Update app.py

app.py

@@ -7,12 +7,10 @@ labels = ["Column","Header","Table"]
 threshold = 0.75
 model = tf.saved_model.load("best_saved_model") #Loading the saved model
 
-def process_img(img_path):
-    img = cv2.imread(img_path)       # reading the image 
-    #copy_img = img.copy()            # save a copy of image to plot result
-    img = cv2.resize(img,(640,640))  # resize the image as reqired for the model input
+def process_img(img): 
+    img = img.resize(640,640) # resize the image as reqired for the model input
     copy_img = img.copy()
-    img = img/255                    # Normalizing the picel value 
+    img = np.array(img/255)                    # Normalizing the picel 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
@@ -22,8 +20,9 @@ file_name = st.file_uploader("Upload a repport image")
 print(type(file_name),file_name)
 if file_name is not None:
     col1, col2 = st.columns(2)
-
-    input_image,copy_image = process_img(file_name)
+    
+    image = Image.open(file_name)
+    input_image, copy_img= process_img(image)
     col1.image(input_image, use_column_width=True)
     
     bbox,confidance,classes,nc = model(input_img)