Update app.py

app.py

@@ -27,7 +27,11 @@ if input_option == "Upload Image":
 
         # Make predictions
         results = model.predict(source=img_np, conf=0.5)
-
+        
+        if Fire_Detected= True:
+            print("Fire Detected")
+        else:
+            print("Not")
         # Draw bounding boxes on the image
         for result in results:
             boxes = result.boxes.xyxy
@@ -38,100 +42,100 @@ if input_option == "Upload Image":
         # Show the resulting image
         st.image(img_np, caption='Detected Fire', use_column_width=True)
 
-elif input_option == "Use Webcam":
-    st.write("Starting webcam for live detection...")
+# elif input_option == "Use Webcam":
+#     st.write("Starting webcam for live detection...")
     
-    # Start video capture
-    camera = cv2.VideoCapture(0)  # 0 is the default camera
+#     # Start video capture
+#     camera = cv2.VideoCapture(0)  # 0 is the default camera
 
-    # Create a placeholder for the video feed
-    video_placeholder = st.empty()
+#     # Create a placeholder for the video feed
+#     video_placeholder = st.empty()
 
-    # Main loop for live detection
-    while True:
-        ret, frame = camera.read()
-        if not ret:
-            st.write("Failed to capture image")
-            break
+#     # Main loop for live detection
+#     while True:
+#         ret, frame = camera.read()
+#         if not ret:
+#             st.write("Failed to capture image")
+#             break
 
-        # Make predictions
-        results = model.predict(source=frame, conf=0.5)
+#         # Make predictions
+#         results = model.predict(source=frame, conf=0.5)
 
-        # Draw bounding boxes on the frame
-        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)
+#         # Draw bounding boxes on the frame
+#         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)
 
-        # Convert frame to RGB
-        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+#         # Convert frame to RGB
+#         rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
-        # Display the frame in the Streamlit app
-        video_placeholder.image(rgb_frame, channels="RGB", use_column_width=True)
+#         # Display the frame in the Streamlit app
+#         video_placeholder.image(rgb_frame, channels="RGB", use_column_width=True)
 
-        # Break loop on user command
-        if st.button("Stop Detection"):
-            break
+#         # Break loop on user command
+#         if st.button("Stop Detection"):
+#             break
 
-    # Release the camera
-    camera.release()
+#     # Release the camera
+#     camera.release()
 
 
 
 
 
-elif input_option == "Upload Video":
-    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())
+# elif input_option == "Upload Video":
+#     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)
+#         # Display the uploaded video
+#         st.video(temp_video_path)
 
-        # Open the video file
-        video_capture = cv2.VideoCapture(temp_video_path)
+#         # Open the video file
+#         video_capture = cv2.VideoCapture(temp_video_path)
 
-        # Create a placeholder for video frame processing
-        video_frame_placeholder = st.empty()
-        fire_detected = False
+#         # 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:
-                break
+#         # Loop through video frames
+#         while video_capture.isOpened():
+#             ret, frame = video_capture.read()
+#             if not ret:
+#                 break
 
-            # Make predictions using your fire detection model
-            results = model.predict(source=frame, conf=0.5)
+#             # Make predictions using your fire detection model
+#             results = model.predict(source=frame, conf=0.5)
             
                 
 
-            # 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
+#             # 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 the frame to RGB format
-            rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+#             # Convert the frame to RGB format
+#             rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
-            # Display the processed frame
-            video_frame_placeholder.image(rgb_frame, channels="RGB", use_column_width=True)
+#             # 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.")
+#         # 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()
+#         # Release the video capture
+#         video_capture.release()