Create app.oy

app.oy

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+import cv2
+import streamlit as st
+from ultralytics import YOLO
+import time
+import numpy as np
+from datetime import datetime
+import pytz
+
+# Page config and header
+st.set_page_config(
+    page_title="Fire Watch: AI-Powered Fire and Smoke Detection",
+    page_icon="🔥",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+st.title("Fire Watch: Fire Detection with an AI Vision Model")
+
+# --- Session State Initialization ---
+if "streams" not in st.session_state:
+    st.session_state.streams = []
+if "num_streams" not in st.session_state:
+    st.session_state.num_streams = 1
+if "confidence" not in st.session_state:
+    st.session_state.confidence = 0.30  # default 30%
+if "target_fps" not in st.session_state:
+    st.session_state.target_fps = 1.0   # default 1 FPS
+
+# --- Default URLs and Names for 10 Streams ---
+default_m3u8_urls = [
+    "https://publicstreamer4.cotrip.org/rtplive/070E27890CAM1RHS/playlist.m3u8",   # EB at i270
+    "https://publicstreamer2.cotrip.org/rtplive/070E27555CAM1RP1/playlist.m3u8",  # EB @ York St Denver
+    "https://publicstreamer1.cotrip.org/rtplive/225N00535CAM1RP1/playlist.m3u8",  # NB at Iliff Denver
+    "https://publicstreamer2.cotrip.org/rtplive/070W28220CAM1RHS/playlist.m3u8",  # WB Half Mile West of I225 Denver
+    "https://publicstreamer1.cotrip.org/rtplive/070W26805CAM1RHS/playlist.m3u8",  # 1 mile E of Kipling Denver
+    "https://publicstreamer4.cotrip.org/rtplive/076W03150CAM1RP1/playlist.m3u8",  # Main St Hudson
+    "https://publicstreamer2.cotrip.org/rtplive/070E27660CAM1NEC/playlist.m3u8",   # EB Colorado Blvd i70 Denver
+    "https://publicstreamer2.cotrip.org/rtplive/070W27475CAM1RHS/playlist.m3u8",   # E of Washington St Denver
+    "https://publicstreamer3.cotrip.org/rtplive/070W28155CAM1RHS/playlist.m3u8",   # WB Peroia St Underpass Denver
+    "https://publicstreamer3.cotrip.org/rtplive/070E11660CAM1RHS/playlist.m3u8"    # Grand Ave Glenwood
+]
+default_names = [
+    "EB at i270",
+    "EB @ York St Denver",
+    "NB at Iliff Denver",
+    "WB Half Mile West of I225 Denver",
+    "1 mile E of Kipling Denver",
+    "Main St Hudson",
+    "EB Colorado Blvd i70 Denver",
+    "E of Washington St Denver",
+    "WB Peroia St Underpass Denver",
+    "Grand Ave Glenwood"
+]
+
+# --- Sidebar Settings ---
+with st.sidebar:
+    st.header("Stream Settings")
+    # Custom configuration for stream 1 only.
+    custom_m3u8 = st.text_input("Custom M3U8 URL for Stream 1 (optional)", value="", key="custom_m3u8")
+    custom_name = st.text_input("Custom Webcam Name for Stream 1 (optional)", value="", key="custom_name")
+    
+    # Choose number of streams (1 to 10)
+    num_streams = st.selectbox("Number of Streams", list(range(1, 11)), index=0)
+    st.session_state.num_streams = num_streams
+
+    # Global settings for confidence and processing rate.
+    confidence = float(st.slider("Confidence Threshold", 5, 100, 30)) / 100
+    st.session_state.confidence = confidence
+    fps_options = {
+        "1 FPS": 1,
+        "1 frame/2s": 0.5,
+        "1 frame/3s": 0.3333,
+        "1 frame/5s": 0.2,
+        "1 frame/15s": 0.0667,
+        "1 frame/30s": 0.0333
+    }
+    video_option = st.selectbox("Processing Rate", list(fps_options.keys()), index=3)
+    st.session_state.target_fps = fps_options[video_option]
+
+# Update or initialize the streams using defaults (with custom override for stream 1).
