Update app.py

app.py

@@ -86,7 +86,7 @@ with tabs[0]:
     col1, col2 = st.columns([1, 1])
     with col1:
         st.markdown("**Add Your File**")
-        st.write("Upload an image or video to scan for fire or smoke.")
+        st.write("Upload an image or video (max 200MB) to scan for fire or smoke.")
         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:
@@ -94,44 +94,50 @@ with tabs[0]:
         status_placeholder = st.empty()
         if uploaded_file:
             try:
-                file_type = uploaded_file.type.split('/')[0]
-                status_placeholder.write(f"Processing {file_type} file...")
+                # Log file details
+                file_size_mb = uploaded_file.size / 1024 / 1024
+                status_placeholder.write(f"File size: {file_size_mb:.2f} MB, Type: {uploaded_file.type}")
                 
-                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':
-                    # Save uploaded file to temporary location
-                    tfile = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
-                    tfile.write(uploaded_file.read())
-                    tfile.close()
+                if file_size_mb > 200:
+                    status_placeholder.error("File exceeds 200MB limit. Please use a smaller file.")
+                else:
+                    file_type = uploaded_file.type.split('/')[0]
+                    status_placeholder.write(f"Processing {file_type} file...")
                     
-                    # 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)
+                    if file_type == 'image':
+                        image = PIL.Image.open(uploaded_file)
+                        results = model.predict(image, conf=confidence)
+                        detected_frame = results[0].plot()[:, :, ::-1]
+                        frame_placeholder.image(detected_frame, use_column_width=True)
+                        status_placeholder.write(f"Objects detected: {len(results[0].boxes)}")
                     
+                    elif file_type == 'video':
+                        # Save to temporary file
+                        tfile = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
+                        tfile.write(uploaded_file.read())
+                        tfile.close()
+                        
+                        # Process video
+                        cap = cv2.VideoCapture(tfile.name)
+                        if not cap.isOpened():
+                            status_placeholder.error("Failed to open video file. Check format or codec.")
+                        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)
+                            cap.release()
+                        # Clean up
+                        import os
+                        os.unlink(tfile.name)
             except Exception as e:
                 status_placeholder.error(f"Error processing file: {str(e)}")