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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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import os |
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import tensorflow as tf |
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import numpy as np |
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from keras.models import load_model |
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from tensorflow.keras.utils import load_img |
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model = load_model('model_multi.h5') |
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def format_decimal(value): |
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decimal_value = format(value, ".2f") |
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return decimal_value |
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def detect(img): |
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img = np.expand_dims(img, axis=0) |
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img = img/255 |
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prediction = model.predict(img)[0] |
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if format_decimal(prediction[0]) >= "0.5": |
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return "Risque d'infection bactérienne" |
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if format_decimal(prediction[1]) >= "0.5": |
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return "Poumon sain" |
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if format_decimal(prediction[2]) >= "0.5": |
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return "Risque d'infection biologique" |
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os.system("tar -zxvf examples.tar.gz") |
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examples = ['examples/n1.jpeg', 'examples/n2.jpeg', 'examples/n3.jpeg', 'examples/n4.jpeg', 'examples/n5.jpeg', |
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'examples/n6.jpeg', 'examples/n7.jpeg', 'examples/n8.jpeg', 'examples/p6.jpeg', 'examples/p7.jpeg',] |
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input = gr.inputs.Image(shape=(100,100)) |
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title = "PneumoDetect: Pneumonia Detection from Chest X-Rays" |
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iface = gr.Interface(fn=detect, inputs=input, outputs="text",examples = examples, examples_per_page=20, title=title) |
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iface.launch(inline=False) |