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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() |