Update app.py

app.py

@@ -9,10 +9,10 @@ threshold = 0.75
 model = tf.saved_model.load("best_saved_model") #Loading the saved model
 
 def process_img(img): 
-    img = img.resize((640,640)) # resize the image as reqired for the model input
-    img = np.array(img)
-    copy_img = img.copy()
-    img = img/255                    # Normalizing the picel value 
+    img = img.resize((640,640))      # resize the image as reqired for the model input
+    img = np.array(img)              # Convert Pil Object into numpy array
+    copy_img = img.copy()            # Save a copy for backup
+    img = img/255                    # Normalizing the pixel value 
     img = img.astype("float32")      # Convert the format double to format float
     img = np.expand_dims(img,axis=0) # exapanding dimension to add batch
     return img,copy_img
@@ -35,7 +35,7 @@ if file_name is not None:
         if confidance[i] >= threshold:
             x1,y1,x2,y2 = bbox[i]*640
             class_name = labels[int(classes[i])]
-            st.text(class_name+" : "+str(confidance[i])+"%")
+            st.text(class_name+" : "+str(int(confidance[i]*100))+"%")
             if class_name =="Header":
                 color = (0,0,255) #Blue color
                 cv2.rectangle(copy_img, (int(x1), int(y1)), (int(x2), int(y2)),color, 2)