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| from ultralytics import YOLO | |
| import streamlit as st | |
| import numpy as np | |
| from PIL import Image | |
| model=YOLO("best.pt") | |
| # Set the title of the app | |
| st.title("Fire Detection App") | |
| # Sidebar for input options | |
| input_option = st.sidebar.selectbox("Select Input Method", ["Upload Image"]) | |
| 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: | |
| # Open and display the uploaded image | |
| img = Image.open(uploaded_file) | |
| st.image(img, caption='User Image', use_column_width=True) | |
| # Convert the image to a numpy array | |
| img_np = np.array(img) | |
| # Make predictions | |
| results = model.predict(source=img_np, conf=0.5) # Adjust confidence threshold as needed | |
| # Variable to check if fire is detected | |
| fire_detected = False | |
| # Draw bounding boxes on the image | |
| for result in results: | |
| boxes = result.boxes.xyxy # Bounding boxes | |
| 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 with bounding boxes | |
| 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.") | |