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

app = Flask(__name__)

# Configuration
UPLOAD_FOLDER = 'static/uploads'
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

# Create upload folder if it doesn't exist
os.makedirs(UPLOAD_FOLDER, exist_ok=True)

# Load model once at startup
model = None

def get_model():
    global model
    if model is None:
        model = load_model('brain_tumor_model.h5')
    return model

def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

def preprocess_image(img_path, target_size=(150, 150)):  # ← Update this!
    """Preprocess image for model prediction"""
    img = image.load_img(img_path, target_size=target_size)
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array = img_array / 255.0  # Normalize
    return img_array

@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"}), 400
    
    file = request.files['file']
    
    if file.filename == '':
        return jsonify({"error": "No file selected"}), 400
    
    if file and allowed_file(file.filename):
        filename = secure_filename(file.filename)
        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        file.save(filepath)
        
        try:
            # Preprocess and predict
            model = get_model()
            processed_image = preprocess_image(filepath)
            prediction = model.predict(processed_image)
            
            # Adjust this based on your model's output
            # For binary classification:
            if prediction[0][0] > 0.5:
                result = "Tumor Detected"
                confidence = float(prediction[0][0]) * 100
            else:
                result = "No Tumor Detected"
                confidence = (1 - float(prediction[0][0])) * 100
            
            return jsonify({
                "success": True,
                "prediction": result,
                "confidence": f"{confidence:.2f}%",
                "image_path": filepath
            })
            
        except Exception as e:
            return jsonify({"error": str(e)}), 500
    
    return jsonify({"error": "Invalid file type. Use PNG, JPG, or JPEG"}), 400

if __name__ == '__main__':
    print("Loading model...")
    get_model()
    print("Model loaded successfully!")
    app.run(host="0.0.0.0",port=7860,debug=False)