import tensorflow as tf
#inception_net = tf.keras.applications.MobileNetV2()


import requests

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

model.load("./Pikachu_and_Raichu.h5")

def classify_image(inp):
  inp = inp.reshape((-1, 224, 224, 3))
  inp = model(inp)
  prediction = model(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.Image(shape=(224, 224)),
             outputs=gr.Label(num_top_classes=2)).launch()