Spaces:
Running
Running
File size: 2,562 Bytes
f492127 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | """
HaramGuard — Dashboard Entry Point
=====================================
"""
import cv2
import streamlit as st
import numpy as np
from config import VIDEO_PATH, DB_PATH, MODEL_PATH, OPENAI_KEY, ANTHROPIC_KEY
from pipeline import RealTimePipeline
# ── Init ──────────────────────────────────────────────────────────────
st.set_page_config(page_title='HaramGuard', layout='wide')
st.title('🕌 HaramGuard — Real-Time Crowd Safety')
pipeline = RealTimePipeline(
video_path = VIDEO_PATH,
openai_key = OPENAI_KEY,
anthropic_key = ANTHROPIC_KEY, # enables Claude Vision counting
model_path = MODEL_PATH,
db_path = DB_PATH,
)
cap = pipeline.get_video_capture()
# ── Placeholders ──────────────────────────────────────────────────────
col1, col2 = st.columns([2, 1])
with col1:
ph_video = st.empty()
with col2:
ph_risk = st.empty()
ph_count = st.empty()
ph_trend = st.empty()
ph_alert = st.empty()
ph_reflect = st.empty()
# ── Main loop ─────────────────────────────────────────────────────────
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
pipeline.process_one_frame(frame)
s = pipeline.state
# Video feed
ph_video.image(
cv2.cvtColor(s['annotated'], cv2.COLOR_BGR2RGB),
use_column_width=True,
caption=f"Frame {s['frame_id']} | {s['fps']} FPS"
)
# Risk metrics
risk_color = '🔴' if s['risk_level'] == 'HIGH' else '🟡' if s['risk_level'] == 'MEDIUM' else '🟢'
ph_risk.metric('Risk Level', f"{risk_color} {s['risk_level']}", f"score: {s['risk_score']:.3f}")
ph_count.metric('Pilgrims Detected', s['person_count'])
ph_trend.metric('Trend', s['trend'].upper())
# Arabic P0 alert
if s.get('arabic_alert'):
ph_alert.error(f"🚨 {s['arabic_alert']}")
pipeline.state['arabic_alert'] = ''
# Reflection summary
rs = s.get('reflection_summary', {})
if rs:
ph_reflect.info(
f"🪞 Reflection: {rs.get('bias_events', 0)} bias events / "
f"{rs.get('total_reflections', 0)} frames "
f"({rs.get('bias_rate_pct', 0):.1f}%)"
)
cap.release()
|