app.py

import os
import tempfile
import logging
import numpy as np
import torch
import torch.nn as nn
import whisper
import librosa
import asyncio
import random
import requests
from datetime import datetime, timedelta
from typing import List, Dict, Any, Optional
from fastapi import FastAPI, UploadFile, File, HTTPException, Request, Form
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from PIL import Image
from torchvision import transforms
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
from speechbrain.inference.classifiers import EncoderClassifier
import torchaudio
import json
from pydantic import BaseModel
from supabase import create_client, Client

# Setup logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
)
logger = logging.getLogger(__name__)

class ModelManager:
    """Centralized model management for all ML models."""
    
    _instance = None
    
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super(ModelManager, cls).__new__(cls)
            cls._instance._initialized = False
        return cls._instance
    
    def __init__(self):
        if self._initialized:
            return
            
        self._initialized = True
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        logger.info(f"Using device: {self.device}")
        
        self.emotion_model = None
        self.whisper_model = None
        self.text_tokenizer = None
        self.text_model = None
        self.speechbrain_model = None
        
        # Model paths
        self.MODEL_PATHS = {
            'whisper_model': 'base',
            'text_model': 'emotion-distilbert-model',
            'speechbrain_model': 'speechbrain/emotion-recognition-wav2vec2-IEMOCAP'
        }
        
        # Constants
        self.EMOTIONS = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
        self.SAMPLE_RATE = 16000
        self.TEXT_EMOTIONS = ["sadness", "joy", "love", "anger", "fear", "surprise"]
        
        # SpeechBrain emotion mapping
        self.SPEECHBRAIN_EMOTION_MAP = {
            'neu': 'Neutral',
            'hap': 'Happy',
            'sad': 'Sad',
            'ang': 'Angry',
            'fea': 'Fear',
            'dis': 'Disgust',
            'sur': 'Surprise'
        }
        
    def load_all_models(self):
        """Load all required models."""
        try:
            logger.info("Starting to load all models...")
            self._load_emotion_model()
            self._load_whisper_model()
            self._load_text_models()
            self._load_speechbrain_model()
            logger.info("All models loaded successfully!")
            return True
        except Exception as e:
            logger.error(f"Error loading models: {str(e)}")
            raise
    
    def _load_emotion_model(self):
        """Use DeepFace for emotion recognition."""
        try:
            logger.info("Loading DeepFace for emotion recognition...")
            from deepface import DeepFace
            self.emotion_model = DeepFace
            logger.info("DeepFace loaded successfully")
        except Exception as e:
            logger.error(f"Failed to initialize DeepFace: {str(e)}")
            raise

    def _load_whisper_model(self):
        """Load the Whisper speech-to-text model."""
        try:
            logger.info("Loading Whisper model...")
            self.whisper_model = whisper.load_model(self.MODEL_PATHS['whisper_model'])
            logger.info("Whisper model loaded successfully")
        except Exception as e:
            logger.error(f"Failed to load Whisper model: {str(e)}")
            raise
    
    def _load_text_models(self):
        """Load the text emotion classification model and tokenizer."""
        try:
            logger.info("Loading text emotion model...")
            model_path = self.MODEL_PATHS['text_model']
            
            # Try to load from local path first, then from HuggingFace Hub
            if os.path.exists(model_path):
                self.text_tokenizer = DistilBertTokenizerFast.from_pretrained(model_path)
                self.text_model = DistilBertForSequenceClassification.from_pretrained(model_path)
            else:
                # Use a public emotion model from HuggingFace
                logger.info("Local model not found, using HuggingFace model...")
                self.text_tokenizer = DistilBertTokenizerFast.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
                self.text_model = DistilBertForSequenceClassification.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
            
            self.text_model.eval()
            logger.info("Text models loaded successfully")
            
        except Exception as e:
            logger.error(f"Failed to load text models: {str(e)}")
            raise
    
    def _load_speechbrain_model(self):
        """Load SpeechBrain emotion recognition model."""
        try:
            logger.info("Loading SpeechBrain emotion recognition model...")
            self.speechbrain_model = EncoderClassifier.from_hparams(
                source=self.MODEL_PATHS['speechbrain_model'],
                savedir="pretrained_models/emotion-recognition-wav2vec2-IEMOCAP",
                run_opts={"device": "cpu"}
            )
            logger.info("SpeechBrain emotion recognition model loaded successfully")
        except Exception as e:
            logger.error(f"Failed to load SpeechBrain model: {str(e)}")
            raise

