# import streamlit as st
# from ultralytics import YOLO
# import cv2
# import numpy as np
# from PIL import Image

# model = YOLO("best.pt") 
# model.predict(source=0,imgsize=640,conf=0.6,show=True)


# # Load the YOLO model (replace with your model path)
# model = YOLO("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", "Upload Video"])

# 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)

#         # Variable to check if fire is detected
#         fire_detected = False

#         # 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)

#                 # Check if the detected class is "fire" (adjust based on your model's class mapping)
#                 class_id = int(box[5])  # Assuming class ID is at the 6th position
#                 if class_id == 0:  # Replace 0 with the actual class ID for fire if different
#                     fire_detected = True

#         # Show the resulting image
#         st.image(img_np, caption='Detected Fire', use_column_width=True)

#         # Display message based on fire detection
#         if fire_detected:
#             st.success("🔥 Fire Detected!")
#         else:
#             st.warning("No Fire Detected.")


# 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()





# 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)

#         # 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

#         # 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)
            
                

#             # 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)

#             # 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()




import streamlit as st
from ultralytics import YOLO
import numpy as np
from PIL import Image

# Load the YOLO model (use the path to your 'best.pt' file)
model = YOLO("best.pt")

st.title("Fire Detection in Forest")

# Upload Image
uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Open the uploaded image
    img = Image.open(uploaded_file)
    st.image(img, caption="Uploaded Image", use_column_width=True)

    # Convert image to a numpy array
    img_np = np.array(img)

    # Make predictions
    results = model.predict(source=img_np, imgsz=640, conf=0.5)

    # Check if fire is detected
    fire_detected = any("fire" in results.names[int(cls)] for cls in results[0].boxes.cls)

    # Display results
    if fire_detected:
        st.success("🔥 Fire Detected!")
    else:
        st.warning("No Fire Detected.")