from flask import Flask, render_template, request, redirect, https://github.com/Shrinitha23/MediScan-Image-Analysis.git
from github.com import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
import os

app = Flask(__name__)
model = load_model('model/eye_disease_model.h5')  # Load your pre-trained model

# Allowed extensions for image uploads
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}

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

@app.route('/', methods=['GET', 'POST'])
def index():
    if request.method == 'POST':
        if 'file' not in request.files:
            return redirect(request.url)
        file = request.files['file']
        if file and allowed_file(file.filename):
            filepath = os.path.join('static/uploads', file.filename)
            file.save(filepath)
            # Predict the disease from the uploaded image
            prediction, confidence = predict_disease(filepath)
            return render_template('index.html', uploaded_image=filepath, prediction=prediction, confidence=confidence)
    return render_template('index.html')

def predict_disease(filepath):
    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)
    
    predictions = model.predict(img_array)
    class_index = np.argmax(predictions)
    confidence = np.max(predictions) * 100
    
    # Map index to disease label (change this according to your model's classes)
    classes = ['Cataract', 'Conjunctivitis', 'Glaucoma', 'Normal']
    prediction = classes[class_index]
    
    return prediction, confidence

if __name__ == '__main__':
    app.run(debug=True)