    def get_emotion_model(self):
        if self.emotion_model is None:
            self._load_emotion_model()
        return self.emotion_model

    def get_whisper_model(self):
        if self.whisper_model is None:
            self._load_whisper_model()
        return self.whisper_model

    def get_text_models(self):
        if self.text_model is None or self.text_tokenizer is None:
            self._load_text_models()
        return self.text_tokenizer, self.text_model

    def get_speechbrain_model(self):
        if self.speechbrain_model is None:
            self._load_speechbrain_model()
        return self.speechbrain_model


# Initialize FastAPI app
app = FastAPI(title="Manan ML API - Emotion Recognition")

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

# Initialize model manager
model_manager = ModelManager()

# Image transformation pipeline
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])


@app.on_event("startup")
async def startup_event():
    """Initialize all models when the application starts."""
    try:
        logger.info("Starting model initialization...")
        model_manager.load_all_models()
        logger.info("All models initialized successfully!")
    except Exception as e:
        logger.error(f"Failed to initialize models: {str(e)}")
        # Don't raise - let the app start and load models on demand


@app.get("/")
async def root():
    """Health check endpoint."""
    return {
        "status": "running",
        "message": "Manan ML API is running!",
        "endpoints": [
            "/pred_face - Face emotion prediction",
            "/predict_audio_batch - Voice emotion prediction",
            "/predict_text/ - Text emotion prediction"
        ]
    }


@app.get("/health")
async def health_check():
    """Health check endpoint."""
    return {"status": "healthy", "device": str(model_manager.device)}


# Helper function for SpeechBrain prediction
def predict_emotion_speechbrain(audio_path: str) -> Dict[str, Any]:
    """Predict emotion from audio using SpeechBrain."""
    try:
        speechbrain_model = model_manager.get_speechbrain_model()
        
        signal, sr = torchaudio.load(audio_path)
        
        if sr != 16000:
            resampler = torchaudio.transforms.Resample(sr, 16000)
            signal = resampler(signal)
        
        if signal.dim() == 1:
            signal = signal.unsqueeze(0)
        elif signal.dim() == 3:
            signal = signal.squeeze(1)
        
        device = next(speechbrain_model.mods.wav2vec2.parameters()).device
        signal = signal.to(device)
        
        with torch.no_grad():
            feats = speechbrain_model.mods.wav2vec2(signal)
            pooled = speechbrain_model.mods.avg_pool(feats)
            out = speechbrain_model.mods.output_mlp(pooled)
            out_prob = speechbrain_model.hparams.softmax(out)
        
        score, index = torch.max(out_prob, dim=-1)
        predicted_emotion = speechbrain_model.hparams.label_encoder.decode_ndim(index.cpu())
        
        if isinstance(predicted_emotion, list):
            if isinstance(predicted_emotion[0], list):
                emotion_key = str(predicted_emotion[0][0]).lower()[:3]
            else:
                emotion_key = str(predicted_emotion[0]).lower()[:3]
        else:
            emotion_key = str(predicted_emotion).lower()[:3]
        
        emotion = model_manager.SPEECHBRAIN_EMOTION_MAP.get(emotion_key, 'Neutral')
        probs = out_prob[0].detach().cpu().numpy()
        
        if probs.ndim > 1:
            probs = probs.flatten()
        
        all_emotions = speechbrain_model.hparams.label_encoder.decode_ndim(
            torch.arange(len(probs))
        )
        prob_dict = {}
        for i in range(len(probs)):
            if i < len(all_emotions):
                if isinstance(all_emotions[i], list):
                    key = str(all_emotions[i][0]).lower()[:3]
                else:
                    key = str(all_emotions[i]).lower()[:3]
                emotion_name = model_manager.SPEECHBRAIN_EMOTION_MAP.get(key, f'emotion_{i}')
                prob_dict[emotion_name] = float(probs[i])
        
        confidence = float(score[0])
        
        return {
            'emotion': emotion,
            'confidence': confidence,
            'probabilities': prob_dict
        }
        
    except Exception as e:
        logger.error(f"Error predicting emotion with SpeechBrain: {str(e)}")
        raise


def transcribe_audio(audio_path: str) -> str:
    """Transcribe audio to text using Whisper."""
    try:
        result = model_manager.whisper_model.transcribe(audio_path)
        return result["text"].strip()
    except Exception as e:
        logger.error(f"Error in audio transcription: {str(e)}")
        return ""


