Update app.py

app.py

@@ -1,7 +1,23 @@
 import gradio as gr
+import tensorflow as tf
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load the pre-trained model
+model = tf.keras.applications.MobileNetV2()
+labels_path = tf.keras.utils.get_file(
+    'ImageNetLabels.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')
+imagenet_labels = np.array(open(labels_path).read().splitlines())
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# Define the prediction function
+def classify_image(image):
+    image = tf.keras.applications.mobilenet_v2.preprocess_input(image)
+    predictions = model.predict(np.expand_dims(image, axis=0))
+    return {imagenet_labels[i]: float(predictions[0][i]) for i in range(1000)}
+
+# Create a Gradio interface
+inputs = gr.inputs.Image(shape=(224, 224))
+outputs = gr.outputs.Label(num_top_classes=3)
+interface = gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, capture_session=True)
+
+# Launch the interface
+interface.launch()