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69def8e | 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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | import streamlit as st
import os
import sys
import time
import tempfile
import pandas as pd
from PIL import Image
import numpy as np
# Add workspace to path
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from src.embedding import FaceEmbedder
from scripts.inference import run_inference
# Page configuration
st.set_page_config(
page_title="FaceID - Milestone 4 Dashboard",
page_icon="🛡️",
layout="wide",
initial_sidebar_state="expanded"
)
# Custom CSS for "Premium" look
st.markdown("""
<style>
@import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;700&family=Inter:wght@300;400;600&display=swap');
.stApp {
background: linear-gradient(135deg, #0f172a 0%, #1e293b 100%);
color: #f8fafc;
}
.main-title {
font-family: 'Orbitron', sans-serif;
font-size: 3rem;
background: linear-gradient(90deg, #38bdf8, #818cf8);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
text-align: center;
margin-bottom: 0.2rem;
}
.sub-title {
font-family: 'Inter', sans-serif;
text-align: center;
color: #94a3b8;
margin-bottom: 2rem;
}
.glass-card {
background: rgba(30, 41, 59, 0.7);
backdrop-filter: blur(10px);
border-radius: 1rem;
border: 1px solid rgba(255, 255, 255, 0.1);
padding: 1.5rem;
margin-bottom: 1rem;
}
.match-dec {
font-family: 'Orbitron', sans-serif;
font-size: 2rem;
text-align: center;
padding: 0.8rem;
border-radius: 0.5rem;
margin-top: 1rem;
}
.same { color: #4ade80; border: 2px solid #4ade80; background: rgba(74, 222, 128, 0.1); }
.diff { color: #f87171; border: 2px solid #f87171; background: rgba(248, 113, 113, 0.1); }
.stMarkdown pre {
background-color: rgba(0, 0, 0, 0.3) !important;
color: #e2e8f0 !important;
border: 1px solid rgba(255, 255, 255, 0.1);
}
</style>
""", unsafe_allow_html=True)
# Singleton Model Loader
@st.cache_resource
def get_embedder():
return FaceEmbedder(model_name="Facenet")
def save_uploaded_file(uploaded_file):
try:
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file:
tmp_file.write(uploaded_file.getvalue())
return tmp_file.name
except Exception as e:
st.error(f"Error saving file: {e}")
return None
def load_markdown(path):
if os.path.exists(path):
with open(path, 'r', encoding='utf-8') as f:
return f.read()
return "File not found."
def main():
st.markdown("<h1 class='main-title'>FaceID Final Release</h1>", unsafe_allow_html=True)
st.markdown("<p class='sub-title'>Milestone 4: Hardware-Aware Inference & Professional Documentation</p>", unsafe_allow_html=True)
# Tabs for different sections
tab1, tab2, tab3, tab4 = st.tabs(["🚀 Real-time Inference", "📊 Performance Insights", "🛡️ System Card", "📈 Final Metrics"])
# --- TAB 1: INFERENCE ---
with tab1:
# Sample selection
st.markdown("### 🧬 Quick Select Samples")
samples = {
"Custom Upload": (None, None),
"Same Person (Albrecht Mentz)": (
"data/lfw/test/Albrecht_Mentz/Albrecht_Mentz_0000.jpg",
"data/lfw/test/Albrecht_Mentz/Albrecht_Mentz_0001.jpg"
),
"Same Person (Alejandro Toledo)": (
"data/lfw/test/Alejandro_Toledo/Alejandro_Toledo_0000.jpg",
"data/lfw/test/Alejandro_Toledo/Alejandro_Toledo_0001.jpg"
),
"Different People (Albrecht vs Alejandro)": (
"data/lfw/test/Albrecht_Mentz/Albrecht_Mentz_0000.jpg",
"data/lfw/test/Alejandro_Toledo/Alejandro_Toledo_0000.jpg"
)
}
sample_choice = st.selectbox("Pick a pre-loaded pair or use your own:", list(samples.keys()))
col1, col2 = st.columns([1, 1])