# ============== API ENDPOINTS ==============

@app.post("/pred_face")
async def predict_face_emotion(
    files: List[UploadFile] = File(...),
    questions: str = Form(None)
):
    """Predict emotions from face images using DeepFace."""
    from deepface import DeepFace

    logger.info(f"Received {len(files)} files for face prediction")
    if not files:
        raise HTTPException(status_code=400, detail="No files provided")

    temp_files = []

    try:
        questions_data = {}
        question_count = 0

        if questions:
            try:
                questions_data = json.loads(questions)
                question_count = len(questions_data)
            except json.JSONDecodeError:
                raise HTTPException(status_code=400, detail="Invalid questions JSON format.")
        else:
            question_count = 3
            questions_data = {str(i): {"text": f"Question {i+1}", "imageCount": 1} for i in range(question_count)}

        question_files = {str(i): [] for i in range(question_count)}
        for file in files:
            if '_' in file.filename and file.filename.startswith('q'):
                try:
                    q_idx = file.filename.split('_')[0][1:]
                    if q_idx in question_files:
                        question_files[q_idx].append(file)
                except Exception as e:
                    logger.warning(f"Skipping file {file.filename}: {e}")

        results = []

        for q_idx, q_files in question_files.items():
            if not q_files:
                results.append({
                    "emotion": "Unknown",
                    "probabilities": {e: 0.0 for e in model_manager.EMOTIONS}
                })
                continue

            probs_list = []

            for file in q_files:
                try:
                    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
                        content = await file.read()
                        tmp.write(content)
                        temp_path = tmp.name
                        temp_files.append(temp_path)

                    analysis = DeepFace.analyze(
                        img_path=temp_path,
                        actions=['emotion'],
                        enforce_detection=False,
                        silent=True
                    )

                    if isinstance(analysis, list):
                        analysis = analysis[0]

                    emotion_scores = analysis.get('emotion', {})
                    dominant_emotion = analysis.get('dominant_emotion', 'neutral')

                    normalized_probs = {}
                    for emo in model_manager.EMOTIONS:
                        key = emo.lower()
                        normalized_probs[emo] = emotion_scores.get(key, 0.0) / 100.0

                    probs_list.append(normalized_probs)

                except Exception as e:
                    logger.error(f"Error processing {file.filename}: {e}")

            if probs_list:
                avg_probs = {}
                for emo in model_manager.EMOTIONS:
                    avg_probs[emo] = sum(p.get(emo, 0) for p in probs_list) / len(probs_list)

                dominant_emotion = max(avg_probs, key=avg_probs.get)
                results.append({
                    "emotion": dominant_emotion,
                    "probabilities": avg_probs
                })
            else:
                results.append({
                    "emotion": "Unknown",
                    "probabilities": {e: 0.0 for e in model_manager.EMOTIONS}
                })

        return results

    except Exception as e:
        logger.error(f"Error in face emotion prediction: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

    finally:
        for file_path in temp_files:
            try:
                if os.path.exists(file_path):
                    os.remove(file_path)
            except Exception as e:
                logger.warning(f"Failed to delete temp file {file_path}: {e}")


@app.post("/predict_audio_batch")
async def predict_audio_batch(files: List[UploadFile] = File(...)):
    """Predict emotions from multiple audio files using SpeechBrain."""
    logger.info(f"Received {len(files)} audio files for prediction")
    
    if not files:
        raise HTTPException(status_code=400, detail="No audio files provided")

    temp_files = []
    results = []

    try:
        for file in files:
            try:
                with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
                    content = await file.read()
                    tmp.write(content)
                    temp_path = tmp.name
                    temp_files.append(temp_path)

                prediction = predict_emotion_speechbrain(temp_path)
                results.append(prediction)
                logger.info(f"Predicted emotion for {file.filename}: {prediction['emotion']}")

            except Exception as e:
                logger.error(f"Error processing {file.filename}: {e}")
                results.append({
                    'emotion': 'Unknown',
                    'confidence': 0.0,
                    'probabilities': {},
                    'error': str(e)
                })

        return {'status': 'success', 'results': results}

    except Exception as e:
        logger.error(f"Error in audio batch prediction: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

    finally:
        for file_path in temp_files:
            try:
                if os.path.exists(file_path):
                    os.remove(file_path)
            except Exception as e:
                logger.warning(f"Failed to delete temp file {file_path}: {e}")


