from fastapi import FastAPI, HTTPException, Depends from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel from typing import List, Dict, Optional from supabase import create_client, Client import os from dotenv import load_dotenv from llm import LLMPipeline from chat import GeminiChat load_dotenv() app = FastAPI() # CORS middleware app.add_middleware( CORSMiddleware, allow_origins=["*"], # In production, replace with your frontend URL allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) @app.get("/health") async def health_check(): """Health check endpoint""" return {"status": "healthy", "version": "1.0.0"} # Initialize Supabase client supabase_url = os.getenv("SUPABASE_URL") supabase_key = os.getenv("SUPABASE_SERVICE_KEY") if not supabase_url or not supabase_key: raise ValueError("Supabase environment variables not set") supabase: Client = create_client(supabase_url, supabase_key) # Initialize AI models llm = LLMPipeline() gemini = GeminiChat() class Message(BaseModel): role: str content: str class ChatRequest(BaseModel): messages: List[Message] use_gemini: bool = True temperature: float = 0.7 @app.post("/api/chat") async def chat(request: ChatRequest): try: if request.use_gemini: # Use Gemini for interactive chat response = await gemini.chat( [{"role": m.role, "content": m.content} for m in request.messages], temperature=request.temperature ) else: # Use local LLM for specific tasks last_message = request.messages[-1].content response = await llm.generate(last_message) # Store chat history in Supabase supabase.table("chat_history").insert({ "messages": [m.dict() for m in request.messages], "response": response, "model": "gemini" if request.use_gemini else "local" }).execute() return {"response": response} 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)