Update main.py

main.py

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+import streamlit as st
+from ultralytics import YOLO
+import cv2
+import numpy as np
+from PIL import Image
+
+# Load the YOLO model (replace with your model path)
+model = YOLO("D:\\streamlit\\my_streamlit_app\\fire\\best.pt")
+   # Use your YOLO model file here
+
+st.title("Fire Detection in Forest")
+
+# Sidebar for input options
+input_option = st.sidebar.selectbox("Select Input Method", ["Upload Image", "Use Webcam"])
+
+if input_option == "Upload Image":
+    # Upload Image
+    uploaded_file = st.file_uploader("Choose an Image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        img = Image.open(uploaded_file)
+        st.image(img, caption='User Image')
+        st.write("Classifying...")
+
+        # Convert image to numpy array
+        img_np = np.array(img)
+
+        # Make predictions
+        results = model.predict(source=img_np, conf=0.5)
+
+        # Draw bounding boxes on the image
+        for result in results:
+            boxes = result.boxes.xyxy
+            for box in boxes:
+                x1, y1, x2, y2 = box[:4].astype(int)
+                img_np = cv2.rectangle(img_np, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        
+        # 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...")
+    
+    # Start video capture
+    camera = cv2.VideoCapture(0)  # 0 is the default camera
+
+    # 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
+
+        # 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)
+
+        # 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)
+
+        # Break loop on user command
+        if st.button("Stop Detection"):
+            break
+
+    # Release the camera
+    camera.release()