Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load your trained Xception model
+model = tf.keras.models.load_model("your-huggingface-username/your-model-name")
+
+def classify_image(img):
+    # Preprocess the image
+    img = img.resize((299, 299))  # Resize to the input shape of Xception
+    img_array = image.img_to_array(img)  # Convert image to array
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array /= 255.0  # Normalize the image
+
+    # Make prediction
+    predictions = model.predict(img_array)
+    predicted_class = np.argmax(predictions, axis=-1)[0]  # Get the predicted class index
+    
+    return str(predicted_class)  # Return the class index as a string
+
+# Set up Gradio interface
+iface = gr.Interface(fn=classify_image, inputs=gr.inputs.Image(type="pil"), outputs="text")
+
+iface.launch()