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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lata-11/GHCI_HACKATHON/blob/main/Poster_generation_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This notebook is a submission for the GHCI 2024 hackathon, which aims to create a solution that can be integrated with generative AI\n",
        "# for environmental sustainability and creative AI innovations. It showcases an AI-powered tool for generating environmental campaign\n",
        "# posters using Gradio. The tool leverages the Stable Diffusion model to create images based on user-defined parameters, such as region, language, and\n",
        "# specific environmental issues. Users can add custom slogans with color-coded text based on keywords. The interface simplifies the process of\n",
        "# creating visually impactful posters, promoting awareness for sustainability initiatives.\n",
        "\n",
        "# Author: Lata\n",
        "# Date: November 2, 2024"
      ],
      "metadata": {
        "id": "-U8jEEYYjxfz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install required libraries for text-to-image generation and model processing"
      ],
      "metadata": {
        "id": "9ajNjnGUhUDV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install transformers diffusers torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IXNkQFpyIwoZ",
        "outputId": "0382b80c-0045-48f9-f18f-bd872d0b7f9b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
            "Requirement already satisfied: diffusers in /usr/local/lib/python3.10/dist-packages (0.30.3)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.0+cu121)\n",
            "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
            "\u001b[0mTraceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 3070, in _dep_map\n",
            "    return self.__dep_map\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 2863, in __getattr__\n",
            "    raise AttributeError(attr)\n",
            "AttributeError: _DistInfoDistribution__dep_map\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 179, in exc_logging_wrapper\n",
            "    status = run_func(*args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/req_command.py\", line 67, in wrapper\n",
            "    return func(self, options, args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/commands/install.py\", line 377, in run\n",
            "    requirement_set = resolver.resolve(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/resolver.py\", line 95, in resolve\n",
            "    result = self._result = resolver.resolve(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 546, in resolve\n",
            "    state = resolution.resolve(requirements, max_rounds=max_rounds)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 427, in resolve\n",
            "    failure_causes = self._attempt_to_pin_criterion(name)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 239, in _attempt_to_pin_criterion\n",
            "    criteria = self._get_updated_criteria(candidate)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 229, in _get_updated_criteria\n",
            "    for requirement in self._p.get_dependencies(candidate=candidate):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/provider.py\", line 247, in get_dependencies\n",
            "    return [r for r in candidate.iter_dependencies(with_requires) if r is not None]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/provider.py\", line 247, in <listcomp>\n",
            "    return [r for r in candidate.iter_dependencies(with_requires) if r is not None]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py\", line 401, in iter_dependencies\n",
            "    for r in self.dist.iter_dependencies():\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/metadata/pkg_resources.py\", line 247, in iter_dependencies\n",
            "    return self._dist.requires(extras)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 2786, in requires\n",
            "    dm = self._dep_map\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 3072, in _dep_map\n",
            "    self.__dep_map = self._compute_dependencies()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 3082, in _compute_dependencies\n",
            "    reqs.extend(parse_requirements(req))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 3135, in __init__\n",
            "    super().__init__(requirement_string)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/packaging/requirements.py\", line 36, in __init__\n",
            "    parsed = _parse_requirement(requirement_string)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/packaging/_parser.py\", line 62, in parse_requirement\n",
            "    return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/packaging/_tokenizer.py\", line 104, in __init__\n",
            "    self.rules: dict[str, re.Pattern[str]] = {\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/packaging/_tokenizer.py\", line 105, in <dictcomp>\n",
            "    name: re.compile(pattern) for name, pattern in rules.items()\n",
            "  File \"/usr/lib/python3.10/re.py\", line 251, in compile\n",
            "    return _compile(pattern, flags)\n",
            "  File \"/usr/lib/python3.10/re.py\", line 288, in _compile\n",
            "    def _compile(pattern, flags):\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/pip3\", line 8, in <module>\n",
            "    sys.exit(main())\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/main.py\", line 80, in main\n",
            "    return command.main(cmd_args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 100, in main\n",
            "    return self._main(args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 232, in _main\n",
            "    return run(options, args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 215, in exc_logging_wrapper\n",
            "    logger.critical(\"Operation cancelled by user\")\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1524, in critical\n",
            "    self._log(CRITICAL, msg, args, **kwargs)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1624, in _log\n",
            "    self.handle(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1634, in handle\n",
            "    self.callHandlers(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1696, in callHandlers\n",
            "    hdlr.handle(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 968, in handle\n",
            "    self.emit(record)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/utils/logging.py\", line 177, in emit\n",
            "    self.console.print(renderable, overflow=\"ignore\", crop=False, style=style)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/rich/console.py\", line 1673, in print\n",
            "    with self:\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/rich/console.py\", line 865, in __exit__\n",
            "    self._exit_buffer()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/rich/console.py\", line 823, in _exit_buffer\n",
            "    self._check_buffer()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/rich/console.py\", line 2060, in _check_buffer\n",
            "    self.file.write(text)\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set up and authenticate the Stable Diffusion model for campaign poster generation."
