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import gradio as gr | |
import tensorflow as tf | |
import numpy as np | |
from PIL import Image | |
import tensorflow.keras as keras | |
from tensorflow.keras.models import load_model | |
# load model | |
model = load_model('model520.h5') | |
#prediction classes | |
classnames = ['paper', 'cardboard', 'plastic', 'metal', 'food', 'battery', 'shoes', 'clothes', 'glass', 'medical'] | |
#prediction function | |
def predict_image(img): | |
img_4d=img.reshape(-1,224, 224,3) | |
prediction=model.predict(img_4d)[0] | |
return {classnames[i]: float(prediction[i]) for i in range(len(classnames))} | |
#Gradio interface | |
image = gr.inputs.Image(shape=(224, 224)) | |
label = gr.outputs.Label(num_top_classes=3) | |
article="<p style='text-align: center; font-weight:bold;'>Model based on the VGG-16 CNN</p>" | |
gr.Interface(fn=predict_image, inputs=image, title="Garbage Classifier VGG-16", | |
description="This is a Garbage Classification Model Trained using VGG-16 architecture. Deployed to Hugging Face using Gradio.", outputs=label, article=article, enable_queue=True, interpretation='default').launch(share="True") |