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app.py

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+import tensorflow as tf
+import gradio as gr
+
+CLASS_NAMES = ['Cat', 'Dog', 'Rabbit']
+
+# Load and initialize tf lite model
+interpreter = tf.lite.Interpreter(model_path="classifier.tflite")
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+input_shape = input_details[0]['shape']
+
+def classify_image(image):
+    image = tf.image.resize(image, (224, 224))
+    input_data = tf.expand_dims(image, axis=0)
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+    interpreter.invoke()
+    predictions = interpreter.get_tensor(output_details[0]['index']).ravel()
+    confidences = {CLASS_NAMES[i]: float(predictions[i]) for i in range(len(CLASS_NAMES))}
+    return confidences
+
+gr.Interface(fn=classify_image, 
+             inputs=gr.Image(shape=(224,224)),                                    
+             outputs=gr.Label(num_top_classes=3),                              
+             examples=['Cat.jpg', 'Dog.jpg', 'Rabbit.jpg'],
+             cache_examples=True).launch()

classifier.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83016276ad0a48c1f589139d68fd18ad12b0b210cac0ffe0979420cacc03070b
+size 16064232

requirements.txt

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+tensorflow==2.8.4
+gradio==3.16.2