@app.post("/predict_text/")
async def predict_text_emotion(files: List[UploadFile] = File(...)):
    """Transcribe audio and predict text emotion."""
    logger.info(f"Received {len(files)} audio files for text prediction")
    
    if not files:
        raise HTTPException(status_code=400, detail="No audio files provided")

    temp_files = []
    results = []

    try:
        tokenizer, text_model = model_manager.get_text_models()
        whisper_model = model_manager.get_whisper_model()

        for file in files:
            try:
                with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
                    content = await file.read()
                    tmp.write(content)
                    temp_path = tmp.name
                    temp_files.append(temp_path)

                # Transcribe
                transcription = whisper_model.transcribe(temp_path)
                transcript = transcription["text"].strip()
                logger.info(f"Transcribed: {transcript}")

                if not transcript:
                    results.append({
                        'transcript': '',
                        'emotion': 'neutral',
                        'confidence': 0.0,
                        'probabilities': {}
                    })
                    continue

                # Predict emotion from text
                inputs = tokenizer(
                    transcript,
                    return_tensors="pt",
                    truncation=True,
                    max_length=128,
                    padding=True
                )

                with torch.no_grad():
                    outputs = text_model(**inputs)
                    probs = torch.softmax(outputs.logits, dim=1)[0]

                # Get emotion labels
                emotion_labels = model_manager.TEXT_EMOTIONS
                if hasattr(text_model.config, 'id2label'):
                    emotion_labels = [text_model.config.id2label[i] for i in range(len(probs))]

                prob_dict = {emotion_labels[i]: float(probs[i]) for i in range(len(probs))}
                predicted_idx = torch.argmax(probs).item()
                predicted_emotion = emotion_labels[predicted_idx]
                confidence = float(probs[predicted_idx])

                results.append({
                    'transcript': transcript,
                    'emotion': predicted_emotion,
                    'confidence': confidence,
                    'probabilities': prob_dict
                })

            except Exception as e:
                logger.error(f"Error processing {file.filename}: {e}")
                results.append({
                    'transcript': '',
                    'emotion': 'unknown',
                    'confidence': 0.0,
                    'error': str(e)
                })

        return results

    except Exception as e:
        logger.error(f"Error in text prediction: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

    finally:
        for file_path in temp_files:
            try:
                if os.path.exists(file_path):
                    os.remove(file_path)
            except Exception as e:
                logger.warning(f"Failed to delete temp file {file_path}: {e}")


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)


# =============================================================================
# AUTHENTICATION & USER MANAGEMENT ENDPOINTS
# =============================================================================

# Supabase configuration
SUPABASE_URL = os.environ.get("SUPABASE_URL")
SUPABASE_KEY = os.environ.get("SUPABASE_KEY")
BREVO_API_KEY = os.environ.get("EMAIL_API")

# Initialize Supabase client
supabase: Client = None
try:
    if SUPABASE_URL and SUPABASE_KEY:
        supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
        logger.info("Supabase client initialized successfully")
    else:
        logger.warning("Supabase credentials not found in environment variables")
except Exception as e:
    logger.error(f"Failed to initialize Supabase: {str(e)}")

# OTP storage (in-memory, resets on restart)
otp_store = {}

# -------------------------------
# Request Models for Auth
# -------------------------------
class OTPRequest(BaseModel):
    email: str

class OTPVerifyRequest(BaseModel):
    email: str
    otp: str

class RegisterUserRequest(BaseModel):
    name: str
    email: str
    password: str

class SendEmotionRequest(BaseModel):
    email: str
    emotion: str

class UpdateProfilePicRequest(BaseModel):
    email: str
    profile_pic_url: str

class UpdateProfileRequest(BaseModel):
    email: str
    name: str = None
    age: int = None
    phone: str = None

