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import streamlit as st
import cv2
import PIL
from ultralytics import YOLO
import tempfile
import time
# ----------------------------------------------------------------
# Load the model (using a URL to your weight file)
model_path = 'https://huggingface.co/spaces/tstone87/ccr-colorado/blob/main/best.pt'
try:
model = YOLO(model_path)
except Exception as ex:
st.error(f"Unable to load model. Check the specified path: {model_path}")
st.error(ex)
# ----------------------------------------------------------------
# Set page configuration
st.set_page_config(
page_title="WildfireWatch",
page_icon="🔥",
layout="wide",
initial_sidebar_state="expanded"
)
# ----------------------------------------------------------------
# App Title and Description
st.title("WildfireWatch: Detecting Wildfire using AI")
st.markdown(
"""
**Wildfires are a critical threat to ecosystems and communities.**
Early detection can save lives and reduce environmental damage.
Use this app to analyze images, videos, or live webcam streams for signs of fire.
"""
)
# ----------------------------------------------------------------
# Create two tabs: one for file uploads and one for live webcam stream detection
tab_upload, tab_live = st.tabs(["Upload Image/Video", "Live Webcam Stream"])
# =========================
# Tab 1: File Upload for Image/Video Detection
with tab_upload:
st.header("Upload an Image or Video")
uploaded_file = st.file_uploader(
"Choose an image or video...",
type=["jpg", "jpeg", "png", "bmp", "webp", "mp4"]
)
confidence = st.slider("Select Model Confidence", 25, 100, 40) / 100
if uploaded_file is not None:
if uploaded_file.type.split('/')[0] == 'image':
# Process uploaded image
image = PIL.Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
if st.button("Detect Wildfire in Image"):
results = model.predict(image, conf=confidence)
annotated_image = results[0].plot()[:, :, ::-1]
st.image(annotated_image, caption="Detection Result", use_column_width=True)
with st.expander("Detection Details"):
for box in results[0].boxes:
st.write("Box coordinates (xywh):", box.xywh)
elif uploaded_file.type.split('/')[0] == 'video':
# Process uploaded video
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(uploaded_file.read())
cap = cv2.VideoCapture(tfile.name)
if st.button("Detect Wildfire in Video"):
frame_placeholder = st.empty()
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model.predict(frame, conf=confidence)
annotated_frame = results[0].plot()[:, :, ::-1]
frame_placeholder.image(annotated_frame, channels="BGR", use_column_width=True)
time.sleep(0.05) # Adjust delay for processing speed
cap.release()
# =========================
# Tab 2: Live Webcam Stream Detection
with tab_live:
st.header("Live Webcam Stream Detection")
st.markdown("Enter the URL for your online hosted webcam stream (e.g., an IP camera stream).")
webcam_url = st.text_input("Webcam Stream URL", value="http://<your_webcam_stream_url>")
live_confidence = st.slider("Select Live Detection Confidence", 25, 100, 40) / 100
# Initialize a session state flag for stopping the live loop
if "stop_live" not in st.session_state:
st.session_state.stop_live = False
def stop_live_detection():
st.session_state.stop_live = True
if st.button("Start Live Detection"):
cap = cv2.VideoCapture(webcam_url)
if not cap.isOpened():
st.error("Unable to open webcam stream. Please check the URL.")
else:
live_frame_placeholder = st.empty()
st.button("Stop Live Detection", on_click=stop_live_detection)
while cap.isOpened() and not st.session_state.stop_live:
ret, frame = cap.read()
if not ret:
st.error("Failed to retrieve frame from stream.")
break
results = model.predict(frame, conf=live_confidence)
annotated_frame = results[0].plot()[:, :, ::-1]
live_frame_placeholder.image(annotated_frame, channels="BGR", use_column_width=True)
time.sleep(0.05)
cap.release()
st.session_state.stop_live = False
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