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from tensorflow.keras.models import load_model
import gradio as gr
import PIL.Image as Image
import numpy as np
model = load_model('DS11Sudhanva.h5')
classnames = ['cardboard', 'metal','paper','plastic','trash','green-glass','white-glass','brown-glass','clothes','biological','battery','shoes']
def predict(img):
img=img.reshape(-1,298, 384,3)
prediction = model.predict(img[0])
return {classnames[i]: float(prediction[i]) for i in range(len(classnames)}
image = gr.inputs.Image(shape=(298, 384))
label = gr.outputs.Label(num_top_classes=3)
gr.Interface(fn=predict, inputs=image, title="Garbage Classifier",
description="This is a Garbage Classification Model Trained using Dataset 11 by Sud.Deployed to Hugging Faces using Gradio.",outputs=label,interpretation='default').launch()
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