Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from io import BytesIO
+
+# Load your trained model
+model = tf.keras.models.load_model("best_model_weights.h5")  # Replace with the path to your saved model
+
+# Define the image classification function
+def classify_image(input_image):
+    # Preprocess the input image
+    input_image = Image.open(BytesIO(input_image))
+    input_image = input_image.resize((img_width, img_height))
+    input_image = np.array(input_image) / 255.0  # Normalize pixel values
+
+    # Make a prediction using the model
+    predictions = model.predict(np.expand_dims(input_image, axis=0))
+
+    # Get the class label with the highest probability
+    class_index = np.argmax(predictions)
+    class_prob = predictions[0][class_index]
+
+    # Define class labels (you can replace these with your actual class labels)
+    class_labels = ["Normal", "Cataract"]
+
+    # Get the class label
+    class_label = class_labels[class_index]
+
+    return f"Predicted Class: {class_label} (Probability: {class_prob:.2f})"
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.inputs.Image(shape=(img_height, img_width)),
+    outputs="text",
+    live=True,
+    title="Image Classifier"
+)
+
+# Run the Gradio interface
+iface.launch()