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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.")
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