main.py

from fastapi import FastAPI, UploadFile, File, HTTPException, Form, Body, Request
from fastapi.responses import JSONResponse, FileResponse
from fastapi.middleware.cors import CORSMiddleware
from typing import Union, Optional
from app import schemas, services
import os

app = FastAPI(title="AI-Powered Health Risk Profiler")

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

# --- Health Check ---
@app.get("/health", summary="Health Check", include_in_schema=False)
async def health_check():
    return {"status": "healthy"}

# --- Serve the Simulator ---
@app.get("/", response_class=FileResponse, include_in_schema=False)
async def read_simulator():
    simulator_path = os.path.join(os.path.dirname(__file__), 'simulator.html')
    if not os.path.exists(simulator_path):
        raise HTTPException(status_code=404, detail="simulator.html not found")
    return FileResponse(simulator_path)


# --- Analyze Endpoint ---
@app.post("/analyze",
          response_model=Union[schemas.Recommendations, schemas.IncompleteProfileError],
          summary="Analyze Health Survey from JSON or Image")
async def analyze_survey(
    request: Request,
    file: Optional[UploadFile] = File(None)
):
    answers = {}
    confidence = 0.0
    if file:
        if not file.content_type.startswith("image/"):
            raise HTTPException(status_code=400, detail="Invalid file type. Please upload an image.")
        image_bytes = await file.read()
        parsed = services.parse_survey_from_image(image_bytes)
        answers = parsed["answers"]
        confidence = parsed["confidence"]
    else:
        try:
            survey_json = await request.json()
            survey_data = schemas.SurveyInput(**survey_json)
            answers = survey_data.dict()
            confidence = 1.0
        except Exception:
            raise HTTPException(status_code=422, detail="Invalid JSON format in request body.")

    required_fields = ["age", "smoker", "exercise", "diet"]
    missing_fields = [field for field in required_fields if field not in answers]
    if len(missing_fields) > len(required_fields) / 2:
        return schemas.IncompleteProfileError(status="incomplete_profile", reason=f">50% fields missing. Missing: {', '.join(missing_fields)}")

    factors = services.extract_factors(answers)
    risk_profile = services.classify_risk(factors)
    final_recommendations = services.generate_recommendations(risk_level=risk_profile["risk_level"], factors=factors)
    final_recommendations["confidence"] = confidence
    return schemas.Recommendations(**final_recommendations)