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