# -------------------------------
# Helper Function - Send Email via Brevo
# -------------------------------
def send_email_brevo(to_email: str, otp: str):
    url = "https://api.brevo.com/v3/smtp/email"
    headers = {
        "accept": "application/json",
        "api-key": BREVO_API_KEY,
        "content-type": "application/json",
    }
    data = {
        "sender": {"name": "मनन", "email": "noreplymanan@gmail.com"},
        "to": [{"email": to_email}],
        "subject": "Your Manan OTP Code",
        "htmlContent": f"""
            <html>
              <body>
                <h2>Your OTP Code</h2>
                <p>Your verification code is: <strong>{otp}</strong></p>
                <p>This code will expire in 5 minutes.</p>
              </body>
            </html>
        """,
    }

    response = requests.post(url, headers=headers, json=data)
    if response.status_code not in [200, 201]:
        raise HTTPException(status_code=500, detail=f"Email sending failed: {response.text}")

# -------------------------------
# Auth Endpoints
# -------------------------------
@app.get("/status")
def get_status():
    try:
        if supabase is None:
            return {"status": "Supabase not configured", "supabase_data_retrieved": False}
        response = supabase.table('users').select("id").limit(1).execute()
        return {"status": "Connection successful", "supabase_data_retrieved": len(response.data) > 0}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Connection failed: {str(e)}")

@app.post("/send_otp")
def send_otp(req: OTPRequest):
    try:
        otp = str(random.randint(100000, 999999))
        otp_store[req.email] = {"otp": otp, "timestamp": datetime.utcnow()}
        logger.info(f"OTP generated for {req.email}")

        # Send email
        send_email_brevo(req.email, otp)

        return {"message": f"OTP sent successfully to {req.email}"}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error sending OTP: {str(e)}")

@app.post("/check_otp")
def check_otp(req: OTPVerifyRequest):
    try:
        if req.email not in otp_store:
            raise HTTPException(status_code=404, detail="No OTP found for this email")

        stored_data = otp_store[req.email]
        stored_otp = stored_data["otp"]
        timestamp = stored_data["timestamp"]

        # Expiry check (5 minutes)
        if datetime.utcnow() - timestamp > timedelta(minutes=5):
            del otp_store[req.email]
            raise HTTPException(status_code=400, detail="OTP expired")

        if req.otp == stored_otp:
            # Mark as verified instead of deleting
            otp_store[req.email]["verified"] = True
            otp_store[req.email]["otp"] = None
            return {"verified": True}
        else:
            raise HTTPException(status_code=400, detail="Invalid OTP")

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error verifying OTP: {str(e)}")

@app.post("/register_user")
def register_user(req: RegisterUserRequest):
    try:
        if supabase is None:
            raise HTTPException(status_code=500, detail="Supabase not configured")
            
        # Check OTP verification
        if req.email not in otp_store or not otp_store[req.email].get("verified", False):
            raise HTTPException(status_code=403, detail="Email not verified via OTP")

        # Insert into Supabase
        response = supabase.table("users").insert({
            "name": req.name,
            "email": req.email,
            "password": req.password
        }).execute()

        # Cleanup
        del otp_store[req.email]

        return {"message": "User registered successfully", "data": response.data}

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error registering user: {str(e)}")

@app.get("/get_profile/{email}")
def get_profile(email: str):
    try:
        if supabase is None:
            raise HTTPException(status_code=500, detail="Supabase not configured")
            
        response = supabase.table("users").select("*").eq("email", email).single().execute()
        
        if not response.data:
            raise HTTPException(status_code=404, detail="User not found")
        
        # Remove password from response for security
        profile_data = response.data.copy()
        profile_data.pop('password', None)
        
        return {"profile": profile_data}
    
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error fetching profile: {str(e)}")

@app.put("/update_profile")
def update_profile(req: UpdateProfileRequest):
    try:
        if supabase is None:
            raise HTTPException(status_code=500, detail="Supabase not configured")
            
        update_data = {}
        if req.name is not None:
            update_data["name"] = req.name
        if req.age is not None:
            update_data["age"] = req.age
        if req.phone is not None:
            update_data["phone"] = req.phone
        
        if not update_data:
            raise HTTPException(status_code=400, detail="No data provided for update")
        
        response = supabase.table("users").update(update_data).eq("email", req.email).execute()
        
        if not response.data:
            raise HTTPException(status_code=404, detail="User not found")
        
        return {"message": "Profile updated successfully", "data": response.data}
    
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error updating profile: {str(e)}")

