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from tensorflow.keras.models import load_model
import gradio as gr
from PIL import Image
import numpy as np
model = load_model('Pikachu_and_Raichu.h5')
labels = ['Pikachu', 'Raichu']
def predict(img):
img=img.reshape(160,160,3)
"""images_list = []
images_list.append(np.array(img))
x = np.asarray(images_list)"""
#prediction = model.predict(img)[0]
prediction = model.predict(img).flatten()
confidences = {labels[i]: float(prediction[i]) for i in range(2)}
return confidences
image = gr.inputs.Image(shape=(160, 160))
label = gr.outputs.Label(num_top_classes=2)
gr.Interface(fn=predict, inputs=image, title="Garbage Classifier",
description="Tradio.",outputs=label,interpretation='default').launch(debug=True,enable_queue=True) |