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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 | |
import keras.applications.mobilenet_v2 as mobilenetv2 | |
from tensorflow.keras.models import load_model | |
# load model | |
model = load_model('model18.h5') | |
classnames = ['battery','biological','brown-glass','cardboard','clothes','green-glass','metal','paper','plastic','shoes','trash','white-glass'] | |
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(12)} | |
image = gr.inputs.Image(shape=(224, 224)) | |
label = gr.outputs.Label(num_top_classes=3) | |
article="<p style='text-align: center'>Made by Aditya Narendra with π€</p>" | |
examples = ['battery.jpeg','cardboard.jpeg','paper.jpg','clothes.jpeg','metal.jpg','plastic.jpg','shoes.jpg'] | |
gr.Interface(fn=predict_image, inputs=image, title="Garbage Classifier V3", | |
description="This is a Garbage Classification Model Trained using MobileNetV2.Deployed to Hugging Faces using Gradio.",outputs=label,examples=examples,article=article,enable_queue=True,interpretation='default').launch(share="True") |