app.py

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()