      ],
      "metadata": {
        "id": "P1HC-G6RiRRM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import StableDiffusionPipeline\n",
        "import torch\n",
        "from huggingface_hub import login\n",
        "\n",
        "# Log in with your token\n",
        "login(\"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\")\n",
        "\n",
        "model_id = \"lata11/campaign-poster\"  # Use model path here\n",
        "pipeline = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True).to(\"cuda\")\n"
      ],
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        "id": "Ev-jjSF0mvp_",
        "outputId": "ed54e6df-fef0-4731-bc28-917170d2f563"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
            "Token is valid (permission: fineGrained).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Keyword arguments {'use_auth_token': True} are not expected by StableDiffusionPipeline and will be ignored.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the CLIP model and processor in half-precision mode for efficient memory usage.\n",
        "\n"
      ],
      "metadata": {
        "id": "O4OKsu_sif7f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import CLIPProcessor, CLIPModel\n",
        "from diffusers import StableDiffusionPipeline\n",
        "import torch\n",
        "from PIL import Image, ImageDraw, ImageFont\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Load CLIP model with offloading for reduced memory usage\n",
        "clip_model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\").to(\"cuda\")\n",
        "clip_model = clip_model.half()  # Half precision to save memory\n",
        "clip_processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n"
      ],
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        "outputId": "fbbebdd3-cd78-4b7e-8092-c7e7a9da5094"
      },
      "execution_count": null,
      "outputs": [
        {
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        },
        {
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          "metadata": {}
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            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "623e3001dd104f7cb5821a19af430614"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install the Gradio package to build and run an interactive web interface for the project"
      ],
      "metadata": {
        "id": "3sGpxcDAijef"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gradio # Install the gradio package"
      ],
      "metadata": {
        "id": "Kw6bituCRMuB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6387d78b-f3e8-4db8-b929-842055590b7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "huggingface_hub"
                ]
              },
              "id": "6ea33ff192e64d9087e3a2a3c6aa0cb1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Custom CSS for styling"
      ],
      "metadata": {
        "id": "xLYv2pw_iwHH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Custom CSS for styling\n",
        "custom_css = \"\"\"\n",
        ".gradio-container {\n",
        "    background-color: #e9f5e9;  /* Light green background for the main container */\n",
        "    font-family: 'Helvetica Neue', Arial, sans-serif;\n",
        "}\n",
        "\n",
        "h1 {\n",
        "    color: #2e7d32;  /* Dark green for the title */\n",
        "    text-align: center;\n",
        "    margin-bottom: 20px;\n",
        "}\n",
        "\n",
        "h2 {\n",
        "    color: #388e3c;  /* Medium green for the subtitle */\n",
        "    text-align: center;\n",
        "    margin-bottom: 30px;\n",
        "    font-weight: 600;  /* Bold font */\n",
        "    font-size: 1.5em;  /* Larger size */\n",
        "}\n",
        "\n",
        ".input_label {\n",
        "    font-weight: bold;\n",
        "    color: #388e3c;  /* Medium green for labels */\n",
        "}\n",
        "\n",
        ".input_textbox {\n",
        "    border: 1px solid #388e3c;  /* Medium green border */\n",
        "    border-radius: 5px;\n",
        "    padding: 10px;\n",
        "    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);\n",
        "}\n",
        "\n",
        ".output_image {\n",
        "    border: 2px solid #388e3c;  /* Medium green border */\n",
        "    border-radius: 10px;\n",
        "    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);\n",
        "    background-color: #f5f5dc;  /* Beige background for the output image area */\n",
        "}\n",
        "\n",
        "button {\n",
        "    background-color: #4caf50;  /* Green button background */\n",
        "    color: white;\n",
        "    border: none;\n",
        "    border-radius: 5px;\n",
        "    padding: 10px 15px;\n",
        "    cursor: pointer;\n",
        "    transition: background-color 0.3s ease;\n",
        "}\n",
        "\n",
        "button:hover {\n",
        "    background-color: #45a049;  /* Darker green on hover */\n",
        "}\n",
        "\n",
        "/* Adding style to the description */\n",
        ".description {\n",
        "    color: #2e7d32;  /* Dark green for description text */\n",
        "    text-align: center;\n",
        "    font-size: 1.2em;\n",
        "    margin-bottom: 20px;\n",
        "    font-weight: 500;  /* Semi-bold font */\n",
        "}\n",
        "\n",
        "/* Ensure beige background for output images */\n",
        ".output_image img {\n",
        "    background-color: #f5f5dc;  /* Beige background for images */\n",
        "}\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "G2h4_9BJ4LHm"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creates an AI-powered Gradio interface for generating customizable environmental campaign posters based on user inputs."
