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
+
+# Load the model (make sure the file is named my_modal.h5)
+model = tf.keras.models.load_model("my_model.h5")
+class_names = ['Class 1', 'Class 2', 'Class 3']  # Update with your real classes
+
+# Define prediction function
+def predict(image):
+    image = image.resize((224, 224))  # Resize to model input shape
+    img_array = np.array(image) / 255.0
+    img_array = img_array.reshape((1, 224, 224, 3))
+    prediction = model.predict(img_array)
+    predicted_class = class_names[np.argmax(prediction)]
+    confidence = float(np.max(prediction))
+    return {predicted_class: confidence}
+
+# Create Gradio interface
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="My ML Model"
+).launch()