prompt should be detailed and specific enough for image + 1. Each prompt should be detailed and specific enough for image generation 2. Prompts should maintain logical coherence between scenes 3. Consider the visual flow and narrative progression - 4. Include relevant details like lighting, composition, mood, and + 4. Include relevant details like lighting, composition, mood, and style 5. Ensure prompts work together to tell a coherent visual story 6. Ignore the text in the screenshot, if they are about the brand. Only keep non-branding words. - + For each screenshot, provide: - - A detailed image generation prompt, including the scene, object, lighting, camera (e.g. overhead, close-up, etc.) + - A detailed image generation prompt, including the scene, object, lighting, + camera (e.g. overhead, close-up, etc.) and image aesthetic style (cartoon, realistic, cyberpunk, etc.) - - A detailed image to video generation prompt, including the potential action of each objects, + - A detailed image to video generation prompt, including the potential action of each objects, and the camera movement (e.g. pan, zoom, etc.) Both should be <100 words. - + Respond in JSON format with the following structure: {{ "scenes": [ @@ -67,17 +70,12 @@ class VideoUnderstandingTool(BaseTool): }} """ - def __init__( - self, - api_key: Optional[str] = None, - model: str = "gpt-4o-mini", - **kwargs - ): + def __init__(self, api_key: Optional[str] = None, model: str = "gpt-4o-mini", **kwargs): """ Initialize the video understanding tool. - + Args: - api_key (Optional[str]): OpenAI API key. If None, will use + api_key (Optional[str]): OpenAI API key. If None, will use environment variable. model (str): OpenAI model to use for video understanding """ @@ -85,140 +83,116 @@ def __init__( api_key = api_key or os.getenv("OPENAI_API_KEY") if not api_key: raise ValueError( - "OpenAI API key is required. Set OPENAI_API_KEY environment " - "variable or pass api_key parameter." + "OpenAI API key is required. Set OPENAI_API_KEY environment " "variable or pass api_key parameter." ) - + # Initialize the parent class with the required fields super().__init__( tool_name="video_understanding", descriptions=[ - "Analyze videos by extracting screenshots and generating " - "image prompts", + "Analyze videos by extracting screenshots and generating " "image prompts", "Video understanding and scene analysis tool", - "AI tool for generating coherent image prompts from video " - "scenes" - ] + "AI tool for generating coherent image prompts from video " "scenes", + ], ) - + self.api_key = api_key self.model = model - + # Initialize OpenAI client self.client = OpenAI(api_key=api_key) def input_spec(self) -> List[Param]: """ Define the input specification for the tool. - + Returns: List[Param]: List of input parameters """ return [ - Param( - name="video_path", - type="string", - description="Path to the video file to analyze", - required=True - ), + Param(name="video_path", type="str", description="Path to the video file to analyze", required=True), Param( name="user_preference", - type="string", + type="str", description="User's preference for image generation style and content", required=False, - default="Create detailed, cinematic image generation prompts" + default="Create detailed, cinematic image generation prompts", ), Param( name="extraction_mode", - type="string", + type="str", description="Extraction mode: 'interval' for regular intervals or 'keyframe' for scene changes", required=False, - default="interval" + default="interval", ), Param( name="screenshot_interval", - type="number", + type="float", description="Time interval between screenshots (for interval mode)", required=False, - default=10.0 + default=10.0, ), Param( name="keyframe_threshold", - type="number", + type="float", description="Threshold for keyframe detection (for keyframe mode)", required=False, - default=30.0 + default=30.0, ), Param( name="min_interval_frames", - type="number", + type="int", description="Minimum frames between keyframes (for keyframe mode)", required=False, - default=30 + default=30, ), Param( name="output_dir", - type="string", + type="str", description="Directory to save extracted screenshots", required=False, - default="~/Downloads/video_understanding" + default="~/Downloads/video_understanding", ), Param( name="max_tokens", - type="number", + type="int", description="Maximum tokens in OpenAI response", required=False, - default=2000 - ) + default=2000, + ), ] def output_spec(self) -> List[Param]: """ Define the output specification for the tool. - + Returns: List[Param]: List of output parameters """ return [ Param( - name="image_prompts", - type="array", - description="List of image generation prompts for each scene" + name="image_prompts", type="List[str]", description="List of image generation prompts for each scene" ), Param( - name="video_prompts", - type="array", - description="List of video generation prompts for each scene" + name="video_prompts", type="List[str]", description="List of video generation prompts for each scene" ), - Param( - name="scene_descriptions", - type="array", - description="List of scene descriptions" - ), - Param( - name="screenshot_paths", - type="array", - description="List of paths to extracted screenshots" - ), - Param( - name="metadata", - type="object", - description="Additional metadata about the analysis" - ) + Param(name="scene_descriptions", type="List[str]", description="List of scene descriptions"), + Param(name="screenshot_paths", type="List[str]", description="List of paths to extracted screenshots"), + Param(name="metadata", type="Dict[str, str]", description="Additional metadata about the analysis"), ] def _extract_screenshots( - self, - video_path: str, + self, + video_path: str, extraction_mode: str = "interval", interval: float = 10.0, keyframe_threshold: float = 30.0, min_interval_frames: int = 30, - output_dir: str = "~/Downloads/video_understanding" + output_dir: str = "~/Downloads/video_understanding", ) -> List[ScreenshotInfo]: """ Extract screenshots from the video using video_utils. - + Args: video_path (str): Path to the video file extraction_mode (str): 'interval' or 'keyframe' @@ -226,7 +200,7 @@ def _extract_screenshots( keyframe_threshold (float): Threshold for keyframe detection (for keyframe mode) min_interval_frames (int): Minimum frames between keyframes (for keyframe mode) output_dir (str): Directory to save screenshots - + Returns: List[ScreenshotInfo]: List of screenshot information """ @@ -238,9 +212,9 @@ def _extract_screenshots( threshold=keyframe_threshold, min_interval_frames=min_interval_frames, output_dir=output_dir, - filename_prefix="video_scene" + filename_prefix="video_scene", ) - + # Convert KeyFrameInfo to ScreenshotInfo for compatibility screenshots = [] for key_frame in key_frames: @@ -248,10 +222,10 @@ def _extract_screenshots( frame_number=key_frame.frame_number, timestamp=key_frame.timestamp, timestamp_str=key_frame.timestamp_str, - file_path=key_frame.file_path + file_path=key_frame.file_path, ) screenshots.append(screenshot) - + print(f"šŸ” Extracted {len(screenshots)} key frames using scene change detection") return screenshots else: @@ -260,74 +234,69 @@ def _extract_screenshots( video_path=video_path, interval_seconds=interval, output_dir=output_dir, - filename_prefix="video_scene" + filename_prefix="video_scene", ) - + print(f"šŸ“ø Extracted {len(screenshots)} screenshots at {interval}s intervals") return screenshots - + except Exception as e: raise RuntimeError(f"Failed to extract screenshots from video: {e}") def _analyze_screenshots(self, screenshots: List[ScreenshotInfo], user_preference: str) -> List[Dict[str, Any]]: """ Analyze screenshots and generate image prompts using OpenAI. - + Args: screenshots (List[ScreenshotInfo]): List of screenshot information user_preference (str): User's preference for image generation - + Returns: List[Dict[str, Any]]: List of analysis results for each screenshot """ if not screenshots: return [] - + # Prepare the analysis prompt base_prompt = self.prompt.format(user_preference=user_preference) - + # Prepare content for OpenAI API content = [{"type": "text", "text": base_prompt}] - + # Add screenshots to content for screenshot in screenshots: try: base64_image = encode_image_to_base64(screenshot.file_path) - content.append({ - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{base64_image}" - } - }) + content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}) except Exception as e: print(f"Warning: Failed to process screenshot {screenshot.file_path}: {e}") continue - + try: # Call OpenAI API response = self.client.chat.completions.create( model=self.model, messages=[{"role": "user", "content": content}], max_tokens=2000, - response_format={"type": "json_object"} + response_format={"type": "json_object"}, ) - + analysis = response.choices[0].message.content - + # Parse JSON response try: analysis_dict = json.loads(analysis) return analysis_dict.get("scenes", []) except json.JSONDecodeError: raise RuntimeError("Failed to parse OpenAI response as JSON") - + except Exception as e: raise RuntimeError(f"Failed to analyze screenshots: {e}") def run(self, input: dict) -> dict: """ Analyze video and generate coherent image generation prompts. - + Args: input (dict): Dictionary containing: - video_path: Path to the video file @@ -338,7 +307,7 @@ def run(self, input: dict) -> dict: - min_interval_frames: Minimum frames between keyframes (for keyframe mode, optional, default: 30) - output_dir: Directory to save screenshots (optional, default: ~/Downloads/video_understanding) - max_tokens: Maximum tokens in response (optional, default: 2000) - + Returns: dict: Dictionary containing: - image_prompts: List of image generation prompts @@ -355,14 +324,13 @@ def run(self, input: dict) -> dict: keyframe_threshold = input.get("keyframe_threshold", 30.0) min_interval_frames = input.get("min_interval_frames", 30) output_dir = input.get("output_dir", "~/Downloads/video_understanding") - max_tokens = input.get("max_tokens", 2000) - + if not video_path: raise ValueError("video_path is required") - + if not os.path.exists(video_path): raise FileNotFoundError(f"Video file not found: {video_path}") - + # Extract screenshots from video screenshots = self._extract_screenshots( video_path=video_path, @@ -370,63 +338,56 @@ def run(self, input: dict) -> dict: interval=screenshot_interval, keyframe_threshold=keyframe_threshold, min_interval_frames=min_interval_frames, - output_dir=output_dir + output_dir=output_dir, ) - + if not screenshots: raise RuntimeError("No screenshots were extracted from the video") - + # Analyze screenshots and generate prompts scene_analyses = self._analyze_screenshots(screenshots, user_preference) - + # Extract results image_prompts = [] video_prompts = [] scene_descriptions = [] screenshot_paths = [s.file_path for s in screenshots] - + for i, analysis in enumerate(scene_analyses): if i < len(screenshots): # Use analysis if available, otherwise create basic prompts if isinstance(analysis, dict): - image_prompts.append( - analysis.get("t2i_prompt", f"Scene {i+1} from video") - ) - video_prompts.append( - analysis.get("i2v_prompt", f"Scene {i+1} action") - ) + image_prompts.append(analysis.get("t2i_prompt", f"Scene {i+1} from video")) + video_prompts.append(analysis.get("i2v_prompt", f"Scene {i+1} action")) scene_descriptions.append(f"Scene {i+1}") else: image_prompts.append(f"Scene {i+1} from video") video_prompts.append(f"Scene {i+1} action") scene_descriptions.append(f"Scene {i+1}") - + # Prepare metadata metadata = { "model": self.model, "extraction_mode": extraction_mode, "total_scenes": str(len(screenshots)), "video_duration": f"{screenshots[-1].timestamp:.1f}s" if screenshots else "0s", - "output_directory": output_dir + "output_directory": output_dir, } - + # Add mode-specific metadata if extraction_mode.lower() == "keyframe": - metadata.update({ - "keyframe_threshold": str(keyframe_threshold), - "min_interval_frames": str(min_interval_frames) - }) + metadata.update( + {"keyframe_threshold": str(keyframe_threshold), "min_interval_frames": str(min_interval_frames)} + ) else: - metadata.update({ - "screenshot_interval": f"{screenshot_interval}s" - }) - + metadata.update({"screenshot_interval": f"{screenshot_interval}s"}) + return { "image_prompts": image_prompts, "video_prompts": video_prompts, "scene_descriptions": scene_descriptions, "screenshot_paths": screenshot_paths, - "metadata": metadata + "metadata": metadata, } @property @@ -437,4 +398,4 @@ def tool_name(self) -> str: @property def tool_description(self) -> str: """Return the description of the tool.""" - return "Analyze videos by extracting screenshots and generating coherent image generation prompts for each scene" + return "Analyze videos by extracting screenshots and generating coherent image generation prompts" diff --git a/media-gen/utils/__init__.py b/media_gen/utils/__init__.py similarity index 57% rename from media-gen/utils/__init__.py rename to media_gen/utils/__init__.py index fe1a093..840745f 100644 --- a/media-gen/utils/__init__.py +++ b/media_gen/utils/__init__.py @@ -5,6 +5,6 @@ and other media-related utilities. """ -from .video_utils import extract_screenshots, VideoScreenshotExtractor +from .video_utils import VideoScreenshotExtractor, extract_screenshots -__all__ = ['extract_screenshots', 'VideoScreenshotExtractor'] \ No newline at end of file +__all__ = ["extract_screenshots", "VideoScreenshotExtractor"] diff --git a/media-gen/utils/video_utils.py b/media_gen/utils/video_utils.py similarity index 77% rename from media-gen/utils/video_utils.py rename to media_gen/utils/video_utils.py index 9b789a1..0058f05 100644 --- a/media-gen/utils/video_utils.py +++ b/media_gen/utils/video_utils.py @@ -9,11 +9,11 @@ from dataclasses import dataclass from datetime import timedelta from enum import Enum +from pathlib import Path from typing import List, Optional, Union import cv2 import numpy as np -from pathlib import Path logger = logging.getLogger(__name__) @@ -21,10 +21,10 @@ def _expand_path(path: str) -> Path: """ Expand a path, handling tilde (~) for home directory. - + Args: path: Path string that may contain tilde - + Returns: Expanded Path object """ @@ -33,6 +33,7 @@ def _expand_path(path: str) -> Path: class ExtractionMode(Enum): """Extraction modes for video frames.""" + INTERVAL = "interval" KEYFRAME = "keyframe" @@ -40,6 +41,7 @@ class ExtractionMode(Enum): @dataclass class ScreenshotInfo: """Information about an extracted screenshot.""" + frame_number: int timestamp: float # seconds timestamp_str: str # formatted as HH:MM:SS @@ -49,6 +51,7 @@ class ScreenshotInfo: @dataclass class KeyFrameInfo: """Information about an extracted key frame.""" + frame_number: int timestamp: float # seconds timestamp_str: str # formatted as HH:MM:SS @@ -59,19 +62,14 @@ class KeyFrameInfo: class VideoExtractor: """ Unified video frame extractor supporting both interval and keyframe modes. - + A single interface for extracting frames from videos using different strategies. """ - - def __init__( - self, - video_path: str, - mode: Union[ExtractionMode, str] = ExtractionMode.INTERVAL, - **kwargs - ): + + def __init__(self, video_path: str, mode: Union[ExtractionMode, str] = ExtractionMode.INTERVAL, **kwargs): """ Initialize the video extractor. - + Args: video_path: Path to the video file mode: Extraction mode ('interval' or 'keyframe') @@ -80,249 +78,216 @@ def __init__( self.video_path = Path(video_path) if not self.video_path.exists(): raise FileNotFoundError(f"Video file not found: {video_path}") - + # Convert string to enum if needed if isinstance(mode, str): mode = ExtractionMode(mode.lower()) self.mode = mode - + self.cap = cv2.VideoCapture(str(self.video_path)) if not self.cap.isOpened(): raise ValueError(f"Could not open video file: {video_path}") - + # Get video properties self.fps = self.cap.get(cv2.CAP_PROP_FPS) self.frame_count = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) self.duration = self.frame_count / self.fps if self.fps > 0 else 0 - + # Set mode-specific parameters self._set_mode_parameters(**kwargs) - + logger.info( f"Video loaded: {self.fps:.2f} FPS, " f"{self.frame_count} frames, " f"{self.duration:.2f}s duration, " f"mode: {self.mode.value}" ) - + def _set_mode_parameters(self, **kwargs) -> None: """Set parameters based on extraction mode.""" if self.mode == ExtractionMode.INTERVAL: - self.interval_seconds = kwargs.get('interval_seconds', 2.0) - self.start_time = kwargs.get('start_time', 0.0) + self.interval_seconds = kwargs.get("interval_seconds", 2.0) + self.start_time = kwargs.get("start_time", 0.0) elif self.mode == ExtractionMode.KEYFRAME: - self.threshold = kwargs.get('threshold', 30.0) - self.min_interval_frames = kwargs.get('min_interval_frames', 30) - + self.threshold = kwargs.get("threshold", 30.0) + self.min_interval_frames = kwargs.get("min_interval_frames", 30) + def __enter__(self): """Context manager entry.""" return self - + def __exit__(self, exc_type, exc_val, exc_tb): """Context manager exit - release video capture.""" self.release() - + def release(self) -> None: """Release the video capture object.""" if self.cap: self.cap.release() - + def _format_timestamp(self, seconds: float) -> str: """Format seconds into HH:MM:SS string.""" return str(timedelta(seconds=int(seconds))) - - def _calculate_frame_difference( - self, frame1: np.ndarray, frame2: np.ndarray - ) -> float: + + def _calculate_frame_difference(self, frame1: np.ndarray, frame2: np.ndarray) -> float: """ Calculate the difference between two frames. - + Args: frame1: First frame as numpy array frame2: Second frame as numpy array - + Returns: Difference score (higher = more different) """ # Convert to grayscale for comparison gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY) - + # Calculate absolute difference diff = cv2.absdiff(gray1, gray2) - + # Return mean difference return np.mean(diff) - + def extract( - self, - output_dir: Optional[str] = "~/Downloads", - filename_prefix: Optional[str] = None + self, output_dir: Optional[str] = "~/Downloads", filename_prefix: Optional[str] = None ) -> List[Union[ScreenshotInfo, KeyFrameInfo]]: """ Extract frames based on the configured mode. - + Args: output_dir: Directory to save frames (default: ~/Downloads) filename_prefix: Prefix for saved files (auto-generated if None) - + Returns: List of ScreenshotInfo or KeyFrameInfo objects """ if filename_prefix is None: filename_prefix = self.mode.value - + if self.mode == ExtractionMode.INTERVAL: return self._extract_interval(output_dir, filename_prefix) elif self.mode == ExtractionMode.KEYFRAME: return self._extract_keyframes(output_dir, filename_prefix) else: raise ValueError(f"Unsupported mode: {self.mode}") - - def _extract_interval( - self, output_dir: str, filename_prefix: str - ) -> List[ScreenshotInfo]: + + def _extract_interval(self, output_dir: str, filename_prefix: str) -> List[ScreenshotInfo]: """Extract frames at regular intervals.""" if self.interval_seconds <= 0: raise ValueError("Interval must be positive") if self.start_time < 0: raise ValueError("Start time must be non-negative") - + screenshots = [] frame_interval = int(self.fps * self.interval_seconds) start_frame = int(self.start_time * self.fps) - + # Handle output directory with tilde expansion - output_path = ( - _expand_path(output_dir) if output_dir - else _expand_path("~/Downloads") - ) + output_path = _expand_path(output_dir) if output_dir else _expand_path("~/Downloads") output_path.mkdir(parents=True, exist_ok=True) - + for frame_num in range(start_frame, self.frame_count, frame_interval): self.cap.set(cv2.CAP_PROP_POS_FRAMES, frame_num) ret, frame = self.cap.read() - + if not ret: break - + timestamp = frame_num / self.fps timestamp_str = self._format_timestamp(timestamp) - + screenshot_info = ScreenshotInfo( - frame_number=frame_num, - timestamp=timestamp, - timestamp_str=timestamp_str, - file_path="" + frame_number=frame_num, timestamp=timestamp, timestamp_str=timestamp_str, file_path="" ) - + # Save screenshot timestamp_seconds = int(timestamp) - filename = ( - f"{filename_prefix}_{timestamp_seconds:06d}s_" - f"{timestamp_str.replace(':', '-')}.jpg" - ) + filename = f"{filename_prefix}_{timestamp_seconds:06d}s_" f"{timestamp_str.replace(':', '-')}.jpg" file_path = output_path / filename cv2.imwrite(str(file_path), frame) screenshot_info.file_path = str(file_path) - + screenshots.append(screenshot_info) - logger.debug( - f"Extracted screenshot at {timestamp_str} " - f"(frame {frame_num})" - ) - - logger.info( - f"Extracted {len(screenshots)} screenshots " - f"at {self.interval_seconds}s intervals" - ) + logger.debug(f"Extracted screenshot at {timestamp_str} " f"(frame {frame_num})") + + logger.info(f"Extracted {len(screenshots)} screenshots " f"at {self.interval_seconds}s intervals") return screenshots - - def _extract_keyframes( - self, output_dir: str, filename_prefix: str - ) -> List[KeyFrameInfo]: + + def _extract_keyframes(self, output_dir: str, filename_prefix: str) -> List[KeyFrameInfo]: """Extract key frames using scene change detection.""" key_frames = [] prev_frame = None last_key_frame = -self.min_interval_frames - + # Handle output directory with tilde expansion - output_path = ( - _expand_path(output_dir) if output_dir - else _expand_path("~/Downloads") - ) + output_path = _expand_path(output_dir) if output_dir else _expand_path("~/Downloads") output_path.mkdir(parents=True, exist_ok=True) - + for frame_num in range(self.frame_count): self.cap.set(cv2.CAP_PROP_POS_FRAMES, frame_num) ret, frame = self.cap.read() - + if not ret: break - + # Skip if too close to last key frame if frame_num - last_key_frame < self.min_interval_frames: prev_frame = frame continue - + if prev_frame is not None: # Calculate frame difference diff_score = self._calculate_frame_difference(prev_frame, frame) - + # Check if this is a key frame if diff_score > self.threshold: timestamp = frame_num / self.fps timestamp_str = self._format_timestamp(timestamp) - + # Calculate confidence based on difference score confidence = min(1.0, diff_score / 100.0) - + key_frame_info = KeyFrameInfo( frame_number=frame_num, timestamp=timestamp, timestamp_str=timestamp_str, file_path="", - confidence=confidence + confidence=confidence, ) - + # Save key frame timestamp_seconds = int(timestamp) - filename = ( - f"{filename_prefix}_{timestamp_seconds:06d}s_" - f"{timestamp_str.replace(':', '-')}.jpg" - ) + filename = f"{filename_prefix}_{timestamp_seconds:06d}s_" f"{timestamp_str.replace(':', '-')}.jpg" file_path = output_path / filename cv2.imwrite(str(file_path), frame) key_frame_info.file_path = str(file_path) - + key_frames.append(key_frame_info) last_key_frame = frame_num - + logger.debug( - f"Extracted key frame at {timestamp_str} " - f"(frame {frame_num}, diff: {diff_score:.2f})" + f"Extracted key frame at {timestamp_str} " f"(frame {frame_num}, diff: {diff_score:.2f})" ) - + prev_frame = frame - - logger.info( - f"Extracted {len(key_frames)} key frames " - f"with threshold {self.threshold}" - ) + + logger.info(f"Extracted {len(key_frames)} key frames " f"with threshold {self.threshold}") return key_frames class KeyFrameExtractor: """ Extract key frames from video files using scene change detection. - + Uses frame difference analysis to identify significant scene changes. """ - + def __init__(self, video_path: str, threshold: float = 30.0): """ Initialize the key frame extractor. - + Args: video_path: Path to the video file threshold: Threshold for scene change detection (default: 30.0) @@ -330,217 +295,201 @@ def __init__(self, video_path: str, threshold: float = 30.0): self.video_path = Path(video_path) if not self.video_path.exists(): raise FileNotFoundError(f"Video file not found: {video_path}") - + self.cap = cv2.VideoCapture(str(self.video_path)) if not self.cap.isOpened(): raise ValueError(f"Could not open video file: {video_path}") - + self.threshold = threshold - + # Get video properties self.fps = self.cap.get(cv2.CAP_PROP_FPS) self.frame_count = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) self.duration = self.frame_count / self.fps if self.fps > 0 else 0 - + logger.info( - f"Video loaded: {self.fps:.2f} FPS, " - f"{self.frame_count} frames, " - f"{self.duration:.2f}s duration" + f"Video loaded: {self.fps:.2f} FPS, " f"{self.frame_count} frames, " f"{self.duration:.2f}s duration" ) - + def __enter__(self): """Context manager entry.""" return self - + def __exit__(self, exc_type, exc_val, exc_tb): """Context manager exit - release video capture.""" self.release() - + def release(self) -> None: """Release the video capture object.""" if self.cap: self.cap.release() - + def _format_timestamp(self, seconds: float) -> str: """Format seconds into HH:MM:SS string.""" return str(timedelta(seconds=int(seconds))) - - def _calculate_frame_difference( - self, frame1: np.ndarray, frame2: np.ndarray - ) -> float: + + def _calculate_frame_difference(self, frame1: np.ndarray, frame2: np.ndarray) -> float: """ Calculate the difference between two frames. - + Args: frame1: First frame as numpy array frame2: Second frame as numpy array - + Returns: Difference score (higher = more different) """ # Convert to grayscale for comparison gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY) - + # Calculate absolute difference diff = cv2.absdiff(gray1, gray2) - + # Return mean difference return np.mean(diff) - + def extract_key_frames( self, output_dir: Optional[str] = "~/Downloads", filename_prefix: str = "keyframe", - min_interval_frames: int = 30 + min_interval_frames: int = 30, ) -> List[KeyFrameInfo]: """ Extract key frames using scene change detection. - + Args: output_dir: Directory to save key frames (default: ~/Downloads) filename_prefix: Prefix for saved files min_interval_frames: Minimum frames between key frames - + Returns: List of KeyFrameInfo objects """ key_frames = [] prev_frame = None last_key_frame = -min_interval_frames - + # Handle output directory with tilde expansion - output_path = ( - _expand_path(output_dir) if output_dir - else _expand_path("~/Downloads") - ) + output_path = _expand_path(output_dir) if output_dir else _expand_path("~/Downloads") output_path.mkdir(parents=True, exist_ok=True) - + for frame_num in range(self.frame_count): self.cap.set(cv2.CAP_PROP_POS_FRAMES, frame_num) ret, frame = self.cap.read() - + if not ret: break - + # Skip if too close to last key frame if frame_num - last_key_frame < min_interval_frames: prev_frame = frame continue - + if prev_frame is not None: # Calculate frame difference diff_score = self._calculate_frame_difference(prev_frame, frame) - + # Check if this is a key frame if diff_score > self.threshold: timestamp = frame_num / self.fps timestamp_str = self._format_timestamp(timestamp) - + # Calculate confidence based on difference score confidence = min(1.0, diff_score / 100.0) - + key_frame_info = KeyFrameInfo( frame_number=frame_num, timestamp=timestamp, timestamp_str=timestamp_str, file_path="", - confidence=confidence + confidence=confidence, ) - + # Save key frame timestamp_seconds = int(timestamp) - filename = ( - f"{filename_prefix}_{timestamp_seconds:06d}s_" - f"{timestamp_str.replace(':', '-')}.jpg" - ) + filename = f"{filename_prefix}_{timestamp_seconds:06d}s_" f"{timestamp_str.replace(':', '-')}.jpg" file_path = output_path / filename cv2.imwrite(str(file_path), frame) key_frame_info.file_path = str(file_path) - + key_frames.append(key_frame_info) last_key_frame = frame_num - + logger.debug( - f"Extracted key frame at {timestamp_str} " - f"(frame {frame_num}, diff: {diff_score:.2f})" + f"Extracted key frame at {timestamp_str} " f"(frame {frame_num}, diff: {diff_score:.2f})" ) - + prev_frame = frame - - logger.info( - f"Extracted {len(key_frames)} key frames " - f"with threshold {self.threshold}" - ) + + logger.info(f"Extracted {len(key_frames)} key frames " f"with threshold {self.threshold}") return key_frames class VideoScreenshotExtractor: """ Extract screenshots from video files at regular intervals. - + Simple utility to take screenshots every X seconds from a video file. """ - + def __init__(self, video_path: str): """ Initialize the screenshot extractor. - + Args: video_path: Path to the video file """ self.video_path = Path(video_path) if not self.video_path.exists(): raise FileNotFoundError(f"Video file not found: {video_path}") - + self.cap = cv2.VideoCapture(str(self.video_path)) if not self.cap.isOpened(): raise ValueError(f"Could not open video file: {video_path}") - + # Get video properties self.fps = self.cap.get(cv2.CAP_PROP_FPS) self.frame_count = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) self.duration = self.frame_count / self.fps if self.fps > 0 else 0 - + logger.info( - f"Video loaded: {self.fps:.2f} FPS, " - f"{self.frame_count} frames, " - f"{self.duration:.2f}s duration" + f"Video loaded: {self.fps:.2f} FPS, " f"{self.frame_count} frames, " f"{self.duration:.2f}s duration" ) - + def __enter__(self): """Context manager entry.""" return self - + def __exit__(self, exc_type, exc_val, exc_tb): """Context manager exit - release video capture.""" self.release() - + def release(self) -> None: """Release the video capture object.""" if self.cap: self.cap.release() - + def _format_timestamp(self, seconds: float) -> str: """Format seconds into HH:MM:SS string.""" return str(timedelta(seconds=int(seconds))) - + def extract_screenshots( self, interval_seconds: float = 2.0, start_time: float = 0.0, output_dir: Optional[str] = "~/Downloads", - filename_prefix: str = "screenshot" + filename_prefix: str = "screenshot", ) -> List[ScreenshotInfo]: """ Extract screenshots at regular time intervals. - + Args: interval_seconds: Time interval between screenshots (default: 2.0) start_time: Start time in seconds (default: 0.0) output_dir: Directory to save screenshots (default: ~/Downloads) filename_prefix: Prefix for saved files - + Returns: List of ScreenshotInfo objects """ @@ -548,56 +497,41 @@ def extract_screenshots( raise ValueError("Interval must be positive") if start_time < 0: raise ValueError("Start time must be non-negative") - + screenshots = [] frame_interval = int(self.fps * interval_seconds) start_frame = int(start_time * self.fps) - + # Handle output directory with tilde expansion - output_path = ( - _expand_path(output_dir) if output_dir - else _expand_path("~/Downloads") - ) + output_path = _expand_path(output_dir) if output_dir else _expand_path("~/Downloads") output_path.mkdir(parents=True, exist_ok=True) - + for frame_num in range(start_frame, self.frame_count, frame_interval): self.cap.set(cv2.CAP_PROP_POS_FRAMES, frame_num) ret, frame = self.cap.read() - + if not ret: break - + timestamp = frame_num / self.fps timestamp_str = self._format_timestamp(timestamp) - + screenshot_info = ScreenshotInfo( - frame_number=frame_num, - timestamp=timestamp, - timestamp_str=timestamp_str, - file_path="" + frame_number=frame_num, timestamp=timestamp, timestamp_str=timestamp_str, file_path="" ) - + # Always save screenshots since we have a default output directory # Use timestamp-based filename for chronological sorting timestamp_seconds = int(timestamp) - filename = ( - f"{filename_prefix}_{timestamp_seconds:06d}s_" - f"{timestamp_str.replace(':', '-')}.jpg" - ) + filename = f"{filename_prefix}_{timestamp_seconds:06d}s_" f"{timestamp_str.replace(':', '-')}.jpg" file_path = output_path / filename cv2.imwrite(str(file_path), frame) screenshot_info.file_path = str(file_path) - + screenshots.append(screenshot_info) - logger.debug( - f"Extracted screenshot at {timestamp_str} " - f"(frame {frame_num})" - ) - - logger.info( - f"Extracted {len(screenshots)} screenshots " - f"at {interval_seconds}s intervals" - ) + logger.debug(f"Extracted screenshot at {timestamp_str} " f"(frame {frame_num})") + + logger.info(f"Extracted {len(screenshots)} screenshots " f"at {interval_seconds}s intervals") return screenshots @@ -606,18 +540,18 @@ def extract_screenshots( interval_seconds: float = 2.0, start_time: float = 0.0, output_dir: Optional[str] = "~/Downloads", - filename_prefix: str = "screenshot" + filename_prefix: str = "screenshot", ) -> List[ScreenshotInfo]: """ Convenience function to extract screenshots from a video file. - + Args: video_path: Path to the video file interval_seconds: Time interval between screenshots (default: 2.0) start_time: Start time in seconds (default: 0.0) output_dir: Directory to save screenshots (default: ~/Downloads) filename_prefix: Prefix for saved files - + Returns: List of ScreenshotInfo objects """ @@ -626,7 +560,7 @@ def extract_screenshots( interval_seconds=interval_seconds, start_time=start_time, output_dir=output_dir, - filename_prefix=filename_prefix + filename_prefix=filename_prefix, ) @@ -635,26 +569,24 @@ def extract_key_frames( threshold: float = 30.0, output_dir: Optional[str] = "~/Downloads", filename_prefix: str = "keyframe", - min_interval_frames: int = 30 + min_interval_frames: int = 30, ) -> List[KeyFrameInfo]: """ Convenience function to extract key frames from a video file. - + Args: video_path: Path to the video file threshold: Threshold for scene change detection (default: 30.0) output_dir: Directory to save key frames (default: ~/Downloads) filename_prefix: Prefix for saved files min_interval_frames: Minimum frames between key frames - + Returns: List of KeyFrameInfo objects """ with KeyFrameExtractor(video_path, threshold) as extractor: return extractor.extract_key_frames( - output_dir=output_dir, - filename_prefix=filename_prefix, - min_interval_frames=min_interval_frames + output_dir=output_dir, filename_prefix=filename_prefix, min_interval_frames=min_interval_frames ) @@ -663,18 +595,18 @@ def extract_frames( mode: Union[ExtractionMode, str] = ExtractionMode.INTERVAL, output_dir: Optional[str] = "~/Downloads", filename_prefix: Optional[str] = None, - **kwargs + **kwargs, ) -> List[Union[ScreenshotInfo, KeyFrameInfo]]: """ Unified convenience function to extract frames from a video file. - + Args: video_path: Path to the video file mode: Extraction mode ('interval' or 'keyframe') output_dir: Directory to save frames (default: ~/Downloads) filename_prefix: Prefix for saved files (auto-generated if None) **kwargs: Mode-specific parameters - + Returns: List of ScreenshotInfo or KeyFrameInfo objects """ diff --git a/media-gen/video_regen_pipeline.py b/media_gen/video_regen_pipeline.py similarity index 77% rename from media-gen/video_regen_pipeline.py rename to media_gen/video_regen_pipeline.py index 9966486..63f2215 100644 --- a/media-gen/video_regen_pipeline.py +++ b/media_gen/video_regen_pipeline.py @@ -7,7 +7,7 @@ 3. Generate videos from each image using video generation Usage: - python video_regen_pipeline.py --video-path + python video_regen_pipeline.py --video-path --user-interests Example: @@ -21,13 +21,13 @@ import os import sys import time -from typing import Any, Dict, List - from pathlib import Path +from typing import Any, Dict, List # Load environment variables from .env file if it exists try: from dotenv import load_dotenv + load_dotenv() except ImportError: # dotenv not installed, continue without it @@ -47,7 +47,7 @@ def expand_path(path: str) -> str: class VideoRegenerationPipeline(MediaGenerationPipeline): """ Pipeline for regenerating videos. - + Workflow: 1. Analyze original video using video understanding 2. Generate images for each scene using image generation @@ -60,11 +60,11 @@ def __init__( image_generation_tool: Any, # BaseTool type video_generation_tool: Any, # BaseTool type name: str = "video_regeneration", - debug: bool = False + debug: bool = False, ): """ Initialize the video regeneration pipeline. - + Args: video_understanding_tool: Tool for analyzing videos image_generation_tool: Tool for generating images @@ -74,7 +74,7 @@ def __init__( """ super().