Update app.py

app.py

@@ -16,7 +16,7 @@ st.markdown(
     """
     <style>
     .stApp {
-        background-color: #f5f5f5;
+        background-color: #cdc0b0;
         color: #1a1a1a;
     }
     h1 {
@@ -90,30 +90,50 @@ with tabs[0]:
         uploaded_file = st.file_uploader("", type=["jpg", "jpeg", "png", "mp4"], label_visibility="collapsed")
         confidence = st.slider("Detection Threshold", 0.25, 1.0, 0.4, key="upload_conf")
     with col2:
-        frame_placeholder = st.empty()  # Pre-create placeholder
+        frame_placeholder = st.empty()
         status_placeholder = st.empty()
         if uploaded_file:
-            file_type = uploaded_file.type.split('/')[0]
-            if file_type == 'image':
-                image = PIL.Image.open(uploaded_file)
-                results = model.predict(image, conf=confidence)
-                detected_image = results[0].plot()[:, :, ::-1]
-                frame_placeholder.image(detected_image, use_column_width=True)
-                status_placeholder.write(f"Objects detected: {len(results[0].boxes)}")
-            elif file_type == 'video':
-                tfile = tempfile.NamedTemporaryFile(delete=False)
-                tfile.write(uploaded_file.read())
-                cap = cv2.VideoCapture(tfile.name)
-                while cap.isOpened():
-                    ret, frame = cap.read()
-                    if not ret:
-                        break
-                    results = model.predict(frame, conf=confidence)
-                    detected_frame = results[0].plot()[:, :, ::-1]
-                    frame_placeholder.image(detected_frame, use_column_width=True)
+            try:
+                file_type = uploaded_file.type.split('/')[0]
+                status_placeholder.write(f"Processing {file_type} file...")
+                
+                if file_type == 'image':
+                    image = PIL.Image.open(uploaded_file)
+                    results = model.predict(image, conf=confidence)
+                    detected_image = results[0].plot()[:, :, ::-1]
+                    frame_placeholder.image(detected_image, use_column_width=True)
                     status_placeholder.write(f"Objects detected: {len(results[0].boxes)}")
-                    time.sleep(0.05)
-                cap.release()
+                
+                elif file_type == 'video':
+                    # Save uploaded file to temporary location
+                    tfile = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
+                    tfile.write(uploaded_file.read())
+                    tfile.close()
+                    
+                    # Open video with OpenCV
+                    cap = cv2.VideoCapture(tfile.name)
+                    if not cap.isOpened():
+                        status_placeholder.error("Failed to open video file.")
+                    else:
+                        frame_count = 0
+                        while cap.isOpened():
+                            ret, frame = cap.read()
+                            if not ret:
+                                status_placeholder.write(f"Finished processing video. Processed {frame_count} frames.")
+                                break
+                            results = model.predict(frame, conf=confidence)
+                            detected_frame = results[0].plot()[:, :, ::-1]
+                            frame_placeholder.image(detected_frame, use_column_width=True)
+                            status_placeholder.write(f"Frame {frame_count}: Objects detected: {len(results[0].boxes)}")
+                            frame_count += 1
+                            time.sleep(0.05)  # Control playback speed
+                        cap.release()
+                    # Clean up temporary file
+                    import os
+                    os.unlink(tfile.name)
+                    
+            except Exception as e:
+                status_placeholder.error(f"Error processing file: {str(e)}")
 
 # Tab 2: Webcam
 with tabs[1]: