Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+
+# Load the TFLite model
+interpreter = tf.lite.Interpreter(model_path='efficent_net50.tflite')
+interpreter.allocate_tensors()
+
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+def predict(image):
+    # Preprocess the input image to match model input shape
+    image = np.array(image, dtype=np.float32)  # Convert to float32
+    image = np.resize(image, (1, 224, 224, 3))  # Resize to [1, 224, 224, 3]
+
+    interpreter.set_tensor(input_details[0]['index'], image)
+    interpreter.invoke()
+    
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    return output_data.tolist()  # Return the prediction as a list
+
+iface = gr.Interface(fn=predict, inputs="image", outputs="label", title="TFLite Model Inference")
+iface.launch()