import os
import shutil
import tempfile
import uvicorn
import warnings
from fastapi import FastAPI, UploadFile, File, HTTPException, Depends
from fastapi.middleware.cors import CORSMiddleware
from mtcnn.mtcnn import MTCNN
import tensorflow as tf
from huggingface_hub import hf_hub_download

# --- Suppress TensorFlow & MTCNN Warnings ---
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.get_logger().setLevel('ERROR')
warnings.filterwarnings('ignore')

# HuggingFace Hub Configuration
HF_REPO_ID = "piyushnaula/deepfake_model_return0"
HF_TOKEN = os.getenv("HF_TOKEN")  # Set this in environment variables

# --- Imports for config and prediction functions ---
try:
    from . import config
    from .predict import get_image_prediction
    from .predict_video_model import get_video_prediction
    from .database import connect_to_mongo, close_mongo_connection, get_database
    from .auth import (
        UserSignup, UserLogin, UserResponse, UsageResponse,
        hash_password, verify_password, generate_api_key, create_user_document,
        validate_api_key, hash_api_key
    )
except ImportError:
    # This fallback lets us run the file directly if needed
    import config
    from predict import get_image_prediction
    from predict_video_model import get_video_prediction
    from database import connect_to_mongo, close_mongo_connection, get_database
    from auth import (
        UserSignup, UserLogin, UserResponse, UsageResponse,
        hash_password, verify_password, generate_api_key, create_user_document,
        validate_api_key, hash_api_key
    )

# --- 1. Create the FastAPI app ---
app = FastAPI(
    title="Deepfake Detector API",
    description="An API to detect deepfake images and videos using advanced ML models.",
    version="1.0.0"
)

# --- CORS Configuration ---
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allows all origins
    allow_credentials=True,
    allow_methods=["*"],  # Allows all methods
    allow_headers=["*"],  # Allows all headers
)

# --- 2. Load Models at Startup (Best Practice) ---
# This dictionary will hold our models, loaded ONCE.
# This is far more efficient than loading them for every request.
models = {}

@app.on_event("startup")
async def startup_event():
    """
    Startup: Connect to MongoDB and load ML models.
    """
    # Connect to MongoDB first
    await connect_to_mongo()
    
    # Then load ML models
    await load_models()


@app.on_event("shutdown")
async def shutdown_event():
    """Shutdown: Close MongoDB connection."""
    await close_mongo_connection()


async def load_models():
    """
    Load all ML models from HuggingFace Hub when the API server starts.
    """
    print("--- Loading models from HuggingFace Hub... ---")
    
    # --- Load Image Model from HuggingFace ---
    try:
        print("Downloading Image Model (baseline_model.h5) from HuggingFace...")
        image_model_path = hf_hub_download(
            repo_id=HF_REPO_ID,
            filename="baseline_model.h5",
            token=HF_TOKEN
        )
        models["image_model"] = tf.keras.models.load_model(image_model_path, compile=False)
        print("Image model loaded successfully.")
    except Exception as e:
        print(f"WARNING: Failed to load Image Model: {e}")

    # --- Load Video Model from HuggingFace ---
    try:
        print("Downloading Video Model from HuggingFace...")
        
        # Download finetuned encoder first (needed for video_model.py)
        finetuned_path = None
        try:
            finetuned_path = hf_hub_download(
                repo_id=HF_REPO_ID,
                filename="finetuned_model.h5",
                token=HF_TOKEN
            )
            print(f"Finetuned encoder downloaded to: {finetuned_path}")
        except Exception as e:
            print(f"WARNING: Could not download finetuned_model.h5: {e}")
            print("Will use ImageNet weights as fallback...")
        
        # Import build_video_model here to avoid circular imports
        try:
            from .video_model import build_video_model
        except ImportError:
            from video_model import build_video_model
        
        # Build the model architecture (pass the downloaded path)
        print("Building video model architecture...")
        video_model = build_video_model(finetuned_model_path=finetuned_path)
        
        # Try to download and load weights
        try:
            video_weights_path = hf_hub_download(
                repo_id=HF_REPO_ID,
                filename="video_model_v2.keras",
                token=HF_TOKEN
            )
            video_model.load_weights(video_weights_path)
            print("Video model weights loaded successfully.")
        except Exception as e:
            print(f"WARNING: Could not load video weights: {e}")
            print("Video model will use untrained weights (less accurate).")
        
        models["video_model"] = video_model
        print("Video model initialized successfully.")
    except Exception as e:
        print(f"WARNING: Failed to load Video Model: {e}")
        import traceback
        traceback.print_exc()

