app.py

import os
import json
import uuid
import httpx
import gradio as gr
from fastapi import FastAPI, HTTPException, Request
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import uvicorn
import asyncio

# ✅ Securely Load Hugging Face Token
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    raise ValueError("❌ HF_TOKEN not found! Set it in Hugging Face Secrets.")

# ✅ Load Model Configuration
MODEL_NAME = "hpyapali/tinyllama-workout"
event_store = {}  # Store AI responses with event_id

app = FastAPI()

# ✅ Log server restart
print("🔄 Restarting Hugging Face AI Model Server...")

# ✅ Load AI Model
try:
    print("🔄 Loading AI Model...")
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)
    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, token=HF_TOKEN)
    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
    print("✅ AI Model Loaded Successfully!")
except Exception as e:
    print(f"❌ Error loading model: {e}")
    pipe = None


# ✅ AI Function - Analyzes workout data
def analyze_workouts(last_workouts: str):
    """Generates AI-based workout rankings based on heart rate recovery."""
    if pipe is None:
        return "❌ AI model is not loaded."

    if not last_workouts.strip():
        return "❌ No workout data provided."

    instruction = (
        "You are a fitness AI assistant. Rank the following workouts based on heart rate recovery after 2 minutes."
        "\n\n### Ranking Rules:"
        "\n- A **larger heart rate dip** indicates better recovery."
        "\n- If two workouts have the same HR dip, **rank by highest peak HR**."
        "\n\n### Workouts Data:\n"
        f"{last_workouts}"
        "\n\n### Output Format (Rank from best to worst, no explanation, just rankings):"
        "\n1. Best: Running - HR dip: 28 bpm"
        "\n2. Cycling - HR dip: 25 bpm"
        "\n3. Rowing - HR dip: 22 bpm"
        "\n4. Strength Training - HR dip: 18 bpm"
        "\n5. Walking - HR dip: 12 bpm"
        "\n6. Yoga - HR dip: 8 bpm"
    )

    try:
        result = pipe(
            instruction,
            max_new_tokens=250,
            temperature=0.3,
            top_p=0.9,
            do_sample=True,
            return_full_text=False
        )

        if not result or not result[0].get("generated_text", "").strip():
            return "❌ AI did not generate a valid response."

        return result[0]["generated_text"].strip()

    except Exception as e:
        return f"❌ Error generating workout recommendation: {str(e)}"


# ✅ API Route for Processing Workout Data
@app.post("/gradio_api/call/predict")
async def process_workout_request(request: Request):
    try:
        req_body = await request.json()
        print("📩 RAW REQUEST FROM HF:", req_body)  

        if "data" not in req_body or not isinstance(req_body["data"], list):
            raise HTTPException(status_code=400, detail="Invalid request format: 'data' must be a list.")

        last_workouts = req_body["data"][0]
        event_id = str(uuid.uuid4())  

        print(f"✅ Processing AI Request - Event ID: {event_id}")

        response_text = analyze_workouts(last_workouts)

        event_store[event_id] = response_text  

        webhook_url = req_body.get("webhook_url")
        if webhook_url:
            print(f"📡 Sending response to Webhook: {webhook_url}")
            async with httpx.AsyncClient() as client:
                await client.post(webhook_url, json={"event_id": event_id, "data": [response_text]})

        return {"event_id": event_id}  

    except Exception as e:
        print(f"❌ Error processing request: {e}")
        raise HTTPException(status_code=500, detail=str(e))


# ✅ Polling API (If Webhook Fails)
@app.get("/gradio_api/poll/{event_id}")
async def poll(event_id: str):
    """Fetches stored AI response for a given event ID."""
    if event_id in event_store:
        return {"data": [event_store.pop(event_id)]}
    return {"detail": "Not Found"}


# ✅ Webhook Receiver (For Debugging Webhook Calls)
@app.post("/fineTuneModel")
async def receive_webhook(request: Request):
    """Handles webhook responses (useful for debugging webhook calls)."""
    try:
        req_body = await request.json()
        print("📩 Webhook Received:", req_body)
        return {"status": "success", "received": req_body}
    except Exception as e:
        return {"error": str(e)}


# ✅ Health Check
@app.get("/")
async def root():
    return {"message": "Workout Analysis & Ranking AI is running!"}


# ✅ Gradio UI for Testing
iface = gr.Interface(
    fn=analyze_workouts,
    inputs="text",
    outputs="text",
    title="Workout Analysis & Ranking AI",
    description="Enter workout data to analyze effectiveness, rank workouts, and receive improvement recommendations."
)


# ✅ Start Both FastAPI & Gradio
def start_gradio():
    iface.launch(server_name="0.0.0.0", server_port=7860, share=True)

def start_fastapi():
    uvicorn.run(app, host="0.0.0.0", port=7861)

# ✅ Run both servers in parallel
if __name__ == "__main__":
    import threading
    threading.Thread(target=start_gradio).start()
    threading.Thread(target=start_fastapi).start()