s_img1, s_img2 = samples[sample_choice]
with col1:
st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
st.subheader("Subject A")
if s_img1:
st.image(s_img1, width='stretch')
file1 = s_img1
else:
file1_up = st.file_uploader("Upload image 1", type=['jpg', 'jpeg', 'png'], key="app_img1")
if file1_up:
st.image(file1_up, width='stretch')
file1 = save_uploaded_file(file1_up)
else: file1 = None
st.markdown("</div>", unsafe_allow_html=True)
with col2:
st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
st.subheader("Subject B")
if s_img2:
st.image(s_img2, width='stretch')
file2 = s_img2
else:
file2_up = st.file_uploader("Upload image 2", type=['jpg', 'jpeg', 'png'], key="app_img2")
if file2_up:
st.image(file2_up, width='stretch')
file2 = save_uploaded_file(file2_up)
else: file2 = None
st.markdown("</div>", unsafe_allow_html=True)
threshold = st.slider("Verification Threshold", 0.0, 1.0, 0.35, 0.01)
if st.button("Run Verification", use_container_width=True, type="primary"):
if file1 and file2:
with st.spinner("Analyzing..."):
embedder = get_embedder()
try:
res = run_inference(file1, file2, threshold, embedder=embedder)
cls = "same" if res['decision'] == "SAME" else "diff"
st.markdown(f"<div class='match-dec {cls}'>{res['decision']} (Confidence: {res['confidence']*100:.1f}%)</div>", unsafe_allow_html=True)
m1, m2, m3 = st.columns(3)
m1.metric("Similarity Score", f"{res['similarity_score']:.4f}")
m2.metric("Total Latency", f"{res['latency_total_ms']:.1f}ms")
m3.metric("Extraction Time", f"{res['latency_emb_ms']:.1f}ms")
except Exception as e:
st.error(f"Inference error: {e}")
finally:
# Only cleanup if it was a temp file from upload
if isinstance(file1, str) and "tmp" in file1 and os.path.exists(file1): os.remove(file1)
if isinstance(file2, str) and "tmp" in file2 and os.path.exists(file2): os.remove(file2)
else:
st.warning("Please upload images or select a sample.")
# --- TAB 2: PERFORMANCE ---
with tab2:
st.subheader("Hardware-Aware Profiling Results")
st.info("Measurements taken on local hardware to characterize CPU latency and throughput.")
# Latency breakdown
l_col1, l_col2 = st.columns(2)
with l_col1:
st.markdown("#### Latency Breakdown")
latency_data = pd.DataFrame({
"Stage": ["Preprocessing", "Embedding", "Similarity"],
"Mean (ms)": [170.54, 237.70, 0.17]
})
st.bar_chart(latency_data.set_index("Stage"))
with l_col2:
st.markdown("#### Throughput by Batch Size")
throughput_data = pd.DataFrame({
"Batch Size": [1, 4, 8, 16],
"FPS": [4.24, 4.31, 3.97, 4.10]
})
st.line_chart(throughput_data.set_index("Batch Size"))
st.markdown("---")
st.markdown("#### Profiling Summary")
summary_txt = load_markdown("reports/profiling_summary.txt")
# Use st.code for high contrast and readability
st.code(summary_txt, language="markdown")
# --- TAB 3: SYSTEM CARD ---
with tab3:
st.subheader("System Documentation")
system_card = load_markdown("reports/System_Card.md")
st.markdown(system_card)
# --- TAB 4: FINAL METRICS ---
with tab4:
st.subheader("Final Evaluation Summary (Milestone 4)")
e_col1, e_col2, e_col3 = st.columns(3)
e_col1.metric("Accuracy", "84.6%")
e_col2.metric("F1-Score", "0.8254")
e_col3.metric("Pairs Evaluated", "500")
st.markdown("#### ROC Curve Artifact")
if os.path.exists("reports/roc_curve.png"):
st.image("reports/roc_curve.png", caption="ROC Curve for Final Release Model")
else:
st.write("ROC curve image not found.")
if __name__ == "__main__":
main()
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