app.py

import requests
import tensorflow as tf

inception_net = tf.keras.applications.mobilenet_v2.MobileNetV2()

import requests

# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")


def classify_image(inp):
  inp = inp.reshape((-1, 224, 224, 3))
  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
  prediction = inception_net.predict(inp).flatten()
  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
  return confidences
  

import gradio as gr

gr.Interface(fn=classify_image, 
             inputs=gr.inputs.Image(shape=(224, 224)),
             outputs=gr.outputs.Label(num_top_classes=3),
             examples=["banana.jpg", "car.jpg"],             
             theme="default",
             css=".footer{display:none !important}").launch()