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() |