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import gradio as gr |
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import tensorflow as tf |
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import numpy as np |
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from PIL import Image |
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import tensorflow.keras as keras |
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from tensorflow.keras.models import load_model |
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model = load_model('model_01.h5') |
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classnames = ['Pikachu','Raichu'] |
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def predict_image(img): |
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img_4d=img.reshape(-1,224, 224,3) |
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prediction=model.predict(img_4d)[0] |
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return {classnames[i]: float(prediction[i]) for i in range(2)} |
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image = gr.inputs.Image(shape=(224, 224)) |
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label = gr.outputs.Label(num_top_classes=2) |
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article="<p style='text-align: center'>Made by Aditya Narendra with 🖤</p>" |
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examples = ['battery.jpeg','cardboard.jpeg','paper.jpg','clothes.jpeg','metal.jpg','plastic.jpg','shoes.jpg'] |
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gr.Interface(fn=predict_image, inputs=image, title="Garbage Classifier V3", |
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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") |
