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| from fastapi import FastAPI, File, UploadFile, HTTPException | |
| from fastapi.responses import JSONResponse | |
| from typing import Any | |
| import google.generativeai as genai | |
| #from dotenv import load_dotenv | |
| import io | |
| from PIL import Image | |
| import os | |
| app = FastAPI() | |
| API_KEY=os.getenv('GEMINI_API_KEY') | |
| genai.configure(api_key=API_KEY) | |
| def get_gemini_response(image: bytes) -> Any: | |
| image_stream = io.BytesIO(image) | |
| image = Image.open(image_stream) | |
| model = genai.GenerativeModel('gemini-1.5-flash', | |
| generation_config={"response_mime_type": "application/json"}) | |
| inputtext = '''System: You are a dietitian, Please check below image and share calorific value of each dish in metric system. Also explain how | |
| much you should eat at one time for healthy diet. Response should be as per below json format for each dish separately. | |
| {"Dish_Name": , | |
| "calorific_value": , | |
| "Healthy serving_size": }. | |
| If unable to identify the dish then respond with {"Dish_Name": Unable to identify the dish}.''' | |
| response = model.generate_content([inputtext, image]) | |
| return response.text | |
| async def analyze_dish(image: UploadFile = File(...)): | |
| try: | |
| image_bytes = await image.read() | |
| result = get_gemini_response(image_bytes) | |
| return JSONResponse(content=result) | |
| except Exception as e: | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| if __name__ == "__main__": | |
| import uvicorn | |
| uvicorn.run(app, host="0.0.0.0", port=7860) |