Update app.py

app.py

@@ -19,7 +19,7 @@ def process_img(img):
 
 st.title("Table Extract")
 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)
     
@@ -31,22 +31,31 @@ if file_name is not None:
     bbox,confidance,classes,nc = model(input_image)
     bbox,confidance , classes , nc  = bbox[0].numpy(),confidance[0].numpy(),classes[0].numpy(),nc[0].numpy()
     st.subheader("Detected Result")
+    table_count =0
+    header_count = 0
+    column_count = 0
     for i in range(nc):
         if confidance[i] >= threshold:
             x1,y1,x2,y2 = bbox[i]*640
             class_name = labels[int(classes[i])]
             st.text(class_name+" : "+str(int(confidance[i]*100))+"%")
             if class_name =="Header":
+                header_count+=1
                 color = (0,0,255) #Blue color
                 cv2.rectangle(copy_img, (int(x1), int(y1)), (int(x2), int(y2)),color, 2)
     
             if class_name =="Column":
+                column_count+=1
                 color = (0,255,0) #Green color
                 cv2.rectangle(copy_img, (int(x1), int(y1)), (int(x2), int(y2)),color, 2)
     
             if class_name =="Table":
+                table_count+=1
                 color = (255,0,0) #Red color
                 cv2.rectangle(copy_img, (int(x1), int(y1)), (int(x2), int(y2)),color, 2)
+    st.text("No of Table  Detected : "+str(table_count))
+    st.text("No of Header Detected : "+str(header_count))
+    st.text("No of Column Detected : "+str(column_count))
     col2.header("Output Result")
     col2.image(copy_img, use_column_width=True)