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streamlit_app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import requests
+import math
+import time
+import os
+from tempfile import NamedTemporaryFile
+import folium
+from streamlit_folium import st_folium
+
+# ============================================================
+# CONFIGURATION
+# ============================================================
+MODEL_PATH = "model/best.pt"
+CLASS_NAMES = {0: "Accident", 1: "Non-accident", 2: "Fire"}
+ALERT_CLASSES = {"Accident", "Fire"}
+DEFAULT_CONFIDENCE = 0.3
+HOSPITAL_RADIUS_M = 5000
+
+# ============================================================
+# PAGE CONFIG
+# ============================================================
+st.set_page_config(
+    page_title="AccidentAI — Real-Time Detection & Alert",
+    page_icon="🚨",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# ============================================================
+# CUSTOM CSS
+# ============================================================
+st.markdown("""
+<style>
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;900&display=swap');
+
+html, body, .stApp {
+    font-family: 'Inter', sans-serif !important;
+}
+
+/* Hero */
+.hero-wrap {
+    text-align: center;
+    padding: 2.5rem 1rem 1.5rem;
+}
+.hero-wrap h1 {
+    font-size: 2.8rem;
+    font-weight: 900;
+    background: linear-gradient(135deg, #ff3b30, #ff9500, #ff3b30);
+    background-size: 200% 200%;
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    animation: grad 4s ease infinite;
+    margin-bottom: 0.25rem;
+}
+@keyframes grad {
+    0%   { background-position: 0% 50%; }
+    50%  { background-position: 100% 50%; }
+    100% { background-position: 0% 50%; }
+}
+.hero-wrap .subtitle {
+    font-size: 1.1rem;
+    color: #aaa;
+    font-weight: 400;
+}
+.hero-wrap .pills {
+    margin-top: 0.75rem;
+    display: flex;
+    gap: 0.5rem;
+    justify-content: center;
+    flex-wrap: wrap;
+}
+.hero-wrap .pill {
+    background: rgba(255,59,48,0.12);
+    border: 1px solid rgba(255,59,48,0.25);
+    color: #ff6b5e;
+    padding: 0.3rem 0.85rem;
+    border-radius: 999px;
+    font-size: 0.78rem;
+    font-weight: 500;
+}
+
+/* Glass card */
+.glass {
+    background: rgba(255,255,255,0.04);
+    border: 1px solid rgba(255,255,255,0.08);
+    border-radius: 16px;
+    padding: 1.5rem;
+    backdrop-filter: blur(12px);
+    margin-bottom: 1rem;
+}
+
+/* Stat box */
+.stat-row {
+    display: flex;
+    gap: 1rem;
+    flex-wrap: wrap;
+    margin: 0.5rem 0;
+}
+.stat-box {
+    flex: 1;
+    min-width: 120px;
+    text-align: center;
+    background: rgba(255,255,255,0.04);
+    border: 1px solid rgba(255,255,255,0.08);
+    border-radius: 12px;
+    padding: 1rem 0.75rem;
+}
+.stat-box .num {
+    font-size: 1.8rem;
+    font-weight: 700;
+}
+.stat-box .lbl {
+    font-size: 0.75rem;
+    color: #888;
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+}
+
+/* Alert banner */
+@keyframes pulse-border {
+    0%, 100% { border-color: rgba(255,59,48,0.25); }
+    50%      { border-color: rgba(255,59,48,0.65); }
+}
+.alert-banner {
+    background: linear-gradient(135deg, rgba(255,59,48,0.10), rgba(255,149,0,0.06));
+    border: 2px solid rgba(255,59,48,0.35);
+    border-radius: 14px;
+    padding: 1.25rem 1.5rem;
+    animation: pulse-border 2s ease infinite;
+    margin-bottom: 1rem;
+}
+.alert-banner h3 {
+    margin: 0 0 0.25rem;
+    color: #ff5e57;
+}
+.alert-banner p {
+    margin: 0;
+    color: #ccc;
+    font-size: 0.92rem;
+}
+
+/* Hospital card */
+.hosp-card {
+    background: rgba(255,255,255,0.04);
+    border: 1px solid rgba(255,255,255,0.09);
+    border-radius: 12px;
+    padding: 1rem 1.25rem;
+    margin-bottom: 0.65rem;
+    transition: border-color 0.25s;
+}
+.hosp-card:hover {
+    border-color: rgba(52,199,89,0.45);
+}
+.hosp-card .name {
+    font-weight: 600;
+    font-size: 1rem;
+    color: #e0e0e0;
+}
