Create app.py

app.py

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+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()