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Upload app.py

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  1. app.py +29 -0
app.py ADDED
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+ import gradio as gr
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+ #from transformers import pipeline
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+ from tensorflow.keras.models import load_model
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+
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+ #pipe = pipeline(task="image-classification", model="SuperSecureHuman/Flower-CNN")
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+
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+ model = load_model('./model.h5')
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+
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+
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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 {class_names[i]: float(prediction[i]) for i in range(3)}
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+
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+
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+ class_names = ['Crescentia_Cujete', 'Fiddle_Wood', 'Gold_Apple', 'Hill_Mango', 'Indian_Tulip_Tree', 'Mahagony', 'Pala_Indigo_Plant', 'Spanish_Cherry', 'Teak', 'Yellow_Trumpet']
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+
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+ image = gr.inputs.Image(shape=(224, 224))
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+
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+ label = gr.outputs.Label(num_top_classes=3)
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+
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+ gr.Interface(fn=predict_image,
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+ title="Tree Classification",
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+ description="Tree CNN",
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+ inputs=image,
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+ outputs=label,
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+ live=True,
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+ interpretation='default',
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+ allow_flagging="never").launch()