+.hosp-card .meta {
+    font-size: 0.82rem;
+    color: #999;
+    margin-top: 0.25rem;
+}
+
+/* Notification log entry */
+.notif-entry {
+    display: flex;
+    align-items: center;
+    gap: 0.6rem;
+    padding: 0.6rem 1rem;
+    border-radius: 10px;
+    margin-bottom: 0.4rem;
+    font-size: 0.88rem;
+}
+.notif-ok {
+    background: rgba(52,199,89,0.10);
+    border: 1px solid rgba(52,199,89,0.20);
+    color: #34c759;
+}
+
+/* Hide default header & footer */
+#MainMenu, header, footer { visibility: hidden; }
+
+/* Folium map container */
+iframe { border-radius: 14px !important; }
+</style>
+""", unsafe_allow_html=True)
+
+
+# ============================================================
+# MODEL LOADING
+# ============================================================
+@st.cache_resource
+def load_model():
+    if not os.path.exists(MODEL_PATH):
+        st.error(f"Model file not found at `{MODEL_PATH}`.")
+        st.stop()
+    return YOLO(MODEL_PATH)
+
+
+model = load_model()
+
+
+# ============================================================
+# UTILITY FUNCTIONS
+# ============================================================
+def haversine_km(lat1, lon1, lat2, lon2):
+    """Return distance in km between two lat/lon points."""
+    R = 6371
+    dlat = math.radians(lat2 - lat1)
+    dlon = math.radians(lon2 - lon1)
+    a = (
+        math.sin(dlat / 2) ** 2
+        + math.cos(math.radians(lat1))
+        * math.cos(math.radians(lat2))
+        * math.sin(dlon / 2) ** 2
+    )
+    return R * 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
+
+
+# ============================================================
+# DETECTION FUNCTIONS
+# ============================================================
+def detect_in_image(image_array, conf):
+    """Run YOLO on a single image. Returns (detections, annotated_rgb)."""
+    results = model.predict(image_array, conf=conf, verbose=False)
+    detections = []
+    for result in results:
+        for box in result.boxes:
+            cls_id = int(box.cls[0])
+            detections.append(
+                {
+                    "label": CLASS_NAMES.get(cls_id, "Unknown"),
+                    "confidence": float(box.conf[0]),
+                }
+            )
+    annotated_bgr = results[0].plot()
+    annotated_rgb = cv2.cvtColor(annotated_bgr, cv2.COLOR_BGR2RGB)
+    return detections, annotated_rgb
+
+
+def detect_in_video(video_path, conf, frame_skip=4, progress_cb=None):
+    """Run YOLO on video frames. Returns (detections, best_annotated_rgb)."""
+    cap = cv2.VideoCapture(video_path)
+    total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) or 1
+    detections = []
+    best_frame = None
+    best_conf = 0
+    idx = 0
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if idx % frame_skip == 0:
+            results = model.predict(frame, conf=conf, verbose=False)
+            for result in results:
+                for box in result.boxes:
+                    cls_id = int(box.cls[0])
+                    c = float(box.conf[0])
+                    label = CLASS_NAMES.get(cls_id, "Unknown")
+                    if label in ALERT_CLASSES:
+                        detections.append(
+                            {"label": label, "confidence": c, "frame": idx}
+                        )
+                        if c > best_conf:
+                            best_conf = c
+                            best_frame = results[0].plot()
+        idx += 1
+        if progress_cb:
+            progress_cb(min(idx / total, 1.0))
+
+    cap.release()
+    if best_frame is not None:
+        best_frame = cv2.cvtColor(best_frame, cv2.COLOR_BGR2RGB)
+    return detections, best_frame
+
+
+# ============================================================
+# LOCATION & HOSPITAL FUNCTIONS
+# ============================================================
+def get_ip_location():
+    """Free IP-based geolocation (no API key required)."""
