Update app.py

app.py

@@ -10,11 +10,9 @@ import tensorflow as tf
 from tensorflow.keras import backend as K
 from tensorflow.keras.models import load_model
 from tensorflow.keras.applications.imagenet_utils import preprocess_input
-from tensorflow.keras.applications.imagenet_utils import preprocess_input
 
 from sklearn.metrics import f1_score
 
-
 import gradio as gr
 
 
@@ -23,13 +21,13 @@ sns.set_style('darkgrid')
 if not sys.warnoptions:
     import warnings
     warnings.simplefilter("ignore")
-pd.set_option('display.max_columns', None)  # or 1000
-pd.set_option('display.max_rows', None)  # or 1000
-pd.set_option('display.max_colwidth', None)  # or 199
+pd.set_option('display.max_columns', None)
+pd.set_option('display.max_rows', None)
+pd.set_option('display.max_colwidth', None)
 print('Modules loaded')
 
 
-def F1_score(y_true, y_pred):  # taken from old keras source code
+def F1_score(y_true, y_pred):  
     true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
     possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
     predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
@@ -43,7 +41,7 @@ model = load_model(r"Model\Model.h5",
                    custom_objects={"F1_score": f1_score})
 
 
-def recog_model(img_path):
+def image_recognition(img_path):
     img = cv2.imread(img_path)
     img = cv2.resize(img, (250, 224))
     x = np.expand_dims(img, axis=0)
@@ -51,14 +49,12 @@ def recog_model(img_path):
 
     prediction = model.predict(x)
 
-    # convert the prediction to a class label
     classes = ['Tumor', 'Cyst', 'Normal', 'Stone']
     predicted_class = classes[np.argmax(prediction[0])]
     confidence = str(100 * (np.max(prediction[0])))
-    return (str(predicted_class+" detected with a confidence of "+confidence+"%"))
-
+    return str(predicted_class + " detected with a confidence of " + confidence + "%")
 
-demo = gr.Interface(fn=recog_model, inputs=gr.Image(image_mode="L", type="filepath", label="Input Image"),
-                    outputs=gr.Label(label="Model Prediction"), allow_flagging="never", examples=[r"demo\Cyst.jpg", r"demo\Normal.jpg", r"demo\Stone.jpg", r"demo\Tumor.jpg"])
 
-demo.launch()
+app = gr.Interface(fn=image_recognition, inputs=gr.Image(image_mode="L", type="filepath", label="Input Image"),
+                   outputs=gr.Label(label="Model Prediction"), allow_flagging="never", examples=[r"demo\Cyst.jpg", r"demo\Normal.jpg", r"demo\Stone.jpg", r"demo\Tumor.jpg"], title="Sistema de Reconhecimento de Imagens Médicas")
+app.launch()