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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 cv2
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new_model = tf.keras.models.load_model('best_model_mammography.h5')
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def classify_image(file_name):
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img1 = cv2.imread(file_name.name.replace("\\",'/'),0)
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img = cv2.resize(img1, (224,224))
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img = img.reshape(img.shape[0],img.shape[1],1)
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pred = new_model.predict(np.array([img]))
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pred = np.round(pred,1)
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if pred == 0:
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pred = "V么tre cancer est Begnine"
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else:
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pred= "V么tre cancer est maline"
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return pred
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image = gr.inputs.File( file_count="single",type="file", label="Fichier 脿 Traiter (sous fichier .pgm)")
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gr.Interface(
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fn=classify_image,
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inputs=image,
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outputs="text",
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interpretation="default",
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live=True,
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theme="dark-peach",
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title="API BREASTNET de Test de diagnostique du Cancer de Sein",
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description="Cette API est utilis茅 pour dire si le Cancer de sein est Maline ou Pas"
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).launch()
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