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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.vgg16 as vgg16 | |
from tensorflow.keras.models import load_model | |
# load model | |
model = load_model('model6904.h5') | |
classnames = ['battery','cardboard','clothes','food','glass','medical','metal','paper','plastic','shoes'] | |
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(10)} | |
image = gr.inputs.Image(shape=(224, 224)) | |
label = gr.outputs.Label(num_top_classes=2) | |
article="<p style='text-align: center'>Made by Aditya Narendra with 🖤</p>" | |
gr.Interface(fn=predict_image, inputs=image, title="Garbage Classifier V4-VGG16+SVM", | |
description="This is a Garbage Classification Model Trained using VGG16+SVM(20 Epochs).Deployed to Hugging Faces using Gradio.",outputs=label,article=article,enable_queue=True,interpretation='default').launch(share="True") |