Update app.py

app.py

@@ -42,8 +42,16 @@ if video_file is not None:
     tfile = tempfile.NamedTemporaryFile(delete=False)
     tfile.write(video_file.read())
 
+    # Open video capture using temporary file path
     # Open video capture using temporary file path
     cap = cv2.VideoCapture(tfile.name)
+    original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    # Set the target width and height based on the conditions
+    target_width = int(original_width * 0.65) if original_width <= 1920 else int(original_width * 0.5)
+    target_height = int(original_height * 0.65) if original_width <= 1920 else int(original_height * 0.5)
+
     alert_set = set(alerting_classes.keys())
     alert_set.remove(0)
 
@@ -67,6 +75,9 @@ if video_file is not None:
             # st.warning("Can't receive frame (stream end?). Exiting ...")
             break
 
+        # Resize the frame
+        resized_frame = cv2.resize(frame, (target_width, target_height))
+
         if frame_counter % 4 == 0:  # Perform inference on every 4th frame
             alert_flag = False
             alert_reason = []
@@ -127,12 +138,9 @@ if video_file is not None:
     cap.release()
     tfile.close()
 
-    # Display frames one by one as a video with progress bar
-    progress_bar_display = st.progress(0)
+    # Display frames one by one as a video
     for i, frame in enumerate(frames):
         video_placeholder.image(frame, channels="BGR", caption="YOLOv8 Inference")
-        # Update display progress bar
-        progress_bar_display.progress((i + 1) / len(frames))
         time.sleep(frame_delay)
 
 st.markdown("<hr>", unsafe_allow_html=True)