__init__(name) self.debug = debug - + # Add video understanding step self.add_step( PipelineStep( @@ -87,18 +87,18 @@ def __init__( "screenshot_interval": "screenshot_interval", "keyframe_threshold": "keyframe_threshold", "min_interval_frames": "min_interval_frames", - "output_dir": "output_dir" + "output_dir": "output_dir", }, output_mapping={ "image_prompts": "image_prompts", "video_prompts": "video_prompts", "scene_descriptions": "scene_descriptions", "screenshot_paths": "screenshot_paths", - "metadata": "video_metadata" - } + "metadata": "video_metadata", + }, ) ) - + # Add image generation step (for each scene) self.add_step( PipelineStep( @@ -108,17 +108,17 @@ def __init__( "image_prompts": "prompt", "output_folder": "output_folder", "aspect_ratio": "aspect_ratio", - "output_format": "output_format" + "output_format": "output_format", }, output_mapping={ "generated_image_paths": "generated_image_paths", - "image_generation_info": "image_generation_info" + "image_generation_info": "image_generation_info", }, transform_input=self._prepare_image_generation, - transform_output=self._extract_image_paths + transform_output=self._extract_image_paths, ) ) - + # Add video generation step (for each generated image) self.add_step( PipelineStep( @@ -128,17 +128,17 @@ def __init__( "generated_image_paths": "image", "video_prompts": "prompt", "output_folder": "output_folder", - "output_format": "output_format" + "output_format": "output_format", }, output_mapping={ "generated_videos": "generated_video_paths", - "video_generation_info": "video_generation_info" + "video_generation_info": "video_generation_info", }, transform_input=self._prepare_video_generation, - transform_output=self._extract_video_paths + transform_output=self._extract_video_paths, ) ) - + def regenerate( self, video_path: str, @@ -149,15 +149,15 @@ def regenerate( keyframe_threshold: float = 30.0, min_interval_frames: int = 30, aspect_ratio: str = "9:16", - output_format: str = "mp4" + output_format: str = "mp4", ) -> Dict[str, Any]: """ Regenerate a video based on the original. - + Args: video_path: Path to the original video user_interests: User preferences for regeneration - output_folder: Folder to save generated videos + output_folder: Folder to save generated videos (default: ~/Downloads) extraction_mode: 'interval' or 'keyframe' (default: 'interval') screenshot_interval: Time interval between screenshots (for interval mode) @@ -165,7 +165,7 @@ def regenerate( min_interval_frames: Minimum frames between keyframes (for keyframe mode) aspect_ratio: Aspect ratio for generated images output_format: Output format for generated videos - + Returns: Dictionary containing: - generated_video_paths: List of paths to generated videos @@ -176,7 +176,7 @@ def regenerate( # Create organized output structure expanded_output_folder = expand_path(output_folder) session_folder = f"{expanded_output_folder}/video_regen_{int(time.time())}" - + # Prepare input data input_data = { "original_video": video_path, @@ -188,20 +188,18 @@ def regenerate( "output_dir": f"{session_folder}/video_analysis", "output_folder": session_folder, "aspect_ratio": aspect_ratio, - "output_format": output_format + "output_format": output_format, } - + # Run pipeline result = self.run(input_data) - + return result - - def _prepare_image_generation( - self, tool_input: Dict[str, Any] - ) -> Dict[str, Any]: + + def _prepare_image_generation(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: """ Prepare input for image generation step. - + Takes the list of image prompts and prepares them for batch image generation. """ @@ -210,146 +208,139 @@ def _prepare_image_generation( aspect_ratio = tool_input.get("aspect_ratio", "1:1") # Always use png for images, regardless of the global output_format output_format = "png" - + # Create a subfolder for generated images image_output_folder = f"{output_folder}/generated_images" - + # Display prompts being used - print(f"\nšŸŽØ IMAGE GENERATION STEP") + print("\nšŸŽØ IMAGE GENERATION STEP") print(f"šŸ“ Output folder: {image_output_folder}") print(f"šŸ“ Processing {len(image_prompts)} image prompts:") - + for i, prompt in enumerate(image_prompts): - print(f" Scene {i+1}: {prompt}") - + print(f" Scene {i + 1}: {prompt}") + # Debug: Print the image prompts being processed if self.debug: print("\nšŸ” DEBUG - Image Generation Input:") print(f" Number of prompts: {len(image_prompts)}") for i, prompt in enumerate(image_prompts): - print(f" Prompt {i+1}: {prompt[:100]}...") + print(f" Prompt {i + 1}: {prompt[:100]}...") print(f" Output folder: {image_output_folder}") - + return { "prompt": image_prompts, "output_folder": image_output_folder, "aspect_ratio": aspect_ratio, - "output_format": output_format + "output_format": output_format, } - - def _extract_image_paths( - self, tool_output: Dict[str, Any] - ) -> Dict[str, Any]: + + def _extract_image_paths(self, tool_output: Dict[str, Any]) -> Dict[str, Any]: """ Extract image paths from image generation output. - + The image generation tool returns a list of image paths. """ generated_images = tool_output.get("generated_image_paths", []) generation_info = tool_output.get("image_generation_info", {}) - + # Filter out empty paths and check for errors valid_images = [] errors = [] - + for i, image_path in enumerate(generated_images): if image_path and os.path.exists(image_path): valid_images.append(image_path) - print(f"āœ… Generated image {i+1}: {os.path.basename(image_path)}") + print(f"āœ… Generated image {i + 1}: {os.path.basename(image_path)}") else: - error_msg = f"Image {i+1} generation failed" + error_msg = f"Image {i + 1} generation failed" if isinstance(generation_info, list) and i < len(generation_info): error_info = generation_info[i] if isinstance(error_info, dict) and "error" in error_info: error_msg += f": {error_info['error']}" errors.append(error_msg) print(f"āŒ {error_msg}") - + # If no images were generated successfully, create fallback images if not valid_images: print("\nāš ļø No images generated successfully. Creating fallback images...") valid_images = self._create_fallback_images( - tool_output.get("prompt", []), - tool_output.get("output_folder", "~/Downloads") + tool_output.get("prompt", []), tool_output.get("output_folder", "~/Downloads") ) - + # Debug: Print the generated image paths if self.debug: print("\nšŸ” DEBUG - Image Generation Output:") print(f" Number of valid images: {len(valid_images)}") print(f" Number of errors: {len(errors)}") for i, image_path in enumerate(valid_images): - print(f" Image {i+1}: {image_path}") - + print(f" Image {i + 1}: {image_path}") + return { "generated_image_paths": valid_images, "image_generation_info": generation_info, - "image_generation_errors": errors + "image_generation_errors": errors, } - - def _create_fallback_images( - self, prompts: List[str], output_folder: str - ) -> List[str]: + + def _create_fallback_images(self, prompts: List[str], output_folder: str) -> List[str]: """ Create fallback images when image generation fails. - + Args: prompts: List of prompts that failed to generate images output_folder: Output folder for fallback images - + Returns: List of paths to fallback images """ fallback_images = [] - + for i, prompt in enumerate(prompts): # Create a simple text-based fallback image try: from PIL import Image, ImageDraw, ImageFont # Create a simple image with the prompt text - img = Image.new('RGB', (512, 512), color='#2c3e50') + img = Image.new("RGB", (512, 512), color="#2c3e50") draw = ImageDraw.Draw(img) - + # Try to use a default font, fallback to default if not available try: font = ImageFont.truetype("/System/Library/Fonts/Arial.ttf", 20) except OSError: font = ImageFont.load_default() - + # Draw the prompt text - text = f"Fallback Image {i+1}\n{prompt[:100]}..." - draw.text((50, 50), text, fill='white', font=font) - + text = f"Fallback Image {i + 1}\n{prompt[:100]}..." + draw.text((50, 50), text, fill="white", font=font) + # Save the fallback image - fallback_path = f"{output_folder}/fallback_image_{i+1}.png" + fallback_path = f"{output_folder}/fallback_image_{i + 1}.png" os.makedirs(os.path.dirname(fallback_path), exist_ok=True) img.save(fallback_path) - + fallback_images.append(fallback_path) - print(f"šŸ“ Created fallback image {i+1}: {os.path.basename(fallback_path)}") - + print(f"šŸ“ Created fallback image {i + 1}: {os.path.basename(fallback_path)}") + except ImportError: # If PIL is not available, create a simple text file - fallback_path = f"{output_folder}/fallback_image_{i+1}.txt" + fallback_path = f"{output_folder}/fallback_image_{i + 1}.txt" os.makedirs(os.path.dirname(fallback_path), exist_ok=True) - - with open(fallback_path, 'w') as f: - f.write(f"Fallback Image {i+1}\n") + + with open(fallback_path, "w") as f: + f.write(f"Fallback Image {i + 1}\n") f.write(f"Prompt: {prompt}\n") f.write("This is a fallback file because image generation failed.