    # --- Load MTCNN Detector ---
    models["mtcnn_detector"] = MTCNN()
    print("MTCNN detector initialized.")

    # --- Load Audio Model (HuggingFace Transformers) ---
    try:
        print("Loading Audio Model (wav2vec2-base-finetuned)...")
        from transformers import Wav2Vec2FeatureExtractor, AutoModelForAudioClassification
        
        # Use FeatureExtractor instead of Processor (no tokenizer needed for classification)
        models["audio_processor"] = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-base")
        models["audio_model"] = AutoModelForAudioClassification.from_pretrained("mo-thecreator/wav2vec2-base-finetuned")
        print("Audio Model and Feature Extractor loaded successfully!")
    except Exception as e:
        print(f"CRITICAL: Failed to load Audio Model: {type(e).__name__}: {e}")
        import traceback
        traceback.print_exc()

    # Print final status
    print("--- Model loading complete ---")
    print(f"Loaded models: {list(models.keys())}")
    
# --- 3. Define API Endpoints ---

@app.get("/")
def read_root():
    """A simple 'health check' endpoint to see if the server is running."""
    return {"status": "Deepfake Detector API is online and running."}


# --- 4. Authentication Endpoints ---

@app.post("/signup", response_model=UserResponse)
async def signup(user: UserSignup):
    """
    Create a new user account and get your API key.
    ⚠️ IMPORTANT: Save your API key! It will only be shown ONCE.
    """
    db = get_database()
    
    # Check if email already exists
    existing = await db.users.find_one({"email": user.email})
    if existing:
        raise HTTPException(
            status_code=400,
            detail="Email already registered. Please login to get your API key."
        )
    
    # Create new user (returns user_doc and raw_api_key)
    user_doc, raw_api_key = create_user_document(user.email, user.password)
    await db.users.insert_one(user_doc)
    
    return UserResponse(
        email=user.email,
        api_key=raw_api_key,
        message="Account created! ⚠️ SAVE YOUR API KEY NOW - it will NOT be shown again!"
    )


@app.post("/login", response_model=UserResponse)
async def login(user: UserLogin):
    """
    Login to view your API key prefix.
    Note: For security, full key is only shown at signup.
    Use /regenerate-key to get a new key if lost.
    """
    db = get_database()
    
    # Find user
    existing = await db.users.find_one({"email": user.email})
    if not existing:
        raise HTTPException(status_code=404, detail="User not found. Please signup first.")
    
    # Verify password
    if not verify_password(user.password, existing["password_hash"]):
        raise HTTPException(status_code=401, detail="Invalid password.")
    
    # Update last login
    from datetime import datetime
    await db.users.update_one(
        {"email": user.email},
        {"$set": {"last_login": datetime.utcnow()}}
    )
    
    return UserResponse(
        email=user.email,
        api_key=existing.get("api_key_prefix", "Key hidden for security"),
        message="Login successful. Use /regenerate-key if you need a new API key."
    )


@app.post("/regenerate-key", response_model=UserResponse)
async def regenerate_key(user: UserLogin):
    """
    Generate a new API key. The old key will stop working.
    ⚠️ IMPORTANT: Save your new API key! It will only be shown ONCE.
    """
    db = get_database()
    
    # Find user
    existing = await db.users.find_one({"email": user.email})
    if not existing:
        raise HTTPException(status_code=404, detail="User not found.")
    
    # Verify password
    if not verify_password(user.password, existing["password_hash"]):
        raise HTTPException(status_code=401, detail="Invalid password.")
    
    # Generate new key (returns tuple)
    raw_key, key_hash, key_prefix = generate_api_key()
    await db.users.update_one(
        {"email": user.email},
        {"$set": {"api_key_hash": key_hash, "api_key_prefix": key_prefix}}
    )
    
    return UserResponse(
        email=user.email,
        api_key=raw_key,
        message="New API key generated! ⚠️ SAVE IT NOW - old key is now invalid!"
    )


@app.get("/usage", response_model=UsageResponse)
async def get_usage(user: dict = Depends(validate_api_key)):
    """
    Check your API usage and remaining quota.
    Requires x-api-key header.
    """
    rate_limit = user.get("rate_limit", 100)
    requests_today = user.get("requests_today", 0)
    
    return UsageResponse(
        email=user["email"],
        requests_today=requests_today,
        rate_limit=rate_limit,
        remaining=max(0, rate_limit - requests_today),
        total_requests=user.get("total_requests", 0)
    )


# --- 5. Prediction Endpoints (Protected) ---

@app.post("/predict_image")
async def predict_image_api(
    file: UploadFile = File(...),
    user: dict = Depends(validate_api_key)
):
    """
    Endpoint for predicting a single deepfake image.
    Requires API key in x-api-key header.
    """
    if "image_model" not in models:
        raise HTTPException(status_code=500, detail="Image model is not loaded.")
    
