ADDED: Text2image endpoint

README.md

@@ -114,6 +114,26 @@ percentage, missing keywords, and profile summary.
 - **Description:** Provides a simple web interface to interact with the smart ats.
 - **Try ATS:**  [Smart ATS](https://llm-pgc4.onrender.com/blog_generator_ui)
 
+### 19. Text to Image using Diffusion Models
+
+- **Route:** `/text2image`
+description: This route allows you to generate images using various diffusion models available on Hugging Face.
+
+    You can choose from the following models -
+
+    **DreamShaper v7**: A highly capable and versatile text-to-image model, suitable for a wide range of image generation tasks.
+
+    **Animagine XL**: A specialized model for generating high-quality anime-style images from text prompts.
+
+    **Stable Diffusion Base**: The base version of the popular Stable Diffusion model, suitable for general-purpose image generation.
+
+    **Stable Diffusion v2**: The latest version of Stable Diffusion, with improved performance and quality compared to the base version.
+
+    To generate an image, send a POST request to the `/text2image` endpoint with the desired model and prompt in the request body.
+
+    **Request Body:** `{ "model": "DreamShaper_v7", "prompt": "An astronaut riding a horse on the moon" }` The response will be the generated image in PNG format.
+
+
 ## Usage
 
 Each endpoint accepts specific parameters as described in the respective endpoint documentation. Users can make POST requests to these endpoints with the required parameters to perform the desired tasks.

api.py

@@ -1,14 +1,17 @@
 import os
 import uvicorn
-from fastapi import FastAPI,Form,File,UploadFile, Request
+import io
+import requests
+from PIL import Image
+from fastapi import FastAPI,Form,File,UploadFile, Request ,Response
 from fastapi.templating import Jinja2Templates
 from fastapi.encoders import jsonable_encoder
 from fastapi.responses import JSONResponse,RedirectResponse,StreamingResponse
 from typing import List,Optional
 from pydantic import BaseModel
 import google.generativeai as genai
 from fastapi.middleware.cors import CORSMiddleware
-from settings import invoice_prompt,youtube_transcribe_prompt,text2sql_prompt,EMPLOYEE_DB,GEMINI_PRO,GEMINI_PRO_1_5
+from settings import invoice_prompt,youtube_transcribe_prompt,text2sql_prompt,EMPLOYEE_DB,GEMINI_PRO,GEMINI_PRO_1_5, diffusion_models
 from mongo import MongoDB
 from helper_functions import get_qa_chain,get_gemini_response,get_url_doc_qa,extract_transcript_details,\
     get_gemini_response_health,get_gemini_pdf,read_sql_query,remove_substrings,questions_generator,groq_pdf,\
@@ -395,6 +398,43 @@ async def ats(resume_pdf: UploadFile = File(...),job_description: str = Form(...
     except Exception as e:
         return ResponseText(response=f"Error: {str(e)}")
 
+@app.post("/text2image",description=
+        """
+            This API provides access to the following diffusion models for generating images from text prompts.
+            
+            Models you can use for generating image are:
+
+            1. DreamShaper_v7 - A highly capable and versatile text-to-image model, suitable for a wide range of image generation tasks.
+
+            2. Animagine_xl - A specialized model for generating high-quality anime-style images from text prompts.
+
+            3. Stable_Diffusion_base - The base version of the popular Stable Diffusion model, suitable for general-purpose image generation.
+
+            4. Stable_Diffusion_v2 - The latest version of Stable Diffusion, with improved performance and quality compared to the base version.
+        """)
+def generate_image(prompt: str = Form("Astronaut riding a horse"), model: str = Form("DreamShaper_v7")):
+    try:
+        if model in diffusion_models:
+            def query(payload):
+                api_key = os.getenv("HUGGINGFACE_API_KEY")
+                headers = {"Authorization": f"Bearer {api_key}"}
+                response = requests.post(diffusion_models[model], headers=headers, json=payload)
+                return response.content
+
+            image_bytes = query({"inputs": prompt})
+            image = Image.open(io.BytesIO(image_bytes))
+            bytes_io = io.BytesIO()
+            image.save(bytes_io, format="PNG")
+            bytes_io.seek(0)
+            return Response(bytes_io.getvalue(), media_type="image/png")
+        else:
+            return ResponseText(response="Invalid model name")
+    # except requests.exceptions.RequestException as e:
+    #     print(f"Request Exception: {str(e)}")
+    #     return ResponseText(response="Busy server: Please try later")
+    except Exception as e:
+        return ResponseText(response="Busy server: Please try later")
+
 if __name__ == '__main__':
     import uvicorn
     uvicorn.run(app)

settings.py

@@ -73,4 +73,11 @@
 If the context is not helpful, please provide the original questions.
 QUESTIONS:
 """
-)
+)
+
+diffusion_models = {
+    "DreamShaper_v7"  : "https://api-inference.huggingface.co/models/SimianLuo/LCM_Dreamshaper_v7",
+    "Animagine_xl" : "https://api-inference.huggingface.co/models/cagliostrolab/animagine-xl-3.0",
+    "Stable_Diffusion_base" : "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0",
+    "Stable_Diffusion_v2" : "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1",
+}