\n") - + fallback_images.append(fallback_path) - print(f"šŸ“ Created fallback file {i+1}: {os.path.basename(fallback_path)}") - + print(f"šŸ“ Created fallback file {i + 1}: {os.path.basename(fallback_path)}") + return fallback_images - - def _prepare_video_generation( - self, tool_input: Dict[str, Any] - ) -> Dict[str, Any]: + + def _prepare_video_generation(self, tool_input: Dict[str, Any]) -> Dict[str, Any]: """ Prepare input for video generation step. - + Takes the list of generated images and video prompts and prepares them for batch video generation. """ @@ -357,106 +348,100 @@ def _prepare_video_generation( video_prompts = tool_input.get("prompt", []) output_folder = tool_input.get("output_folder", "~/Downloads") output_format = tool_input.get("output_format", "mp4") - + # Create a subfolder for generated videos video_output_folder = f"{output_folder}/generated_videos" - + # Display video generation info - print(f"\nšŸŽ¬ VIDEO GENERATION STEP") + print("\nšŸŽ¬ VIDEO GENERATION STEP") print(f"šŸ“ Output folder: {video_output_folder}") print(f"šŸ–¼ļø Processing {len(generated_images)} images:") - - for i, (image_path, prompt) in enumerate( - zip(generated_images, video_prompts) - ): + + for i, (image_path, prompt) in enumerate(zip(generated_images, video_prompts)): image_name = os.path.basename(image_path) if image_path else "No image" - print(f" Video {i+1}: {image_name}") + print(f" Video {i + 1}: {image_name}") print(f" Prompt: {prompt}") - + # Debug: Print the video generation input if self.debug: print("\nšŸ” DEBUG - Video Generation Input:") print(f" Number of images: {len(generated_images)}") print(f" Number of prompts: {len(video_prompts)}") - for i, (image_path, prompt) in enumerate( - zip(generated_images, video_prompts) - ): - print(f" Pair {i+1}:") + for i, (image_path, prompt) in enumerate(zip(generated_images, video_prompts)): + print(f" Pair {i + 1}:") print(f" Image: {image_path}") print(f" Prompt: {prompt[:100]}...") print(f" Output folder: {video_output_folder}") - + return { "image": generated_images, "prompt": video_prompts, "output_folder": video_output_folder, "output_format": output_format, - "aspect_ratio": tool_input.get("aspect_ratio", "9:16") + "aspect_ratio": tool_input.get("aspect_ratio", "9:16"), } - - def _extract_video_paths( - self, tool_output: Dict[str, Any] - ) -> Dict[str, Any]: + + def _extract_video_paths(self, tool_output: Dict[str, Any]) -> Dict[str, Any]: """ Extract video paths from video generation output. - + The video generation tool returns a list of video paths. """ generated_videos = tool_output.get("generated_video_paths", []) generation_info = tool_output.get("video_generation_info", {}) - + # Filter out empty paths and check for errors valid_videos = [] errors = [] - + for i, video_path in enumerate(generated_videos): if video_path and os.path.exists(video_path): valid_videos.append(video_path) - print(f"āœ… Generated video {i+1}: {os.path.basename(video_path)}") + print(f"āœ… Generated video {i + 1}: {os.path.basename(video_path)}") else: - error_msg = f"Video {i+1} generation failed" + error_msg = f"Video {i + 1} generation failed" if isinstance(generation_info, list) and i < len(generation_info): error_info = generation_info[i] if isinstance(error_info, dict) and "error" in error_info: error_msg += f": {error_info['error']}" errors.append(error_msg) print(f"āŒ {error_msg}") - + # Debug: Print the generated video paths if self.debug: print("\nšŸ” DEBUG - Video Generation Output:") print(f" Number of videos: {len(valid_videos)}") print(f" Number of errors: {len(errors)}") for i, video_path in enumerate(valid_videos): - print(f" Video {i+1}: {video_path}") - + print(f" Video {i + 1}: {video_path}") + return { "generated_video_paths": valid_videos, "video_generation_info": generation_info, - "video_generation_errors": errors + "video_generation_errors": errors, } def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """ Execute the pipeline with the given input. - + Args: input_data: Initial input data for the pipeline - + Returns: Final output from the last step """ # Expand paths expanded_video_path = expand_path(input_data["original_video"]) expanded_output_folder = expand_path(input_data["output_folder"]) - + # Create output directory if it doesn't exist Path(expanded_output_folder).mkdir(parents=True, exist_ok=True) - + # Update input data with expanded paths input_data["original_video"] = expanded_video_path input_data["output_folder"] = expanded_output_folder - + # Debug: Show initial input if self.debug: print("\nšŸ” DEBUG - Initial Pipeline Input:") @@ -469,22 +454,18 @@ def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]: print(f" ... and {len(value) - 3} more") else: print(f" {key}: {value}") - + # Run the parent pipeline with custom execution - self.logger.info( - f"Starting pipeline execution with {len(self.steps)} steps" - ) - + self.logger.info(f"Starting pipeline execution with {len(self.steps)} steps") + current_input = input_data.copy() - + for i, step in enumerate(self.steps): - self.logger.info( - f"Executing step {i+1}/{len(self.steps)}: {step.name}" - ) - + self.logger.info(f"Executing step {i + 1}/{len(self.steps)}: {step.name}") + # Debug: Show input to this step if self.debug: - print(f"\nšŸ” DEBUG - Input to step {i+1} ({step.name}):") + print(f"\nšŸ” DEBUG - Input to step {i + 1} ({step.name}):") for key, value in current_input.items(): if isinstance(value, list) and len(value) > 3: print(f" {key}: {len(value)} items") @@ -494,14 +475,14 @@ def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]: print(f" ... and {len(value) - 3} more") else: print(f" {key}: {value}") - + # Execute the step executor = PipelineStepExecutor(step) step_output = executor.execute(current_input) - + # Debug: Show output from this step if self.debug: - print(f"\nšŸ” DEBUG - Output from step {i+1} ({step.name}):") + print(f"\nšŸ” DEBUG - Output from step {i + 1} ({step.name}):") for key, value in step_output.items(): if isinstance(value, list) and len(value) > 3: print(f" {key}: {len(value)} items") @@ -511,14 +492,14 @@ def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]: print(f" ... and {len(value) - 3} more") else: print(f" {key}: {value}") - + # Merge step output with current input for next step current_input.update(step_output) - + self.logger.debug(f"Step {step.name} output: {step_output}") - + self.logger.info("Pipeline execution completed") - + # Debug: Show final result if self.debug: print("\nšŸ” DEBUG - Final Pipeline Result Keys:") @@ -527,34 +508,31 @@ def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]: print(f" {key}: {len(value)} items") else: print(f" {key}: {value}") - + return current_input def setup_logging(): """Setup logging.""" - logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' - ) + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") def check_environment_setup(): """ Check if the environment is properly set up for the pipeline. - + Returns: bool: True if environment is ready, False otherwise """ missing_vars = [] - + # Check for required API keys if not os.getenv("OPENAI_API_KEY"): missing_vars.append("OPENAI_API_KEY") - + if not os.getenv("REPLICATE_API_TOKEN"): missing_vars.append("REPLICATE_API_TOKEN") - + if missing_vars: print("āŒ Missing required environment variables:") for var in missing_vars: @@ -562,108 +540,90 @@ def check_environment_setup(): print("\nšŸ“ Please set these variables in your .env file:") print(" Copy env.example to .env and fill in your API keys") return False - + print("āœ… Environment setup looks good!") return True def main(): """Main function for command-line execution.""" - parser = argparse.ArgumentParser( - description="Regenerate a video based on user interests" - ) + parser = argparse.ArgumentParser(description="Regenerate a video based on user interests") parser.add_argument( "--video-path", default="./examples/media-gen/integration_tests/test_video.mp4", - help="Path to the original video file (supports ~ for home directory). " - "Default: ./examples/media-gen/integration_tests/test_video.mp4" + help=( + "Path to the original video file (supports ~ for home directory). " + "Default: ./examples/media-gen/integration_tests/test_video.mp4" + ), ) parser.add_argument( "--user-interests", - default="Users like cinematic style with dramatic lighting and " - "professional video quality", - help="User interests/preferences for regeneration. " - "Default: cinematic style with dramatic lighting" + default="Users like cinematic style with dramatic lighting and " "professional video quality", + help=("User interests/preferences for regeneration. " "Default: cinematic style with dramatic lighting"), ) parser.add_argument( - "--output-folder", - default="~/Downloads", - help="Output folder for generated videos (default: ~/Downloads)" + "--output-folder", default="~/Downloads", help="Output folder for generated videos (default: ~/Downloads)" ) parser.add_argument( "--extraction-mode", choices=["interval", "keyframe"], default="interval", - help="Extraction mode: 'interval' for regular intervals or 'keyframe' for scene changes (default: interval)" + help=( + "Extraction mode: 'interval' for regular intervals or " "'keyframe' for scene changes (default: interval)" + ), ) parser.add_argument( "--screenshot-interval", type=float, default=10.0, - help="Time