Update app.py

app.py

@@ -1,42 +1,46 @@
-import streamlit as st
 import cv2
-import tempfile
 from ultralytics import YOLO
-import numpy as np
+import streamlit as st
+from tempfile import NamedTemporaryFile
 
-# Title and Description
+# Load YOLOv8 model
+model = YOLO("yolov8n.pt")
+
+# Streamlit UI
 st.title("🔍 YOLOv8 Object Detection on Video")
-st.write("Upload a video file to detect objects using the YOLOv8 model. You can download the processed video with bounding boxes around detected objects.")
 
-# Sidebar for video upload
-uploaded_file = st.sidebar.file_uploader("Upload a video", type=["mp4", "avi", "mov"])
+# Upload video file
+uploaded_file = st.file_uploader("Upload Video", type=["mp4", "avi", "mov"])
 
 if uploaded_file is not None:
     # Save the uploaded video to a temporary file
-    temp_input_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    temp_input_file = NamedTemporaryFile(delete=False)
     temp_input_file.write(uploaded_file.read())
-    temp_input_file.close()
+    temp_input_file.flush()
 
     # Display the uploaded video
     st.video(temp_input_file.name)
 
-    # Process video button
-    if st.sidebar.button("Submit"):
-        # Load YOLOv8 model
-        model = YOLO("yolov8n.pt")
+    # Define the output video file path
+    temp_output_file = NamedTemporaryFile(delete=False, suffix='.mp4')
 
-        # Open the input video file
-        cap = cv2.VideoCapture(temp_input_file.name)
+    # Open the input video file
+    cap = cv2.VideoCapture(temp_input_file.name)
 
+    # Check if the video was opened successfully
+    if not cap.isOpened():
+        st.error("Error: Could not open video file.")
+    else:
         # Get video properties
         frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
         frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
         fps = cap.get(cv2.CAP_PROP_FPS)
 
         # Define codec and create VideoWriter object
-        temp_output_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
-        out = cv2.VideoWriter(temp_output_file.name, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for .mp4 files
+        out = cv2.VideoWriter(temp_output_file.name, fourcc, fps, (frame_width, frame_height))
 
+        # Process the video frame by frame
         while cap.isOpened():
             ret, frame = cap.read()
             if not ret:
@@ -45,7 +49,7 @@ if uploaded_file is not None:
             # Perform object detection
             results = model(frame)
 
-            # Access detection results and draw bounding boxes
+            # Access detection results
             if results:
                 for result in results:
                     boxes = result.boxes  # Access boxes attribute
@@ -65,14 +69,18 @@ if uploaded_file is not None:
         # Release resources
         cap.release()
         out.release()
+        cv2.destroyAllWindows()
+
+        st.success("Video processing complete!")
 
-        # Display the processed video
+        # Display the processed video in the browser
         st.video(temp_output_file.name)
 
-        # Provide download link for processed video
-        st.sidebar.download_button(
-            label="Download Processed Video",
-            data=open(temp_output_file.name, "rb"),
-            file_name="processed_video.mp4",
-            mime="video/mp4"
-        )
+        # Provide a download link for the processed video
+        with open(temp_output_file.name, 'rb') as file:
+            btn = st.download_button(
+                label="Download Processed Video",
+                data=file,
+                file_name="processed_video.mp4",
+                mime="video/mp4"
+            )