+    try:
+        r = requests.get("http://ip-api.com/json/", timeout=5)
+        d = r.json()
+        if d.get("status") == "success":
+            return {
+                "lat": d["lat"],
+                "lon": d["lon"],
+                "city": d.get("city", ""),
+                "region": d.get("regionName", ""),
+                "country": d.get("country", ""),
+            }
+    except Exception:
+        pass
+    return None
+
+
+def fetch_hospitals(lat, lon, radius_m=5000):
+    """Query OpenStreetMap Overpass API for hospitals within radius."""
+    query = f"""
+    [out:json][timeout:10];
+    (
+      node["amenity"="hospital"](around:{radius_m},{lat},{lon});
+      way["amenity"="hospital"](around:{radius_m},{lat},{lon});
+      relation["amenity"="hospital"](around:{radius_m},{lat},{lon});
+    );
+    out center body;
+    """
+    try:
+        r = requests.post(
+            "https://overpass-api.de/api/interpreter",
+            data={"data": query},
+            timeout=15,
+        )
+        elements = r.json().get("elements", [])
+    except Exception:
+        return []
+
+    hospitals = []
+    for el in elements:
+        tags = el.get("tags", {})
+        if el["type"] == "node":
+            h_lat, h_lon = el["lat"], el["lon"]
+        else:
+            c = el.get("center", {})
+            h_lat = c.get("lat", lat)
+            h_lon = c.get("lon", lon)
+        dist = haversine_km(lat, lon, h_lat, h_lon)
+        hospitals.append(
+            {
+                "name": tags.get("name", "Unnamed Hospital"),
+                "lat": h_lat,
+                "lon": h_lon,
+                "distance_km": round(dist, 2),
+                "phone": tags.get("phone", tags.get("contact:phone", "—")),
+                "beds": tags.get("beds", "—"),
+                "emergency": tags.get("emergency", "unknown"),
+            }
+        )
+    hospitals.sort(key=lambda h: h["distance_km"])
+    return hospitals
+
+
+def build_map(lat, lon, hospitals, radius_m=5000):
+    """Create a folium map with accident marker, radius circle, and hospitals."""
+    m = folium.Map(location=[lat, lon], zoom_start=14, tiles="CartoDB dark_matter")
+    # Accident location
+    folium.Marker(
+        [lat, lon],
+        popup="🚨 Accident",
+        icon=folium.Icon(color="red", icon="exclamation-triangle", prefix="fa"),
+    ).add_to(m)
+    # Radius
+    folium.Circle(
+        [lat, lon],
+        radius=radius_m,
+        color="#ff3b30",
+        fill=True,
+        fill_opacity=0.06,
+        weight=2,
+        dash_array="6 4",
+    ).add_to(m)
+    # Hospitals
+    for h in hospitals:
+        folium.Marker(
+            [h["lat"], h["lon"]],
+            popup=f"🏥 {h['name']} — {h['distance_km']} km",
+            icon=folium.Icon(color="green", icon="plus-square", prefix="fa"),
+        ).add_to(m)
+    return m
+
+
+# ============================================================
+# SIDEBAR
+# ============================================================
+with st.sidebar:
+    st.markdown("## ⚙️ Detection Settings")
+    confidence = st.slider(
+        "Confidence threshold", 0.10, 0.90, DEFAULT_CONFIDENCE, 0.05
+    )
+
+    st.markdown("---")
+    st.markdown("## 📍 Location")
+    st.caption(
+        "Location is auto-detected via IP. On cloud deployments this returns "
+        "the server location — use manual override for accurate testing."
