main.py

"""GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment variables.")
genai.configure(api_key=GOOGLE_API_KEY)"""
from fastapi import FastAPI, File, UploadFile, Form, HTTPException
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
import google.generativeai as genai
import time
import json
import logging
import tempfile
import os
import traceback

GOOGLE_API_KEY = 'AIzaSyAxk5HFIz1DmxzW0NnvIJCppjQf9B9eawE'
genai.configure(api_key=GOOGLE_API_KEY)

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

generation_config = {
    "temperature": 1,
    "top_p": 0.95,
    "top_k": 64,
    "max_output_tokens": 8192,
    "response_mime_type": "application/json",
}

model = genai.GenerativeModel(
    model_name="gemini-1.5-pro-exp-0827",
    generation_config=generation_config,
)

logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')


@app.post("/process_video/")
async def process_video(
        video_file: UploadFile = File(...),
        prompt: str = Form(
            "Analyze the video and provide a JSON report with workout exercises minimum 5 (name of exercise, sets, reps), facial emotions minimum 10 (emotion, timestamp), voice emotions minimum 8 (emotion, timestamp), and nutrition plan (meal, time, food)")
):
    temp_video_path = None
    try:
        logging.info(f"Received video file: {video_file.filename}")

        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video:
            content = await video_file.read()
            temp_video.write(content)
            temp_video_path = temp_video.name

        logging.info(f"Temporary video file created: {temp_video_path}")

        genai_video_file = genai.upload_file(temp_video_path)

        logging.info(f"Completed upload to Google AI: {genai_video_file.uri}")

        timeout = 600
        start_time = time.time()
        while genai_video_file.state.name == "PROCESSING" and (time.time() - start_time) < timeout:
            logging.info(f"Processing... Time elapsed: {time.time() - start_time:.2f}s")
            time.sleep(10)
            genai_video_file = genai.get_file(genai_video_file.name)

        if genai_video_file.state.name == "FAILED":
            raise ValueError(f"Video processing failed: {genai_video_file.state.name}")
        elif genai_video_file.state.name == "PROCESSING":
            raise TimeoutError("Video processing timed out")

        logging.info("Making LLM inference request...")
        response = model.generate_content([prompt, genai_video_file],
                                          request_options={"timeout": 600})

        logging.info("LLM response received. Parsing JSON...")
        data = json.loads(response.text)
        logging.info(f"Parsed JSON data: {json.dumps(data, indent=2)}")

        os.unlink(temp_video_path)
        logging.info(f"Temporary video file deleted: {temp_video_path}")

        return JSONResponse(content=data)

    except Exception as e:
        logging.error(f"Error occurred: {str(e)}")
        logging.error(traceback.format_exc())

        if temp_video_path and os.path.exists(temp_video_path):
            os.unlink(temp_video_path)
            logging.info(f"Temporary video file deleted due to error: {temp_video_path}")

        raise HTTPException(status_code=500, detail=str(e))