    # We must save the uploaded file to a temporary path
    # because our prediction function expects a file path.
    temp_file_path = ""
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=file.filename) as temp_file:
            shutil.copyfileobj(file.file, temp_file)
            temp_file_path = temp_file.name
        
        print(f"Processing image: {temp_file_path}")
        
        # Call our prediction function and pass it the pre-loaded model
        result = get_image_prediction(
            image_path=temp_file_path,
            model=models["image_model"]
        )
        return result
        
    except Exception as e:
        # If anything goes wrong, return an error
        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
    finally:
        # CRITICAL: Always clean up the temp file
        if os.path.exists(temp_file_path):
            os.remove(temp_file_path)

@app.post("/predict_video")
async def predict_video_api(
    file: UploadFile = File(...),
    user: dict = Depends(validate_api_key)
):
    """
    Endpoint for predicting a single deepfake video.
    Requires API key in x-api-key header.
    """
    if "video_model" not in models or "mtcnn_detector" not in models:
        raise HTTPException(status_code=500, detail="Video models are not loaded.")

    temp_file_path = ""
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=file.filename) as temp_file:
            shutil.copyfileobj(file.file, temp_file)
            temp_file_path = temp_file.name
            
        print(f"Processing video: {temp_file_path}")

        # Call the video prediction function
        result = get_video_prediction(
            video_path=temp_file_path,
            video_model=models["video_model"],
            detector=models["mtcnn_detector"]
        )
        return result
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
    finally:
        # CRITICAL: Always clean up the temp file
        if os.path.exists(temp_file_path):
            os.remove(temp_file_path)

@app.post("/predict_audio")
async def predict_audio_api(
    file: UploadFile = File(...),
    user: dict = Depends(validate_api_key)
):
    """
    Endpoint for predicting a single deepfake audio.
    Requires API key in x-api-key header.
    """
    if "audio_model" not in models or "audio_processor" not in models:
        # Try to reload if missing
        try:
            from transformers import Wav2Vec2FeatureExtractor, AutoModelForAudioClassification
            models["audio_processor"] = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-base")
            models["audio_model"] = AutoModelForAudioClassification.from_pretrained("mo-thecreator/wav2vec2-base-finetuned")
        except:
            raise HTTPException(status_code=500, detail="Audio model is not loaded.")

    temp_file_path = ""
    try:
        # Save temp file
        with tempfile.NamedTemporaryFile(delete=False, suffix=file.filename) as temp_file:
            shutil.copyfileobj(file.file, temp_file)
            temp_file_path = temp_file.name

        print(f"Processing audio: {temp_file_path}")
        
        # Load audio using librosa (required for wav2vec2)
        import librosa
        import torch
        
        # Load audio and resample to 16kHz (required for wav2vec2)
        audio_array, sampling_rate = librosa.load(temp_file_path, sr=16000)
        
        print(f"Audio loaded: {len(audio_array)} samples at {sampling_rate}Hz")
        
        # Process audio with the processor
        processor = models["audio_processor"]
        model = models["audio_model"]
        
        inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt", padding=True)
        
        # Run inference
        with torch.no_grad():
            logits = model(**inputs).logits
        
        # Get probabilities using softmax
        probabilities = torch.nn.functional.softmax(logits, dim=-1)
        
        print(f"Raw logits: {logits}")
        print(f"Probabilities: {probabilities}")
        
        # Get the predicted class
        predicted_class_id = logits.argmax().item()
        predicted_label = model.config.id2label[predicted_class_id]
        
        # Get individual scores (id2label: {0: "fake", 1: "real"})
        fake_score = probabilities[0][0].item() * 100  # Index 0 = fake
        real_score = probabilities[0][1].item() * 100  # Index 1 = real
        
        print(f"Fake Score: {fake_score:.2f}%, Real Score: {real_score:.2f}%")
        print(f"Predicted: {predicted_label}")
        
        # Determine prediction
        if fake_score > real_score:
            prediction = "FAKE"
            confidence = fake_score
        else:
            prediction = "REAL"
            confidence = real_score

        return {
            "prediction": prediction,
            "confidence": round(confidence, 2),
            "fake_score": round(fake_score, 2),
            "real_score": round(real_score, 2),
            "raw": f"Fake: {fake_score:.2f}%, Real: {real_score:.2f}%"
        }

    except Exception as e:
        print(f"Audio prediction error: {e}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
    finally:
        # CRITICAL: Always clean up the temp file
        if os.path.exists(temp_file_path):
            os.remove(temp_file_path)

# --- 4. How to run this file for development ---
if __name__ == "__main__":
    print("--- Starting FastAPI server directly (for development) ---")
    print("--- Go to http://127.0.0.1:8000 for the API ---")
    uvicorn.run("main:app", host="127.0.0.1", port=8000, reload=True, app_dir="src")