+    )
+    use_manual = st.checkbox("Use manual coordinates")
+    if use_manual:
+        manual_lat = st.number_input("Latitude", value=28.6139, format="%.4f")
+        manual_lon = st.number_input("Longitude", value=77.2090, format="%.4f")
+    else:
+        manual_lat, manual_lon = None, None
+
+    st.markdown("---")
+    st.markdown("## ℹ️ About")
+    st.markdown(
+        "**AccidentAI** uses a custom-trained **YOLOv8** model to detect "
+        "accidents and fires in CCTV footage, then automatically locates "
+        "nearby hospitals and sends simulated emergency alerts."
+    )
+    st.markdown(
+        "Built with Ultralytics, Streamlit, OpenStreetMap Overpass API, and Folium."
+    )
+
+
+# ============================================================
+# HERO
+# ============================================================
+st.markdown(
+    """
+<div class="hero-wrap">
+    <h1>🚨 AccidentAI</h1>
+    <p class="subtitle">Real-Time Accident Detection &amp; Emergency Alert System</p>
+    <div class="pills">
+        <span class="pill">YOLOv8</span>
+        <span class="pill">CCTV Analysis</span>
+        <span class="pill">Hospital Alerts</span>
+        <span class="pill">Live Map</span>
+    </div>
+</div>
+""",
+    unsafe_allow_html=True,
+)
+
+
+# ============================================================
+# FILE UPLOAD
+# ============================================================
+uploaded = st.file_uploader(
+    "Upload CCTV footage or image",
+    type=["jpg", "jpeg", "png", "mp4", "avi", "mov"],
+    help="Supported formats: JPG, PNG images and MP4, AVI, MOV videos",
+)
+
+if not uploaded:
+    st.info("👆 Upload an image or video to start detection.")
+    st.stop()
+
+# ============================================================
+# RUN DETECTION
+# ============================================================
+is_video = uploaded.name.lower().endswith((".mp4", ".avi", ".mov"))
+
+if is_video:
+    with NamedTemporaryFile(delete=False, suffix=".mp4") as tmp:
+        tmp.write(uploaded.read())
+        tmp_path = tmp.name
+    st.markdown("### 🎬 Analyzing video frames…")
+    pbar = st.progress(0)
+    detections, annotated = detect_in_video(
+        tmp_path, confidence, progress_cb=pbar.progress
+    )
+    pbar.empty()
+    try:
+        os.unlink(tmp_path)
+    except OSError:
+        pass
+else:
+    raw = np.asarray(bytearray(uploaded.read()), dtype=np.uint8)
+    image = cv2.imdecode(raw, cv2.IMREAD_COLOR)
+    detections, annotated = detect_in_image(image, confidence)
+
+alert_dets = [d for d in detections if d["label"] in ALERT_CLASSES]
+
+# ============================================================
+# RESULTS
+# ============================================================
+st.markdown("---")
+col_img, col_stats = st.columns([2, 1], gap="large")
+
+with col_img:
+    st.markdown("### 🔍 Detection Output")
+    if annotated is not None:
+        st.image(annotated, use_container_width=True)
+    elif is_video:
+        st.info("No hazard frames captured — video appears safe.")
+
+with col_stats:
+    st.markdown("### 📊 Analysis")
+    n_acc = sum(1 for d in alert_dets if d["label"] == "Accident")
+    n_fire = sum(1 for d in alert_dets if d["label"] == "Fire")
+    total = len(detections)
+
+    st.markdown(
+        f"""
+    <div class="stat-row">
+        <div class="stat-box">
+            <div class="num" style="color:#ff5e57">{n_acc}</div>
+            <div class="lbl">Accidents</div>
+        </div>
+        <div class="stat-box">
+            <div class="num" style="color:#ff9500">{n_fire}</div>
+            <div class="lbl">Fires</div>
+        </div>
+        <div class="stat-box">
+            <div class="num" style="color:#34c759">{total}</div>
+            <div class="lbl">Total Detections</div>
+        </div>
+    </div>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    if alert_dets:
+        peak = max(d["confidence"] for d in alert_dets)
+        avg = sum(d["confidence"] for d in alert_dets) / len(alert_dets)
+        st.metric("Peak Confidence", f"{peak:.1%}")
+        st.metric("Avg Confidence", f"{avg:.1%}")
+        if is_video:
+            frames_hit = len(set(d.get("frame", 0) for d in alert_dets))
+            st.metric("Frames with Hazards", frames_hit)
+    else:
+        st.success("✅ No hazards detected in the uploaded media.")
