import os
import json
import io
import base64
from datetime import datetime
import numpy as np
import torch
import torch.nn as nn
from torchvision import transforms, models
import joblib
from PIL import Image
from flask import Flask, request, jsonify
from flask_cors import CORS
from supabase import create_client, Client

app = Flask(__name__)
CORS(app)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

MODEL_DIR = os.path.join(os.path.dirname(__file__), "models")
model_path = os.path.join(MODEL_DIR, "svm_densenet201_rbf.joblib")
meta_path = os.path.join(MODEL_DIR, "metadata.json")

svm_model = None
class_names = None
IMG_SIZE = 224

supabase_url = os.environ.get('SUPABASE_URL')
supabase_key = os.environ.get('SUPABASE_ANON_KEY')
supabase: Client = None

if supabase_url and supabase_key:
    try:
        supabase = create_client(supabase_url, supabase_key)
        print("✓ Supabase client initialized")
    except Exception as e:
        print(f"⚠ Failed to initialize Supabase: {e}")
        supabase = None
else:
    print("⚠ Supabase credentials not found, predictions won't be saved to database")

def load_model():
    global svm_model, class_names, IMG_SIZE

    try:
        if os.path.exists(model_path):
            svm_model = joblib.load(model_path)
            print("✓ SVM model loaded successfully")
        else:
            print(f"⚠ Model file not found at {model_path}")
            print("  Using simulation mode until model is uploaded")
            svm_model = None

        if os.path.exists(meta_path):
            with open(meta_path, "r") as f:
                meta = json.load(f)
                class_names = meta.get("class_names", ["3 Bulan", "6 Bulan", "9 Bulan"])
                IMG_SIZE = meta.get("img_size", 224)
                print(f"✓ Metadata loaded: {class_names}")
        else:
            class_names = ["3 Bulan", "6 Bulan", "9 Bulan"]
            print(f"⚠ Metadata not found, using default classes: {class_names}")

    except Exception as e:
        print(f"Error loading model: {str(e)}")
        svm_model = None
        class_names = ["3 Bulan", "6 Bulan", "9 Bulan"]

densenet = models.densenet201(weights=models.DenseNet201_Weights.DEFAULT)
densenet.eval()
feature_extractor = densenet.features.to(device)
gap = nn.AdaptiveAvgPool2d((1, 1)).to(device)

eval_tfms = transforms.Compose([
    transforms.Resize((IMG_SIZE, IMG_SIZE)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406],
                        [0.229, 0.224, 0.225]),
])

def decode_base64_image(base64_string):
    if ',' in base64_string:
        base64_string = base64_string.split(',')[1]

    image_data = base64.b64decode(base64_string)
    image = Image.open(io.BytesIO(image_data)).convert("RGB")
    return image

def preprocess_image(image):
    x = eval_tfms(image).unsqueeze(0)
    return x

@torch.no_grad()
def extract_features(img_tensor):
    img_tensor = img_tensor.to(device)
    feats = feature_extractor(img_tensor)
    feats = torch.relu(feats)
    feats = gap(feats)
    feats = feats.view(feats.size(0), -1)
    return feats.cpu().numpy()

def simulate_prediction():
    probabilities = np.random.dirichlet(np.ones(len(class_names)), size=1)[0]
    pred_idx = int(np.argmax(probabilities))
    pred_label = class_names[pred_idx]
    confidence = float(probabilities[pred_idx])

    return pred_label, confidence, probabilities

def predict_with_model(features):
    proba = svm_model.predict_proba(features)[0]
    pred_idx = int(np.argmax(proba))
    pred_label = class_names[pred_idx]
    confidence = float(proba[pred_idx])

    return pred_label, confidence, proba

@app.route('/health', methods=['GET'])
def health_check():
    return jsonify({
        'status': 'healthy',
        'model_loaded': svm_model is not None,
        'device': str(device),
        'classes': class_names
    })

def save_to_database(pred_label, confidence, prob_dict, mode, image_data_url=None):
    if not supabase:
        return None

    try:
        prediction_data = {
            'predicted_class': pred_label,
            'confidence': confidence,
            'probabilities': prob_dict,
            'mode': mode,
            'created_at': datetime.utcnow().isoformat()
        }

        if image_data_url:
            prediction_data['image_data'] = image_data_url[:1000]

        result = supabase.table('predictions').insert(prediction_data).execute()
        return result.data[0] if result.data else None
    except Exception as e:
        print(f"⚠ Failed to save to database: {e}")
        return None

@app.route('/classify', methods=['POST'])
def classify_image():
    try:
        data = request.json

        if not data or 'image' not in data:
            return jsonify({'error': 'No image data provided'}), 400

        image_base64 = data['image']
        image = decode_base64_image(image_base64)

        img_tensor = preprocess_image(image)

        if svm_model is not None:
            features = extract_features(img_tensor)
            pred_label, confidence, probabilities = predict_with_model(features)
        else:
            pred_label, confidence, probabilities = simulate_prediction()

        prob_dict = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}
        mode = 'real' if svm_model is not None else 'simulation'

        db_record = save_to_database(pred_label, confidence, prob_dict, mode, data['image'])

        response = {
            'predicted_class': pred_label,
            'confidence': confidence,
            'probabilities': prob_dict,
            'mode': mode
        }

        if db_record:
            response['id'] = db_record.get('id')
            response['saved_to_db'] = True
        else:
            response['saved_to_db'] = False

        return jsonify(response)

    except Exception as e:
        return jsonify({
            'error': 'Classification failed',
            'message': str(e)
        }), 500

@app.route('/reload-model', methods=['POST'])
def reload_model():
    try:
        load_model()
        return jsonify({
            'status': 'success',
            'model_loaded': svm_model is not None,
            'classes': class_names
        })
    except Exception as e:
        return jsonify({
            'status': 'error',
            'message': str(e)
        }), 500

if __name__ == '__main__':
    os.makedirs(MODEL_DIR, exist_ok=True)
    load_model()

    port = int(os.environ.get('PORT', 5000))
    app.run(host='0.0.0.0', port=port, debug=False)