main.py

# main.py
from fastapi import FastAPI, HTTPException, Header
from fastapi.middleware.cors import CORSMiddleware
import requests
import os
from pydantic import BaseModel

app = FastAPI()

# Enable CORS for all origins
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allows all origins
    allow_credentials=True,
    allow_methods=["*"],  # Allows all methods
    allow_headers=["*"],  # Allows all headers
)

# Load environment variables
LLM_API_URL = os.getenv("LLM_API_URL", "https://pvanand-audio-chat.hf.space/llm-agent")
API_KEY = os.getenv("X_API_KEY")

# Pydantic model for request validation
class LLMChatRequest(BaseModel):
    prompt: str
    system_message: str = ""
    model_id: str = "openai/gpt-4o-mini"
    conversation_id: str = "string"
    user_id: str = "string"

@app.post("/llm-chat")
async def llm_chat(
    request: LLMChatRequest,
    x_api_key: str = Header(None)
):
    if x_api_key != API_KEY:
        raise HTTPException(status_code=403, detail="Invalid API Key")

    payload = {
        "prompt": request.prompt,
        "system_message": request.system_message,
        "model_id": request.model_id,
        "conversation_id": request.conversation_id,
        "user_id": request.user_id
    }

    headers = {
        "accept": "application/json",
        "X-API-Key": x_api_key,
        "Content-Type": "application/json"
    }

    # Use requests to call the external API
    response = requests.post(LLM_API_URL, json=payload, headers=headers)
    if response.status_code != 200:
        raise HTTPException(status_code=response.status_code, detail="Error from LLM API")

    return response.json()

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)