Create main.py

main.py

@@ -0,0 +1,355 @@
+import os
+import asyncio
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import Optional, List
+import httpx
+from supabase import create_client, Client
+from datetime import datetime
+import uuid
+
+app = FastAPI(title="AI Team Chat API")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Replace with your frontend URL in production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ─────────────────────────────────────────────
+# ENV VARS (set in HuggingFace Space secrets)
+# ─────────────────────────────────────────────
+GROQ_API_KEY = os.getenv("GROQ_API_KEY", "")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
+SUPABASE_URL = os.getenv("SUPABASE_URL", "")
+SUPABASE_KEY = os.getenv("SUPABASE_KEY", "")
+
+# Supabase client (optional - graceful fallback if not configured)
+supabase: Optional[Client] = None
+if SUPABASE_URL and SUPABASE_KEY:
+    supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
+
+
+# ─────────────────────────────────────────────
+# MODELS
+# ─────────────────────────────────────────────
+class ChatRequest(BaseModel):
+    message: str
+    provider: str = "groq"           # "groq" or "openai"
+    religion: Optional[str] = None   # for Spiritual Coach
+    session_id: Optional[str] = None # for Supabase history
+    conversation_history: Optional[List[dict]] = []
+
+
+class AgentResponse(BaseModel):
+    agent: str
+    role: str
+    avatar: str
+    color: str
+    message: str
+
+
+class ChatResponse(BaseModel):
+    session_id: str
+    agent_responses: List[AgentResponse]
+    summary: str
+    question: str
+
+
+# ─────────────────────────────────────────────
+# LLM WRAPPER
+# ─────────────────────────────────────────────
+async def call_llm(provider: str, system_prompt: str, user_message: str, temperature: float = 0.7) -> str:
+    """
+    Unified LLM wrapper. Supports Groq and OpenAI.
+    """
+    if provider == "groq":
+        return await call_groq(system_prompt, user_message, temperature)
+    elif provider == "openai":
+        return await call_openai(system_prompt, user_message, temperature)
+    else:
+        raise HTTPException(status_code=400, detail=f"Unknown provider: {provider}")
+
+
+async def call_groq(system_prompt: str, user_message: str, temperature: float) -> str:
+    if not GROQ_API_KEY:
+        raise HTTPException(status_code=500, detail="GROQ_API_KEY not set")
+
+    async with httpx.AsyncClient(timeout=30) as client:
+        response = await client.post(
+            "https://api.groq.com/openai/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {GROQ_API_KEY}",
+                "Content-Type": "application/json",
+            },
+            json={
+                "model": "llama-3.3-70b-versatile",
+                "messages": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_message},
+                ],
+                "temperature": temperature,
+                "max_tokens": 300,
+            },
+        )
+        response.raise_for_status()
+        data = response.json()
+        return data["choices"][0]["message"]["content"].strip()
+
+
+async def call_openai(system_prompt: str, user_message: str, temperature: float) -> str:
+    if not OPENAI_API_KEY:
+        raise HTTPException(status_code=500, detail="OPENAI_API_KEY not set")
+
+    async with httpx.AsyncClient(timeout=30) as client:
+        response = await client.post(
+            "https://api.openai.com/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {OPENAI_API_KEY}",
+                "Content-Type": "application/json",
+            },
+            json={
+                "model": "gpt-4o-mini",
+                "messages": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_message},
+                ],
+                "temperature": temperature,
+                "max_tokens": 300,
+            },
+        )
+        response.raise_for_status()
+        data = response.json()
+        return data["choices"][0]["message"]["content"].strip()
+
+
+# ─────────────────────────────────────────────
+# AGENT DEFINITIONS
+# ───────────────────────��─────────────────────
+def get_agents(religion: Optional[str]) -> List[dict]:
+    spiritual_note = (
+        f"Base your guidance on {religion} principles and teachings. Be respectful and calm."
+        if religion and religion.lower() not in ["none", "prefer not to say", ""]
+        else "Provide neutral mindfulness and universal spiritual guidance. Avoid referencing any specific religion."
+    )
+
+    return [
+        {
+            "name": "Dr. Sarah",
+            "role": "Doctor",
+            "avatar": "🩺",
+            "color": "#4FC3F7",
+            "system_prompt": (
+                "You are Dr. Sarah, a careful and responsible medical advisor. "
+                "You DO NOT give diagnoses. You suggest possibilities carefully and always recommend "
+                "consulting a licensed physician for personal medical decisions. "
+                "Be concise, warm, and professional. Respond in 2-4 lines maximum. "
+                "Do not repeat what other experts would say."
+            ),
+        },
+        {
+            "name": "Coach Marcus",
+            "role": "Fitness Coach",
+            "avatar": "💪",
+            "color": "#81C784",
+            "system_prompt": (
+                "You are Coach Marcus, an energetic and experienced fitness coach. "
+                "You focus on physical activity, movement, exercise routines, and safe training. "
+                "Be motivating, practical, and concise. Respond in 2-4 lines maximum. "
+                "Do not repeat what other experts would say."
+            ),
+        },
+        {
+            "name": "Nina",
+            "role": "Nutritionist",
+            "avatar": "🥗",
+            "color": "#FFB74D",
+            "system_prompt": (
+                "You are Nina, a certified nutritionist specializing in diet, energy, and food science. "
+                "You focus on practical, evidence-based dietary guidance. "
+                "Be specific, helpful, and concise. Respond in 2-4 lines maximum. "
+                "Do not repeat what other experts would say."