interval between screenshots in seconds (for interval mode, default: 10.0)" + help=("Time interval between screenshots in seconds " "(for interval mode, default: 10.0)"), ) parser.add_argument( "--keyframe-threshold", type=float, default=30.0, - help="Threshold for keyframe detection (for keyframe mode, default: 30.0)" + help=("Threshold for keyframe detection " "(for keyframe mode, default: 30.0)"), ) parser.add_argument( "--min-interval-frames", type=int, default=30, - help="Minimum frames between keyframes (for keyframe mode, default: 30)" - ) - parser.add_argument( - "--aspect-ratio", - default="1:1", - help="Aspect ratio for generated images (default: 1:1)" - ) - parser.add_argument( - "--output-format", - default="mp4", - help="Output format for generated videos (default: mp4)" + help=("Minimum frames between keyframes " "(for keyframe mode, default: 30)"), ) + parser.add_argument("--aspect-ratio", default="1:1", help="Aspect ratio for generated images (default: 1:1)") + parser.add_argument("--output-format", default="mp4", help="Output format for generated videos (default: mp4)") parser.add_argument( "--image-generator", choices=["replicate"], default="replicate", - help="Image generation service to use (default: replicate)" - ) - parser.add_argument( - "--debug", - action="store_true", - help="Enable debug output to see prompts used in each step" + help="Image generation service to use (default: replicate)", ) - parser.add_argument( - "--check-env", - action="store_true", - help="Check environment setup and exit" - ) - + parser.add_argument("--debug", action="store_true", help="Enable debug output to see prompts used in each step") + parser.add_argument("--check-env", action="store_true", help="Check environment setup and exit") + args = parser.parse_args() - + # Check environment if requested if args.check_env: check_environment_setup() return - + # Expand paths expanded_video_path = expand_path(args.video_path) - + # Validate input video exists if not Path(expanded_video_path).exists(): print(f"āŒ Error: Video file not found: {expanded_video_path}") print("šŸ’” Make sure the test video exists or provide a different path") sys.exit(1) - + # Check environment setup if not check_environment_setup(): sys.exit(1) - + # Setup logging setup_logging() - + print("\nšŸŽ¬ Starting video regeneration pipeline...") print(f"šŸ“¹ Input video: {expanded_video_path}") print(f"šŸŽÆ User interests: {args.user_interests}") @@ -678,25 +638,25 @@ def main(): print(f"šŸ”§ Debug mode: {args.debug}") print("ā±ļø Estimated time: 5-15 minutes (depends on video length)") print() - + try: # Initialize tools video_understanding = VideoUnderstandingTool() - + # Initialize image generation tool image_gen = ReplicateImageGen() - + # Initialize video generation tool video_gen = ReplicateVideoGen() - + # Create pipeline pipeline = VideoRegenerationPipeline( video_understanding_tool=video_understanding, image_generation_tool=image_gen, video_generation_tool=video_gen, - debug=args.debug + debug=args.debug, ) - + # Regenerate video result = pipeline.regenerate( video_path=args.video_path, @@ -707,20 +667,20 @@ def main(): keyframe_threshold=args.keyframe_threshold, min_interval_frames=args.min_interval_frames, aspect_ratio=args.aspect_ratio, - output_format=args.output_format + output_format=args.output_format, ) - + # Output results - print("\n" + "="*60) + print("\n" + "=" * 60) print("šŸŽ¬ VIDEO REGENERATION COMPLETE") - print("="*60) - + print("=" * 60) + # Get video paths from the result - generated_videos = result.get('generated_video_paths', []) + generated_videos = result.get("generated_video_paths", []) if generated_videos: print("šŸ“ Videos stored at:") for i, video_path in enumerate(generated_videos): - print(f" Video {i+1}: {video_path}") + print(f" Video {i + 1}: {video_path}") # Show relative path if it's in Downloads downloads_path = os.path.expanduser("~/Downloads") if video_path.startswith(downloads_path): @@ -728,77 +688,75 @@ def main(): print(f" šŸ“‚ Relative to Downloads: {relative_path}") else: print("āŒ No videos generated") - + # Get image paths from the result - generated_images = result.get('generated_image_paths', []) + generated_images = result.get("generated_image_paths", []) if generated_images: print("\nšŸ–¼ļø Generated images:") for i, image_path in enumerate(generated_images): - print(f" Image {i+1}: {image_path}") - + print(f" Image {i + 1}: {image_path}") + # Get video analysis - video_analysis = result.get('video_metadata', {}) + video_analysis = result.get("video_metadata", {}) if video_analysis: print("\nšŸ” Video Analysis:") - total_scenes = video_analysis.get('total_scenes', 'Unknown') + total_scenes = video_analysis.get("total_scenes", "Unknown") print(f" Total scenes: {total_scenes}") - video_duration = video_analysis.get('video_duration', 'Unknown') + video_duration = video_analysis.get("video_duration", "Unknown") print(f" Video duration: {video_duration}") - extraction_mode = video_analysis.get('extraction_mode', 'Unknown') + extraction_mode = video_analysis.get("extraction_mode", "Unknown") print(f" Extraction mode: {extraction_mode}") - + if extraction_mode == "keyframe": - keyframe_threshold = video_analysis.get('keyframe_threshold', 'Unknown') - min_interval_frames = video_analysis.get('min_interval_frames', 'Unknown') + keyframe_threshold = video_analysis.get("keyframe_threshold", "Unknown") + min_interval_frames = video_analysis.get("min_interval_frames", "Unknown") print(f" Keyframe threshold: {keyframe_threshold}") print(f" Min interval frames: {min_interval_frames}") else: - screenshot_interval = video_analysis.get( - 'screenshot_interval', 'Unknown' - ) + screenshot_interval = video_analysis.get("screenshot_interval", "Unknown") print(f" Screenshot interval: {screenshot_interval}") - + # Get scene descriptions - scene_descriptions = result.get('scene_descriptions', []) + scene_descriptions = result.get("scene_descriptions", []) if scene_descriptions: print("\nšŸŽ­ Scene Descriptions:") for i, description in enumerate(scene_descriptions): - print(f" Scene {i+1}: {description}") - + print(f" Scene {i + 1}: {description}") + # Get generation info - video_generation_info = result.get('video_generation_info', []) + video_generation_info = result.get("video_generation_info", []) if video_generation_info: print("\nšŸ“Š Video Generation Info:") if isinstance(video_generation_info, list): for i, info in enumerate(video_generation_info): if isinstance(info, dict): - print(f" Video {i+1}:") + print(f" Video {i + 1}:") for key, value in info.items(): - if key == 'prompt': + if key == "prompt": print(f" {key}: {str(value)[:100]}...") else: print(f" {key}: {value}") else: - print(f" Video {i+1}: {info}") + print(f" Video {i + 1}: {info}") elif isinstance(video_generation_info, dict): for key, value in video_generation_info.items(): print(f" {key}: {value}") else: print(f" {video_generation_info}") - + # Show any errors that occurred - image_errors = result.get('image_generation_errors', []) - video_errors = result.get('video_generation_errors', []) - + image_errors = result.get("image_generation_errors", []) + video_errors = result.get("video_generation_errors", []) + if image_errors or video_errors: print("\nāš ļø Errors encountered:") for error in image_errors: print(f" Image: {error}") for error in video_errors: print(f" Video: {error}") - - print("="*60) - + + print("=" * 60) + except Exception as e: print(f"āŒ Error: {e}") sys.exit(1) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..7bf32e1 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,122 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "media-regen" +version = "0.1.0" +description = "Media regeneration pipeline using Polymind framework" +readme = "README.md" +requires-python = ">=3.10" +license = {text = "MIT"} +authors = [ + {name = "Media Regen Team"} +] +keywords = ["ai", "media", "generation", "polymind"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] + +dependencies = [ + "polymind", + "anthropic", + "openai", + "replicate", + "requests", + "opencv-python==4.8.1.78", + "numpy==1.26.0", + "python-dotenv", +] + +[project.optional-dependencies] +dev = [ + "pytest>=7.0.0", + "pytest-cov", + "black", + "isort", + "flake8", + "mypy", +] + +[project.urls] +Homepage = "https://github.com/your-org/media-regen" +Repository = "https://github.com/your-org/media-regen" +Issues = "https://github.com/your-org/media-regen/issues" + +[tool.hatch.build.targets.wheel] +packages = ["media_gen"] + +[tool.black] +line-length = 120 +target-version = ['py310'] +include = '\.pyi?