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import numpy as np |
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import tensorflow as tf |
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import gradio as gr |
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from PIL import Image |
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from io import BytesIO |
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import matplotlib.pyplot as plt |
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model = tf.keras.models.load_model("potatoesV3.h5") |
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CLASS_NAMES = ["Early Blight", "Late Blight", "Healthy"] |
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def classify_image(file): |
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response = requests.get(file) |
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img = Image.open(BytesIO(response.content)) |
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img_resized = img.convert("RGB").resize((256, 256)) |
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img_array = np.array(img_resized) / 255.0 |
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img_array = np.expand_dims(img_array, axis=0) |
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pred = model.predict(img_array) |
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predicted_class = CLASS_NAMES[np.argmax(pred[0])] |
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confidence = float(np.max(pred[0])) |
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return {"class": predicted_class, "confidence": confidence, "predict": pred[0]} |
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examples=[os.path.join(os.path.dirname(__file__),'7227b3db-c212-4370-8b42-443eea1577aa___RS_Early.B 7306.JPG','7456db33-766c-4a68-b924-ddf69d579981___RS_Early.B 6723.JPG','7486e823-64f7-4e43-ab51-26261b077fc2___RS_Early.B 6785.JPG','8829e413-5a7a-4680-b873-e71dfa9dbfe4___RS_LB 3974.JPG','9001b18c-b659-4c56-9dfb-0d0bf64a7b4a___RS_LB 4987.JPG','9009c86e-1205-4694-b0bb-ef7cf78dd104___RS_LB 3995.JPG','Potato_healthy-76-_0_2420.jpg','Potato_healthy-76-_0_6833.jpg','Potato_healthy-76-_0_7539.jpg')] |
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input_interface = gr.inputs.File(label="Upload an image file") |
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output_interface = gr.Textbox() |
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gr.Interface(fn=classify_image, inputs=input_interface, outputs=output_interface,Examples=examples).launch() |