      ],
      "metadata": {
        "id": "-D6tqHLHjCJJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import torch\n",
        "import cv2\n",
        "\n",
        "# Function to add text to the image\n",
        "def add_text(image, text, position, font_scale, color, thickness=2, line_type=cv2.LINE_AA):\n",
        "    # Get the width and height of the text to be added\n",
        "    (text_width, text_height), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_TRIPLEX, font_scale, thickness)\n",
        "    text_x = int(position[0] - text_width / 2)\n",
        "    text_y = int(position[1] + text_height / 2)\n",
        "\n",
        "    # Add text to the image\n",
        "    cv2.putText(image, text, (text_x, text_y), cv2.FONT_HERSHEY_TRIPLEX, font_scale, color, thickness, line_type)\n",
        "\n",
        "# Function to determine color based on keywords in the slogan\n",
        "def determine_color(slogan):\n",
        "    slogan = slogan.lower()\n",
        "\n",
        "    # Expanded keyword lists for each color with common words\n",
        "    light_green_keywords = ['green', 'plant', 'leaf', 'nature', 'garden', 'tree', 'grass', 'grow', 'fresh']\n",
        "    light_blue_keywords = ['water', 'ocean', 'sea', 'river', 'lake', 'fish', 'clean', 'blue', 'drinking']\n",
        "    light_brown_keywords = ['soil', 'waste', 'earth', 'dirt', 'garbage', 'trash', 'compost', 'field', 'farm']\n",
        "\n",
        "    # Check for keywords\n",
        "    for keyword in light_green_keywords:\n",
        "        if keyword in slogan:\n",
        "            return (144, 238, 144)  # Light green\n",
        "    for keyword in light_blue_keywords:\n",
        "        if keyword in slogan:\n",
        "            return (173, 216, 230)  # Light blue\n",
        "    for keyword in light_brown_keywords:\n",
        "        if keyword in slogan:\n",
        "            return (210, 180, 140)  # Light brown\n",
        "\n",
        "    return (255, 255, 255)  # Default to white if no keywords are found\n",
        "\n",
        "\n",
        "\n",
        "# Function to overlay slogan on the best image\n",
        "def overlay_slogan_on_best_image(best_image, slogan, size):\n",
        "    image_height, image_width = best_image.shape[:2]\n",
        "\n",
        "    # Determine color based on the slogan\n",
        "    band_color = determine_color(slogan)\n",
        "\n",
        "    # Create a light color band at the bottom of the image\n",
        "    band_height = int(image_height * 0.1)  # Height of the color band\n",
        "    band = np.full((band_height, image_width, 3), band_color, dtype=np.uint8)\n",
        "\n",
        "    # Overlay the color band onto the image\n",
        "    best_image[-band_height:, :] = band\n",
        "\n",
        "    # Set font scale based on the size of the poster\n",
        "    if size == \"Small\":\n",
        "        font_scale = 0.5  # Smaller font for small posters\n",
        "    elif size == \"Medium\":\n",
        "        font_scale = 1  # Medium font for medium posters\n",
        "    elif size == \"Large\":\n",
        "        font_scale = 1.5  # Larger font for large posters\n",
        "\n",
        "    thickness = 2\n",
        "    # Calculate text position at the bottom of the image\n",
        "    slogan_position = (image_width // 2, image_height - band_height // 2)\n",
        "\n",
        "    # Text color is set to black for better visibility\n",
        "    text_color = (0, 0, 0)  # Black color for the text\n",
        "\n",
        "    # Add slogan text to the image\n",
        "    add_text(best_image, slogan, slogan_position, font_scale, text_color, thickness=thickness)\n",
        "\n",
        "    return best_image\n",
        "\n",
        "# Function to generate the poster\n",
        "def generate_poster(region, language, issue, additional_details, size, slogan):\n",
        "    prompt = (\n",
        "        f\"A bold, propaganda-style poster for environmental conservation in the {region} community, in {language}. \"\n",
        "        f\"Focus on culturally resonant imagery and vibrant colors to highlight the urgency of {issue}, using only visuals and design elements, people living in the {region}. \"\n",
