Update app.py

app.py

@@ -1,20 +1,16 @@
-
 import gradio as gr
-
 import tensorflow 
-from tensorflow.keras.models import  load_model
+from tensorflow.keras.models import load_model
 
-model = load_model('../input/cnn-model/cnn_model.h5') # هنا احمل المودل بعد ما دربته وخزنتة حته اشوف قدرته على التصنيف 
-
-class_names=['benign','malignant','normal']
+model = load_model('../input/cnn-model/cnn_model.h5') # aqui carregamos o modelo que treinamos e salvamos para avaliar sua capacidade de classificação
+class_names = ['benign', 'malignant', 'normal']
 
 def predict_image(img):
-    img_4d=img.reshape(-1,128,128,3)
-    img_4d=img_4d/255
-    prediction=model.predict(img_4d)[0]
-    return {class_names[i]: float(prediction[i]) for i in range(3)}# range 1 if sigmoid , range=number of class if softmax
-    
-image = gr.inputs.Image(shape=(128,128))
+    img_4d = img.reshape(-1, 128, 128, 3)
+    img_4d = img_4d/255
+    prediction = model.predict(img_4d)[0]
+    return {class_names[i]: float(prediction[i]) for i in range(3)}
+
+image = gr.inputs.Image(shape=(128, 128))
 label = gr.outputs.Label(num_top_classes=3)
-gr.Interface(fn=predict_image, inputs=image,
-             outputs=label).launch(debug='False',share=True)
+gr.Interface(fn=predict_image, inputs=image, outputs=label).launch(debug=False, share=True)