Update app.py

app.py

@@ -77,43 +77,58 @@ elif input_option == "Use Webcam":
     # Release the camera
     camera.release()
 
+import streamlit as st
+import cv2
+
+# Your fire detection model should be loaded here, e.g., `model = load_your_model()`
+
 elif input_option == "Upload Video":
-    uploaded_video = st.file_uploader("choose a video",type=["mp4","avi","mov","mkv"])
+    uploaded_video = st.file_uploader("Choose a video", type=["mp4", "avi", "mov", "mkv"])
     if uploaded_video is not None:
         # Save the uploaded video temporarily
         temp_video_path = "temp_video.mp4"
         with open(temp_video_path, "wb") as f:
             f.write(uploaded_video.read())
 
+        # Display the uploaded video
+        st.video(temp_video_path)
+
         # Open the video file
         video_capture = cv2.VideoCapture(temp_video_path)
 
-        # Create a placeholder for the video frames
+        # Create a placeholder for video frame processing
         video_frame_placeholder = st.empty()
+        fire_detected = False
 
         # Loop through video frames
         while video_capture.isOpened():
             ret, frame = video_capture.read()
             if not ret:
-                st.write("Finished processing video.")
                 break
 
-            # Make predictions
+            # Make predictions using your fire detection model
             results = model.predict(source=frame, conf=0.5)
 
-            # Draw bounding boxes on the frame
+            # Draw bounding boxes on the frame if fire is detected
             for result in results:
                 boxes = result.boxes.xyxy
                 for box in boxes:
                     x1, y1, x2, y2 = box[:4].astype(int)
                     frame = cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                    fire_detected = True  # Set fire_detected flag if a bounding box is found
 
-            # Convert frame to RGB
+            # Convert the frame to RGB format
             rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
-            # Display the frame
+            # Display the processed frame
             video_frame_placeholder.image(rgb_frame, channels="RGB", use_column_width=True)
 
+        # Display detection result
+        if fire_detected:
+            st.write("Fire detected in the video.")
+        else:
+            st.write("No fire detected in the video.")
+
         # Release the video capture
         video_capture.release()