app.py

import gradio as gr
import tensorflow as tf
import numpy as np

IMAGE_SIZE = 256

# Load the saved model
model = tf.keras.models.load_model('/content/drive/MyDrive/Diabetic /RestNet_model/my_model.h5')

# Define class labels (adjust this according to your specific classes)
class_labels = ['Mild', 'Moderate', 'No_DR', 'Proliferate_DR', 'Severe']

def predict(image):
    # Preprocess the image to the required size and scale
    image = tf.image.resize(image, (IMAGE_SIZE, IMAGE_SIZE))
    image = np.expand_dims(image, axis=0)  # Add batch dimension

    # Make prediction
    predictions = model.predict(image)
    confidence = np.max(predictions)
    predicted_class = class_labels[np.argmax(predictions)]

    return predicted_class, float(confidence)

# Create the Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Label(num_top_classes=1), gr.Number(label="Confidence")],
    title="Early Diabetic Retinopathy Detection",
    description="Upload an image and get the predicted class along with confidence score."
)

# Launch the interface
interface.launch()