app.py

import requests
import tensorflow as tf

inception_net = tf.keras.applications.MobileNetV2()

import requests

response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")

title = "Image Classifier Three -- Keras Mobile Net"
description = """This machine has vision. It can see objects and concepts in an image. To test the machine, upload or drop an image, submit and read the results. The results comprise a list of words that the machine sees in the image. Beside a word, the length of the bar indicates the confidence with which the machine sees the word. The longer the bar, the more confident the machine is.
"""
article = "This app was made by following [this Gradio guide](https://gradio.app/image_classification_in_tensorflow/)."

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),
             title=title,
             description=description,
             article=article).launch()