from flask import Flask, render_template, request, jsonify
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np
import os
from werkzeug.utils import secure_filename

app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'static/uploads'
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)

# Load model
MODEL_PATH = 'bird_vs_drone_model.h5'
model = None

def get_model():
    global model
    if model is None:
        if os.path.exists(MODEL_PATH):
            model = tf.keras.models.load_model(MODEL_PATH)
        elif os.path.exists('final_model.h5'):
            model = tf.keras.models.load_model('final_model.h5')
    return model

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/predict', methods=['POST'])
def predict():
    if 'file' not in request.files:
        return jsonify({'error': 'No file uploaded'})
    
    file = request.files['file']
    if file.filename == '':
        return jsonify({'error': 'No file selected'})
    
    if file:
        filename = secure_filename(file.filename)
        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        file.save(filepath)
        
        # Preprocess
        img = image.load_img(filepath, target_size=(224, 224))
        img_array = image.img_to_array(img) / 255.0
        img_array = np.expand_dims(img_array, axis=0)
        
        # Predict
        m = get_model()
        if m is None:
            return jsonify({'error': 'Model not found. Please train the model first.'})
            
        prediction = m.predict(img_array)[0][0]
        
        # Result
        # Class index 0 is Bird, 1 is Drone (based on our generator)
        # Binary generator usually sorts class names alphabetically: ['Bird', 'Drone']
        # So Bird = 0, Drone = 1.
        # prediction > 0.5 means Drone
        
        label = 'Drone' if prediction > 0.5 else 'Bird'
        confidence = float(prediction) if label == 'Drone' else float(1 - prediction)
        
        return jsonify({
            'label': label,
            'confidence': f"{confidence*100:.2f}%",
            'image_url': f"/static/uploads/{filename}"
        })

if __name__ == '__main__':
    port = int(os.environ.get('PORT', 7860))
    app.run(host='0.0.0.0', port=port)