@app.post("/update_profile_pic")
def update_profile_pic(req: UpdateProfilePicRequest):
    try:
        if supabase is None:
            raise HTTPException(status_code=500, detail="Supabase not configured")
            
        response = supabase.table("users").update({
            "profilepic": req.profile_pic_url
        }).eq("email", req.email).execute()
        
        if not response.data:
            raise HTTPException(status_code=404, detail="User not found")
        
        return {"message": "Profile picture updated successfully", "data": response.data}
    
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error updating profile picture: {str(e)}")

@app.get("/get_score/{email}")
def get_score(email: str):
    try:
        if supabase is None:
            raise HTTPException(status_code=500, detail="Supabase not configured")
            
        user_response = supabase.table("users").select("id").eq("email", email).execute()

        if not user_response.data:
            raise HTTPException(status_code=404, detail="User not found")

        user_id = user_response.data[0]["id"]

        predict_response = supabase.table("predict").select("prediction, timestamp") \
            .eq("user_id", user_id).order("timestamp", desc=True).limit(1).execute()

        if not predict_response.data:
            raise HTTPException(status_code=404, detail="No predictions found for this user")

        return predict_response.data[0]

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error fetching score: {str(e)}")

@app.post("/send_emotion")
def send_emotion(req: SendEmotionRequest):
    try:
        if supabase is None:
            raise HTTPException(status_code=500, detail="Supabase not configured")
            
        user_response = supabase.table("users").select("id").eq("email", req.email).execute()
        
        if not user_response.data:
            raise HTTPException(status_code=404, detail="User not found")
            
        user_id = user_response.data[0]["id"]
        
        data = {
            "user_id": user_id,
            "prediction": req.emotion,
            "timestamp": datetime.utcnow().isoformat()
        }
        
        response = supabase.table("predict").insert(data).execute()
        
        return {"message": "Emotion saved successfully", "data": response.data}

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error saving emotion: {str(e)}")

@app.get("/get_emotion/{email}")
def get_emotion(email: str):
    try:
        if supabase is None:
            raise HTTPException(status_code=500, detail="Supabase not configured")
            
        user_response = supabase.table("users").select("id").eq("email", email).execute()
        
        if not user_response.data:
            raise HTTPException(status_code=404, detail="User not found")
            
        user_id = user_response.data[0]["id"]
        
        predict_response = supabase.table("predict").select("prediction") \
            .eq("user_id", user_id).order("timestamp", desc=True).limit(1).execute()
            
        if not predict_response.data:
            return {"emotion": "Neutral"}
            
        return {"emotion": predict_response.data[0]["prediction"]}

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error fetching emotion: {str(e)}")

@app.get("/get_mental_health_details/{email}")
def get_mental_health_details(email: str):
    try:
        if supabase is None:
            raise HTTPException(status_code=500, detail="Supabase not configured")
            
        user_response = supabase.table("users").select("id").eq("email", email).execute()
        if not user_response.data:
            raise HTTPException(status_code=404, detail="User not found")
        user_id = user_response.data[0]["id"]

        # Get most recent prediction
        recent_prediction = supabase.table("predict").select("prediction, timestamp") \
            .eq("user_id", user_id).order("timestamp", desc=True).limit(1).execute()
        current_prediction = recent_prediction.data[0]["prediction"] if recent_prediction.data else None

        # Calculate this week's active days
        week_ago = datetime.utcnow() - timedelta(days=7)
        weekly_predictions = supabase.table("predict").select("timestamp") \
            .eq("user_id", user_id).gte("timestamp", week_ago.isoformat()).execute()

        if weekly_predictions.data:
            dates = set()
            for p in weekly_predictions.data:
                if p.get("timestamp"):
                    try:
                        ts_str = p["timestamp"].replace('Z', '+00:00')
                        dt = datetime.fromisoformat(ts_str)
                        dates.add(dt.date())
                    except (ValueError, AttributeError):
                        try:
                            ts_str = p["timestamp"].split('+')[0].split('Z')[0]
                            dt = datetime.strptime(ts_str, '%Y-%m-%dT%H:%M:%S.%f')
                            dates.add(dt.date())
                        except:
                            try:
                                dt = datetime.strptime(ts_str, '%Y-%m-%dT%H:%M:%S')
                                dates.add(dt.date())
                            except:
                                pass
            active_days = len(dates)
        else:
            active_days = 0