$' +extend-exclude = ''' +/( + # directories + \.eggs + | \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | build + | dist +)/ +''' + +[tool.isort] +profile = "black" +multi_line_output = 3 +line_length = 120 +known_first_party = ["media_gen"] + +[tool.flake8] +max-line-length = 120 +extend-ignore = ["E203", "W503", "E402"] +exclude = [ + ".git", + "__pycache__", + "build", + "dist", + ".venv", + ".mypy_cache", + ".tox", +] + +[tool.mypy] +python_version = "3.10" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +check_untyped_defs = true +disallow_untyped_decorators = true +no_implicit_optional = true +warn_redundant_casts = true +warn_unused_ignores = true +warn_no_return = true +warn_unreachable = true +strict_equality = true + +[tool.pytest.ini_options] +testpaths = ["media_gen/tests"] +python_files = ["test_*.py"] +python_classes = ["Test*"] +python_functions = ["test_*"] +addopts = [ + "--strict-markers", + "--strict-config", + "--cov=media_gen", + "--cov-report=term-missing", + "--cov-report=html", +] +markers = [ + "slow: marks tests as slow (deselect with '-m \"not slow\"')", + "integration: marks tests as integration tests", +] diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 37f7fa9..0000000 --- a/requirements.txt +++ /dev/null @@ -1,22 +0,0 @@ -# Core Polymind framework -polymind - -# Additional dependencies for media generation examples -# (Add any specific media generation libraries here when implementing real tools) -# openai # For DALL-E integration -# stability-sdk # For Stable Diffusion -# replicate # For various AI models - -# Development dependencies -pytest # For running tests -python-dotenv # For environment variable management - -# Additional dependencies that Polymind requires -anthropic # For Claude integration -openai # For OpenAI integration -replicate # For Replicate integration -requests # For downloading images from URLs - -# Video processing dependencies -opencv-python==4.8.1.78 # For video screenshot extraction (compatible with numpy 1.26.0) -numpy==1.26.0 # Required by polymind and compatible with opencv-python 4.8.1.78 diff --git a/setup.py b/setup.py deleted file mode 100644 index 3a68e8c..0000000 --- a/setup.py +++ /dev/null @@ -1,109 +0,0 @@ -#!/usr/bin/env python3 -""" -Setup script for Media Generation Example (Linux). -Handles virtual environment creation and environment configuration. -""" -import subprocess -import sys - -import shutil -from pathlib import Path - - -def run_command( - cmd: list[str], check: bool = True -) -> subprocess.CompletedProcess: - """Run a command and return the result.""" - print(f"Running: {' '.join(cmd)}") - return subprocess.run( - cmd, check=check, capture_output=True, text=True - ) - - -def check_python_version() -> None: - """Check if Python version is compatible.""" - if sys.version_info < (3, 10): - print("Error: Python 3.10+ is required") - sys.exit(1) - version = f"{sys.version_info.major}.{sys.version_info.minor}" - print(f"āœ“ Python {version} detected") - - -def create_virtual_environment() -> None: - """Create virtual environment.""" - venv_path = Path("venv") - - if venv_path.exists(): - print("āœ“ Virtual environment already exists") - response = input("Do you want to recreate it? (y/N): ").lower() - if response == 'y': - shutil.rmtree(venv_path) - else: - return - - print("Creating virtual environment...") - run_command([sys.executable, "-m", "venv", "venv"]) - print("āœ“ Virtual environment created") - - -def install_requirements() -> None: - """Install requirements in the virtual environment.""" - pip_path = "venv/bin/pip" - - print("Upgrading pip...") - run_command([pip_path, "install", "--upgrade", "pip"]) - - print("Installing requirements...") - run_command([pip_path, "install", "-r", "requirements.txt"]) - print("āœ“ Requirements installed") - - -def setup_environment_file() -> None: - """Set up the .env file from template.""" - env_file = Path(".env") - example_file = Path("env.example") - - if env_file.exists(): - print("āœ“ .env file already exists") - response = input("Do you want to overwrite it? (y/N): ").lower() - if response != 'y': - return - - if example_file.exists(): - shutil.copy(example_file, env_file) - print("āœ“ Created .env file from template") - else: - print("āœ— env.example file not found") - return - - -def main() -> None: - """Main setup function.""" - print("Media Generation Example Setup") - print("=" * 40) - - # Check Python version - check_python_version() - - # Create virtual environment - create_virtual_environment() - - # Install requirements - install_requirements() - - # Setup environment file - setup_environment_file() - - print("\nāœ… Setup complete!") - print("\nNext steps:") - print("1. Edit the .env file with your actual API keys") - print("2. Activate the virtual environment:") - print(" source venv/bin/activate") - print("3. Run 'python example_usage.py' to test the setup") - print("\nRequired API keys:") - print("- OPENAI_API_KEY: For DALL-E image generation") - print("- REPLICATE_API_TOKEN: For various AI models") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/setup_uv.py b/setup_uv.py new file mode 100644 index 0000000..54775f6 --- /dev/null +++ b/setup_uv.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +""" +Setup script for Media Generation Example using uv. +Handles virtual environment creation and environment configuration. +""" +import shutil +import subprocess +import sys +from pathlib import Path + + +def run_command(cmd: list[str], check: bool = True) -> subprocess.CompletedProcess: + """Run a command and return the result.""" + print(f"Running: {' '.join(cmd)}") + return subprocess.run(cmd, check=check, capture_output=True, text=True) + + +def check_python_version() -> None: + """Check if Python version is compatible.""" + if sys.version_info < (3, 10): + print("Error: Python 3.10+ is required") + sys.exit(1) + version = f"{sys.version_info.major}.{sys.version_info.minor}" + print(f"āœ“ Python {version} detected") + + +def check_uv_installed() -> None: + """Check if uv is installed.""" + try: + run_command(["uv", "--version"]) + print("āœ“ uv is installed") + except (subprocess.CalledProcessError, FileNotFoundError): + print("āœ— uv is not installed") + print("Please install uv first:") + print(" curl -LsSf https://astral.sh/uv/install.sh | sh") + print(" # or with pip: pip install uv") + sys.exit(1) + + +def create_virtual_environment() -> None: + """Create virtual environment using uv.""" + venv_path = Path(".venv") + + if venv_path.exists(): + print("āœ“ Virtual environment already exists") + response = input("Do you want to recreate it? (y/N): ").lower() + if response == "y": + shutil.rmtree(venv_path) + else: + return + + print("Creating virtual environment with uv...") + run_command(["uv", "venv"]) + print("āœ“ Virtual environment created") + + +def install_dependencies() -> None: + """Install dependencies using uv.""" + print("Installing dependencies with uv...") + run_command(["uv", "sync"]) + print("āœ“ Dependencies installed") + + +def install_dev_dependencies() -> None: + """Install development dependencies.""" + print("Installing development dependencies...") + run_command(["uv", "sync", "--extra", "dev"]) + print("āœ“ Development dependencies installed") + + +def setup_environment_file() -> None: + """Set up the .env file from template.""" + env_file = Path(".env") + example_file = Path("env.example") + + if env_file.exists(): + print("āœ“ .env file already exists") + response = input("Do you want to overwrite it? (y/N): ").lower() + if response != "y": + return + + if example_file.exists(): + shutil.copy(example_file, env_file) + print("āœ“ Created .env file from template") + else: + print("āœ— env.example file not found") + return + + +def main() -> None: + """Main setup function.""" + print("Media Generation Example Setup (uv)") + print("=" * 40) + + # Check Python version + check_python_version() + + # Check uv installation + check_uv_installed() + + # Create virtual environment + create_virtual_environment() + + # Install dependencies + install_dependencies() + + # Install dev dependencies + install_dev_dependencies() + + # Setup environment file + setup_environment_file() + + print("\nāœ… Setup complete!") + print("\nNext steps:") + print("1. Edit the .env file with your actual API keys") + print("2. Activate the virtual environment:") + print(" source .venv/bin/activate") + print("3. Or use uv to run commands directly:") + print(" uv run python example_usage.py") + print("4. Run tests:") + print(" uv run pytest") + print("\nRequired API keys:") + print("- OPENAI_API_KEY: For DALL-E image generation") + print("- REPLICATE_API_TOKEN: For various AI models") + print("\nUseful uv commands:") + print("- uv run : Run a command in the virtual environment") + print("- uv add : Add a new dependency") + print("- uv add --dev : Add a development dependency") + print("- uv sync: Install/update dependencies") + print("- uv lock: Update lock file") + + +if __name__ == "__main__": + main() diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..c55ddcb --- /dev/null +++ b/uv.lock @@ -0,0 +1,1879 @@ +version = 1 +revision = 2 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.12' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "python_full_version == '3.11.*' and platform_machine == 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