+if len(st.session_state.streams) != st.session_state.num_streams:
+    st.session_state.streams = []
+    for i in range(st.session_state.num_streams):
+        if i == 0:
+            url = custom_m3u8.strip() if custom_m3u8.strip() else default_m3u8_urls[0]
+            display_name = custom_name.strip() if custom_name.strip() else default_names[0]
+        else:
+            url = default_m3u8_urls[i] if i < len(default_m3u8_urls) else ""
+            display_name = default_names[i] if i < len(default_names) else f"Stream {i+1}"
+        st.session_state.streams.append({
+            "current_m3u8_url": url,
+            "processed_frame": np.zeros((480, 640, 3), dtype=np.uint8),
+            "start_time": time.time(),
+            "processed_count": 0,
+            "detected_frames": [],
+            "last_processed_time": 0,
+            "stats_text": "Processing FPS: 0.00\nFrame Delay: 0.00 sec\nTensor Results: No detections",
+            "highest_match": 0.0,
+            "display_name": display_name
+        })
+else:
+    if st.session_state.num_streams > 0:
+        url = custom_m3u8.strip() if custom_m3u8.strip() else default_m3u8_urls[0]
+        display_name = custom_name.strip() if custom_name.strip() else default_names[0]
+        st.session_state.streams[0]["current_m3u8_url"] = url
+        st.session_state.streams[0]["display_name"] = display_name
+
+confidence = st.session_state.confidence
+target_fps = st.session_state.target_fps
+
+# --- Load Model ---
+model_path = 'https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/best.pt'
+@st.cache_resource
+def load_model():
+    return YOLO(model_path)
+
+try:
+    model = load_model()
+except Exception as ex:
+    st.error(f"Model loading failed: {str(ex)}")
+    st.stop()
+
+# --- Create Placeholders for Streams in a 2-Column Grid ---
+num_streams = st.session_state.num_streams
+feed_placeholders = []
+stats_placeholders = []
+cols = st.columns(2)
+for i in range(num_streams):
+    col_index = i % 2
+    if i >= 2 and col_index == 0:
+        cols = st.columns(2)
+    feed_placeholders.append(cols[col_index].empty())
+    stats_placeholders.append(cols[col_index].empty())
+if num_streams == 1:
+    _ = st.columns(2)
+
+def update_stream(i):
+    current_time = time.time()
+    sleep_time = 1.0 / target_fps
+    stream_state = st.session_state.streams[i]
+    if current_time - stream_state["last_processed_time"] >= sleep_time:
+        url = stream_state["current_m3u8_url"]
+        cap = cv2.VideoCapture(url)
+        if not cap.isOpened():
+            stats_placeholders[i].text("Failed to open M3U8 stream.")
+            return
+        ret, frame = cap.read()
+        cap.release()
+        if not ret:
+            stats_placeholders[i].text("Stream interrupted or ended.")
+            return
+        res = model.predict(frame, conf=confidence)
+        processed_frame = res[0].plot()[:, :, ::-1]
+        # Extract detection results.
+        tensor_info = "No detections"
+        max_conf = 0.0
+        try:
+            boxes = res[0].boxes
+            if boxes is not None and len(boxes) > 0:
+                max_conf = float(boxes.conf.max())
+                tensor_info = f"Detections: {len(boxes)} | Max Confidence: {max_conf:.2f}"
+        except Exception as ex:
+            tensor_info = f"Error extracting detections: {ex}"
+        # Only update if new detection's confidence is >= current highest.
+        if max_conf >= stream_state["highest_match"]:
+            stream_state["highest_match"] = max_conf
+            stream_state["detected_frames"].append(processed_frame)
+        stream_state["processed_count"] += 1
+        stream_state["last_processed_time"] = current_time
+        mt_time = datetime.now(pytz.timezone('America/Denver')).strftime('%Y-%m-%d %H:%M:%S MT')
+        stream_state["processed_frame"] = processed_frame
+        stream_state["stats_text"] = (
+            f"Processing FPS: {stream_state['processed_count'] / (current_time - stream_state['start_time']):.2f}\n"
+            f"{tensor_info}\n"
+            f"Highest Match: {stream_state['highest_match']:.2f}"
+        )
+        feed_placeholders[i].image(
+            processed_frame,
+            caption=f"Stream {i+1} - {stream_state['display_name']} - {mt_time}",
+            use_container_width=True
+        )
+        stats_placeholders[i].text(stream_state["stats_text"])
+
+# --- Continuous Processing Loop ---
+# Using Streamlit's experimental rerun mechanism to update frames.
+# This loop runs once per script execution; Streamlit will re-run the script as needed.
+for i in range(num_streams):
+    update_stream(i)
+
+# Trigger a rerun after a short delay for continuous updates
+time.sleep(1.0 / target_fps)
+st.experimental_rerun()