        # Total conversations
        total_conversations = supabase.table("predict").select("*", count="exact") \
            .eq("user_id", user_id).execute()
        total_count = total_conversations.count if total_conversations else 0

        return {
            "current_prediction": current_prediction,
            "active_days_this_week": active_days,
            "total_conversations": total_count
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error fetching mental health details: {str(e)}")


# =============================================================================
# DAILY EMOTION SCORE CALCULATION
# =============================================================================

# Valence Mapping for emotions
EMO_VALENCE = {
    "Angry": -0.80,
    "Disgust": -0.60,
    "Fear": -0.70,
    "Happy": 0.90,
    "Sad": -0.90,
    "Surprise": 0.20,
    "Neutral": 0.0,
    # text emotions
    "sadness": -0.90,
    "joy": 0.90,
    "love": 0.80,
    "anger": -0.80,
    "fear": -0.70,
    "surprise": 0.20
}

def valence_from_probabilities(probabilities: Dict[str, float]) -> float:
    if not probabilities:
        return 0.0
    v = 0.0
    total = sum(probabilities.values()) or 1.0
    for emo, p in probabilities.items():
        key = emo if emo in EMO_VALENCE else emo.capitalize()
        v += p * EMO_VALENCE.get(key, 0.0)
    return v / total

def valence_to_score(v: float) -> float:
    return (v + 1) / 2 * 100  # [-1..1] → [0..100]

class EmotionItem(BaseModel):
    emotion: str
    probabilities: Optional[Dict[str, float]] = None
    confidence: Optional[float] = 1.0

class ScoreRequest(BaseModel):
    face_results: Optional[List[EmotionItem]] = []
    audio_results: Optional[List[EmotionItem]] = []
    text_results: Optional[List[EmotionItem]] = []

@app.post("/calculate_day_score")
def calculate_day_score(payload: ScoreRequest):
    """Calculate weighted day score from face, audio, and text emotions."""
    source_weights = {
        "face": 0.4,
        "audio": 0.35,
        "text": 0.25
    }

    accum_num = 0.0
    accum_den = 0.0
    breakdown = {"face": [], "audio": [], "text": []}

    # FACE
    for item in payload.face_results:
        v = valence_from_probabilities(item.probabilities) \
            if item.probabilities else EMO_VALENCE.get(item.emotion, 0.0)
        score = valence_to_score(v)
        w = source_weights["face"] * (item.confidence or 1.0)
        accum_num += score * w
        accum_den += w
        breakdown["face"].append({
            "emotion": item.emotion,
            "valence": v,
            "score": score,
            "weight": w
        })

    # AUDIO
    for item in payload.audio_results:
        v = valence_from_probabilities(item.probabilities) \
            if item.probabilities else EMO_VALENCE.get(item.emotion, 0.0)
        score = valence_to_score(v)
        w = source_weights["audio"] * (item.confidence or 1.0)
        accum_num += score * w
        accum_den += w
        breakdown["audio"].append({
            "emotion": item.emotion,
            "confidence": item.confidence,
            "valence": v,
            "score": score,
            "weight": w
        })

    # TEXT
    for item in payload.text_results:
        v = valence_from_probabilities(item.probabilities) \
            if item.probabilities else EMO_VALENCE.get(item.emotion.lower(), 0.0)
        score = valence_to_score(v)
        w = source_weights["text"] * (item.confidence or 1.0)
        accum_num += score * w
        accum_den += w
        breakdown["text"].append({
            "emotion": item.emotion,
            "confidence": item.confidence,
            "valence": v,
            "score": score,
            "weight": w
        })

    final_score = accum_num / accum_den if accum_den > 0 else None

    return {
        "day_score": final_score,
        "breakdown": breakdown,
        "numerator": accum_num,
        "denominator": accum_den
    }