        "        f\"Create a clean, impactful image with no text or letters, capturing the spirit of environmental protection also {additional_details}.\"\n",
        "    )\n",
        "\n",
        "    # Set dimensions based on user input\n",
        "    if size == \"Small\":\n",
        "        input_height, input_width = 480, 320\n",
        "    elif size == \"Medium\":\n",
        "        input_height, input_width = 768, 512\n",
        "    elif size == \"Large\":\n",
        "        input_height, input_width = 1080, 720\n",
        "\n",
        "    # Generate multiple images for selection\n",
        "    generated_images = []\n",
        "    num_images = 3\n",
        "\n",
        "    for _ in range(num_images):\n",
        "        # Assuming pipeline is defined and can generate images from the prompt\n",
        "        image = pipeline(prompt=prompt, height=input_height, width=input_width).images[0]\n",
        "        generated_images.append(image)\n",
        "\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    clip_model.to(device)\n",
        "    image_scores = []\n",
        "\n",
        "    for image in generated_images:\n",
        "        # Ensure the image is in the correct format\n",
        "        if isinstance(image, Image.Image):\n",
        "            image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)\n",
        "        else:\n",
        "            raise ValueError(\"Generated image is not in the correct format.\")\n",
        "\n",
        "        # Truncate the prompt to fit the model's maximum sequence length\n",
        "        inputs = clip_processor(text=[prompt[:77]], images=image_cv, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "        with torch.no_grad():\n",
        "            outputs = clip_model(**inputs.to(device))\n",
        "        logits_per_image = outputs.logits_per_image\n",
        "        similarity = logits_per_image.item()\n",
        "        image_scores.append((image_cv, similarity))\n",
        "\n",
        "    # Select best matching image\n",
        "    best_image, best_score = max(image_scores, key=lambda x: x[1])\n",
        "\n",
        "    # Overlay the slogan on the best image\n",
        "    best_image = overlay_slogan_on_best_image(best_image, slogan, size)\n",
        "\n",
        "    # Convert the image back to PIL format for Gradio\n",
        "    best_image_pil = Image.fromarray(cv2.cvtColor(best_image, cv2.COLOR_BGR2RGB))\n",
        "\n",
        "    return best_image_pil  # Return the best image with the overlay\n",
        "\n",
        "\n",
        "# Create the Gradio interface\n",
        "iface = gr.Interface(\n",
        "    fn=generate_poster,\n",
        "    inputs=[\n",
        "        gr.Textbox(label=\"Region\", placeholder=\"e.g. Maharashtra, India\", lines=1),\n",
        "        gr.Textbox(label=\"Language\", placeholder=\"e.g. English\", lines=1),\n",
        "        gr.Textbox(label=\"Issue\", placeholder=\"e.g. Deforestation\", lines=1),\n",
        "        gr.Textbox(label=\"Additional Details\", placeholder=\"e.g. poster look realistic\", lines=2),\n",
        "        gr.Radio(label=\"Poster Size\", choices=[\"Small\", \"Medium\", \"Large\"], type=\"value\", value=\"Medium\"),\n",
        "        gr.Textbox(label=\"Slogan\", placeholder=\"Enter your slogan here\", lines=1)\n",
        "    ],\n",
        "    outputs=gr.Image(type=\"pil\"),\n",
        "    title=\"🌿 Campaign Poster Generator\",\n",
        "    description=\"✊ Create visually stunning campaign posters to raise awareness on various issues and fight one step towards sustainability without hassle of writing long prompts! 🙋\",\n",
        "    theme=\"default\",\n",
        "    css=custom_css  # Inject custom CSS\n",
        ")\n",
        "\n",
        "# Launch the Gradio interface\n",
        "iface.launch()\n"
      ],
      "metadata": {
        "id": "WoHenYhcRVWe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "de1570b1-8225-4dad-bd05-8ff5cd90eafb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "* Running on public URL: https://983545e70c3c4090ca.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://983545e70c3c4090ca.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    }
  ]
}