+            ),
+        },
+        {
+            "name": "Dr. Mia",
+            "role": "Mental Health Coach",
+            "avatar": "🧠",
+            "color": "#CE93D8",
+            "system_prompt": (
+                "You are Dr. Mia, an empathetic and supportive mental health coach. "
+                "You help with emotional wellbeing, stress, mindset, and psychological patterns. "
+                "Be compassionate, grounding, and concise. Respond in 2-4 lines maximum. "
+                "Do not repeat what other experts would say."
+            ),
+        },
+        {
+            "name": "Sage Aris",
+            "role": "Spiritual Coach",
+            "avatar": "✨",
+            "color": "#F48FB1",
+            "system_prompt": (
+                f"You are Sage Aris, a gentle and insightful spiritual coach. "
+                f"{spiritual_note} "
+                f"Be calm, respectful, and uplifting. Respond in 2-4 lines maximum. "
+                f"Do not repeat what other experts would say."
+            ),
+        },
+    ]
+
+
+COORDINATOR_SYSTEM_PROMPT = """You are the Coordinator of an expert AI wellness team consisting of a Doctor, Fitness Coach, Nutritionist, Mental Health Coach, and Spiritual Coach.
+
+Your job:
+1. Read all agent responses carefully
+2. Write a SHORT summary of the key collective insights (2-3 sentences max)
+3. Ask ONE clear, thoughtful, combined question to the user to gather more context
+
+Rules:
+- Do NOT repeat the agents' responses verbatim
+- Keep it collaborative and warm
+- The question should help the team give better advice next time
+
+Output format (strictly follow this):
+Summary: <your short summary here>
+Question: <your single question here>"""
+
+
+# ─────────────────────────────────────────────
+# SUPABASE HELPERS
+# ─────────────────────────────────────────────
+async def save_to_supabase(session_id: str, user_message: str, agent_responses: list, summary: str, question: str):
+    if not supabase:
+        return
+
+    try:
+        record = {
+            "session_id": session_id,
+            "user_message": user_message,
+            "agent_responses": agent_responses,
+            "summary": summary,
+            "question": question,
+            "created_at": datetime.utcnow().isoformat(),
+        }
+        supabase.table("chat_history").insert(record).execute()
+    except Exception as e:
+        print(f"Supabase save error: {e}")
+
+
+async def get_session_history(session_id: str) -> list:
+    if not supabase or not session_id:
+        return []
+
+    try:
+        result = (
+            supabase.table("chat_history")
+            .select("*")
+            .eq("session_id", session_id)
+            .order("created_at", desc=False)
+            .limit(20)
+            .execute()
+        )
+        return result.data or []
+    except Exception as e:
+        print(f"Supabase fetch error: {e}")
+        return []
+
+
+# ─────────────────────────────────────────────
+# MAIN ENDPOINT
+# ─────────────────────────────────────────────
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    session_id = request.session_id or str(uuid.uuid4())
+    agents = get_agents(request.religion)
+
+    # Build context from conversation history
+    history_context = ""
+    if request.conversation_history:
+        history_lines = []
+        for turn in request.conversation_history[-6:]:  # last 3 exchanges
+            history_lines.append(f"User: {turn.get('user', '')}")
+            if turn.get("question"):
+                history_lines.append(f"Team Question: {turn.get('question', '')}")
+        history_context = "\n\nPrevious conversation context:\n" + "\n".join(history_lines)
+
+    user_prompt = f"{history_context}\n\nUser's current message: {request.message}"
+
+    # ── Step 1: Run all 5 agents in parallel ──
+    async def run_agent(agent: dict) -> AgentResponse:
+        message = await call_llm(
+            provider=request.provider,
+            system_prompt=agent["system_prompt"],
+            user_message=user_prompt,
+        )
+        return AgentResponse(
+            agent=agent["name"],
+            role=agent["role"],
+            avatar=agent["avatar"],
+            color=agent["color"],
+            message=message,
+        )
+
+    agent_results: List[AgentResponse] = await asyncio.gather(*[run_agent(a) for a in agents])
+
+    # ── Step 2: Run Coordinator ──
+    all_responses_text = "\n\n".join(
+        [f"[{r.role} — {r.agent}]:\n{r.message}" for r in agent_results]
+    )
+    coordinator_user_prompt = (
+        f"User asked: \"{request.message}\"\n\n"
+        f"Agent responses:\n{all_responses_text}"
+    )
+
+    coordinator_raw = await call_llm(
+        provider=request.provider,
+        system_prompt=COORDINATOR_SYSTEM_PROMPT,
+        user_message=coordinator_user_prompt,
+        temperature=0.5,
+    )
+
+    # Parse coordinator output
+    summary = ""
+    question = ""
+    for line in coordinator_raw.splitlines():
+        if line.lower().startswith("summary:"):
+            summary = line[len("summary:"):].strip()
+        elif line.lower().startswith("question:"):
+            question = line[len("question:"):].strip()
+
+    if not summary:
+        summary = coordinator_raw
+    if not question:
+        question = "Can you share more details so the team can help you better?"
+
+    # ── Step 3: Save to Supabase ──
+    agent_data = [r.dict() for r in agent_results]
+    asyncio.create_task(
+        save_to_supabase(session_id, request.message, agent_data, summary, question)
+    )
+
+    return ChatResponse(
+        session_id=session_id,
+        agent_responses=agent_results,
+        summary=summary,
+        question=question,
+    )
+
+
+@app.get("/history/{session_id}")
+async def get_history(session_id: str):
+    history = await get_session_history(session_id)
+    return {"session_id": session_id, "history": history}
+
+
+@app.get("/health")
+async def health():
+    return {
+        "status": "ok",
+        "groq_configured": bool(GROQ_API_KEY),
+        "openai_configured": bool(OPENAI_API_KEY),
+        "supabase_configured": bool(supabase),
+    }