Update app.py

app.py

@@ -1,118 +1,4 @@
-'''import cv2
-import numpy as np
-import torch
-import streamlit as st
-from ultralytics import YOLO
-from camera_input_live import camera_input_live
-
-# Load YOLO fire detection model
-model_path = "last.pt"
-if not torch.cuda.is_available():
-    device = "cpu"
-else:
-    device = "cuda"
-
-model = YOLO(model_path)
-model.to(device)
-
-# Streamlit app title
-st.title("Live Fire Detection with Camera")
-st.subheader("Hold the camera towards potential fire sources to detect in real-time.")
-
-# Capture live camera input
-image = camera_input_live()
-
-if image is not None:
-    # Convert the image to OpenCV format
-    bytes_data = image.getvalue()
-    cv2_img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
-
-    # Perform fire detection
-    results = model(cv2_img)
-
-    # Draw bounding boxes for detected fires
-    for result in results:
-        boxes = result.boxes
-        for box in boxes:
-            b = box.xyxy[0].cpu().numpy().astype(int)
-            c = int(box.cls[0])
-            label = f'Fire {box.conf[0]:.2f}'
-            cv2.rectangle(cv2_img, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 3)
-            cv2.putText(cv2_img, label, (b[0], b[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
-
-    # Display the annotated image
-    st.image(cv2_img, channels="BGR", caption="Detected Fire", use_container_width=True)
-'''
-
-
-
-'''import cv2
-import numpy as np
-import torch
-import streamlit as st
-import pygame
-import os
-from ultralytics import YOLO
-from camera_input_live import camera_input_live
 
-# Set environment variable to use dummy audio on Hugging Face Spaces
-os.environ["SDL_AUDIODRIVER"] = "dummy"
-
-# Initialize Pygame mixer for audio alarm
-pygame.mixer.init()
-alarm_sound = "alarm.mp3"  # Ensure you have an alarm.mp3 file in your directory
-
-# Load YOLO fire detection model
-model_path = "last.pt"
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-model = YOLO(model_path)
-model.to(device)
-
-# Streamlit app title
-st.title("Live Fire Detection with Camera")
-st.subheader("Hold the camera towards potential fire sources to detect in real-time.")
-
-# Capture live camera input
-image = camera_input_live()
-fire_detected = False  # Track fire detection state
-
-if image is not None:
-    # Convert the image to OpenCV format
-    bytes_data = image.getvalue()
-    cv2_img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
-
-    # Perform fire detection
-    results = model(cv2_img)
-
-    fire_present = False  # Temporary flag for fire detection in this frame
-    
-    # Draw bounding boxes for detected fires
-    for result in results:
-        boxes = result.boxes
-        for box in boxes:
-            b = box.xyxy[0].cpu().numpy().astype(int)
-            label = f'Fire {box.conf[0]:.2f}'
-            cv2.rectangle(cv2_img, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 3)
-            cv2.putText(cv2_img, label, (b[0], b[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
-            fire_present = True  # Fire detected
-    
-    # Display the annotated image
-    st.image(cv2_img, channels="BGR", caption="Detected Fire", use_container_width=True)
-    
-    # Display logs
-    if fire_present:
-        st.error("🔥 Fire Detected! 🔥")
-        if not fire_detected:  # Play alarm if not already playing
-            pygame.mixer.music.load(alarm_sound)
-            pygame.mixer.music.play(-1)  # Loop indefinitely
-        fire_detected = True  # Update fire status
-    else:
-        st.success("✅ No Fire Detected")
-        if fire_detected:
-            pygame.mixer.music.stop()  # Stop alarm
-        fire_detected = False
-        '''
 import cv2
 import numpy as np
 import torch