+
+
+# ============================================================
+# EMERGENCY ALERT SYSTEM
+# ============================================================
+if not alert_dets:
+    st.stop()
+
+st.markdown("---")
+
+# Alert banner
+top_label = "Accident" if n_acc else "Fire"
+st.markdown(
+    f"""
+<div class="alert-banner">
+    <h3>🚨 EMERGENCY — {top_label} Detected</h3>
+    <p>Initiating automated alert protocol • Searching hospitals within 5 km radius</p>
+</div>
+""",
+    unsafe_allow_html=True,
+)
+
+# -- Get location --
+if use_manual and manual_lat is not None:
+    loc = {
+        "lat": manual_lat,
+        "lon": manual_lon,
+        "city": "Manual",
+        "region": "",
+        "country": "",
+    }
+else:
+    with st.spinner("📍 Detecting location…"):
+        loc = get_ip_location()
+
+if loc is None:
+    st.warning(
+        "Could not detect location automatically. "
+        "Enable **manual coordinates** in the sidebar."
+    )
+    st.stop()
+
+loc_str = ", ".join(filter(None, [loc["city"], loc["region"], loc["country"]]))
+st.markdown(
+    f"**📍 Incident Location:** {loc_str} &nbsp;|&nbsp; "
+    f"`{loc['lat']:.4f}, {loc['lon']:.4f}`"
+)
+
+# -- Fetch hospitals --
+with st.spinner("🏥 Querying OpenStreetMap for nearby hospitals…"):
+    hospitals = fetch_hospitals(loc["lat"], loc["lon"], HOSPITAL_RADIUS_M)
+
+if not hospitals:
+    st.warning(
+        "No hospitals found within 5 km. Try different coordinates or increase radius."
+    )
+    st.stop()
+
+st.markdown(f"**Found {len(hospitals)} hospital(s) within 5 km**")
+
+# -- Map & hospital list side by side --
+col_map, col_list = st.columns([1, 1], gap="large")
+
+with col_map:
+    st.markdown("### 🗺️ Incident Map")
+    m = build_map(loc["lat"], loc["lon"], hospitals)
+    st_folium(m, height=420, use_container_width=True)
+
+with col_list:
+    st.markdown("### 🏥 Nearby Hospitals")
+    for i, h in enumerate(hospitals):
+        emoji = "🥇" if i == 0 else "🥈" if i == 1 else "🥉" if i == 2 else "🏥"
+        st.markdown(
+            f"""<div class="hosp-card">
+                <div class="name">{emoji} {h['name']}</div>
+                <div class="meta">
+                    📏 {h['distance_km']} km &nbsp;|&nbsp;
+                    📞 {h['phone']} &nbsp;|&nbsp;
+                    🛏️ Beds: {h['beds']}
+                </div>
+            </div>""",
+            unsafe_allow_html=True,
+        )
+    if len(hospitals) > 8:
+        st.caption(f"Showing all {len(hospitals)} results")
+
+# ============================================================
+# SIMULATED NOTIFICATIONS
+# ============================================================
+st.markdown("---")
+st.markdown("### 📨 Alert Dispatch Log")
+st.caption("Sending automated emergency alerts to the nearest hospitals…")
+
+notify_count = min(len(hospitals), 3)
+log_container = st.container()
+for i in range(notify_count):
+    h = hospitals[i]
+    time.sleep(0.7)
+    log_container.markdown(
+        f"""<div class="notif-entry notif-ok">
+            ✅ &nbsp; Alert dispatched to <b>{h['name']}</b> — {h['distance_km']} km away
+        </div>""",
+        unsafe_allow_html=True,
+    )
+
+st.success(
+    f"✅ Emergency alerts sent to **{notify_count}** hospital(s). "
+    f"Nearest: **{hospitals[0]['name']}** ({hospitals[0]['distance_km']} km)"
+)