cot

app.py

@@ -174,16 +174,16 @@ CHAIN_CSS = """
 }
 .hct-num {
   font-family: 'DM Mono', monospace;
-  font-size: 10px;
-  font-weight: 500;
+  font-size: 12px;
+  font-weight: 600;
   letter-spacing: 0.12em;
-  padding: 2px 7px;
+  padding: 3px 9px;
   border-radius: 4px;
 }
 .hct-title {
-  font-size: 11px;
-  font-weight: 600;
-  letter-spacing: 0.04em;
+  font-size: 13px;
+  font-weight: 700;
+  letter-spacing: 0.05em;
   text-transform: uppercase;
   flex: 1;
 }
@@ -263,10 +263,10 @@ CHAIN_CSS = """
 }
 .hct-arrow-line  { flex: 1; height: 1px; background: #d8d4ce; }
 .hct-arrow-label {
-  font-family: 'DM Mono', monospace; font-size: 9px;
-  color: #b0a898; letter-spacing: 0.08em; text-transform: uppercase;
-  white-space: nowrap; background: white;
-  padding: 2px 8px; border: 1px solid #e0dbd4; border-radius: 20px;
+  font-family: 'DM Mono', monospace; font-size: 11px;
+  color: #6a6258; letter-spacing: 0.06em; text-transform: uppercase;
+  white-space: nowrap; background: white; font-weight: 500;
+  padding: 3px 12px; border: 1px solid #ccc8c0; border-radius: 20px;
 }
 
 /* ── Stage 1: Location table ── */
@@ -376,6 +376,38 @@ CHAIN_CSS = """
 .hct-s2 .hct-dot { background: #c08040; }
 .hct-s3 .hct-dot { background: #d4453a; }
 
+/* ── Data flow banner ── */
+.hct-flow-banner {
+  background: #f8f9fc; border: 1px solid #dde0ee;
+  border-radius: 10px; padding: 10px 14px; margin-bottom: 10px;
+  font-size: 11.5px; color: #445;
+}
+.hct-flow-banner-title {
+  font-family: 'DM Mono', monospace; font-size: 9.5px; font-weight: 600;
+  letter-spacing: 0.1em; text-transform: uppercase;
+  color: #7080a0; margin-bottom: 7px;
+}
+.hct-flow-steps {
+  display: flex; align-items: center; gap: 0; flex-wrap: nowrap;
+}
+.hct-flow-step {
+  flex: 1; background: white; border: 1px solid #d4daf0;
+  border-radius: 7px; padding: 6px 8px; text-align: center;
+  min-width: 0;
+}
+.hct-flow-step-label {
+  font-family: 'DM Mono', monospace; font-size: 8.5px;
+  color: #8090b0; letter-spacing: 0.08em; text-transform: uppercase;
+  margin-bottom: 3px;
+}
+.hct-flow-step-desc {
+  font-size: 10.5px; color: #334; line-height: 1.4;
+}
+.hct-flow-arrow {
+  font-size: 14px; color: #a0aac0; padding: 0 5px;
+  flex-shrink: 0;
+}
+
 /* ── Prompt collapsible ── */
 details.hct-prompt-details { padding: 0 14px 10px; }
 details.hct-prompt-details summary {
@@ -547,20 +579,23 @@ def _parse_s3(text):
 PROMPT_BULLETS = {
     1: [
         "Extract objective factual features from the agent's mobility trajectory <b>without any interpretation</b>",
-        "Location inventory: list all visited POIs...",
-        "Temporal patterns: time-of-day distribution, weekday vs. weekend split...",
-        "Spatial characteristics: activity radius...",
-        "Sequence observations: common location transitions...",
+        "Location inventory: list all visited POIs with visit counts and apparent price tier (budget / mid-range / high-end)",
+        "Temporal patterns: time-of-day distribution, weekday vs. weekend split, and regularity of routines",
+        "Spatial characteristics: activity radius, average movement distance between locations",
+        "Sequence observations: common location transitions and typical daily activity chains",
     ],
     2: [
         "Perform behavioral abstraction across four dimensions based on Step 1 features",
-        "Routine &amp; Schedule: infer work schedule type (fixed hours, flexible, shift work, etc.)...",
-        "Economic Behavior: assess spending tier from venue choices...",
-        "Social &amp; Lifestyle: identify social engagement patterns, leisure activities...",
-        "Routine Stability: evaluate consistency and regularity of movement patterns...",
+        "Routine &amp; Schedule: infer work schedule type (fixed hours, flexible, shift work, etc.) and daily structure",
+        "Economic Behavior: assess spending tier from venue choices, transportation costs, and lifestyle signals",
+        "Social &amp; Lifestyle: identify social engagement patterns, leisure activities, and community involvement",
+        "Routine Stability: evaluate consistency and regularity of movement patterns over the observation period",
     ],
     3: [
-        "Synthesize factual features (Step 1) and behavioral patterns (Step 2) to infer household income bracket...",
+        "Synthesize factual features (Step 1) and behavioral patterns (Step 2) to infer household income bracket",
+        "Score location economic indicators: luxury / mid-range / budget venue distribution",
+        "Consider transportation mode signals, activity diversity, and temporal flexibility as income proxies",
+        "Output: <code>INCOME_PREDICTION</code> — a single income range with confidence rating (1–5)",
         "Output: <code>INCOME_REASONING</code> — evidence-grounded justification referencing specific mobility observations",
     ],
 }
@@ -692,7 +727,30 @@ def render_chain(s1_text, s2_text, s3_text, status="idle",
                 f'<div class="hct-arrow-label">{label}</div>'
                 f'<div class="hct-arrow-line"></div></div>')
 
+    flow_banner = (
+        '<div class="hct-flow-banner">'
+        '<div class="hct-flow-banner-title">Data Pipeline</div>'
+        '<div class="hct-flow-steps">'
+        '<div class="hct-flow-step">'
+        '<div class="hct-flow-step-label">Raw Data</div>'
+        '<div class="hct-flow-step-desc">Stay points + POI metadata<br><span style="color:#8090b0;font-size:10px">timestamps · venue names · activity types</span></div>'
+        '</div>'
+        '<div class="hct-flow-arrow">→</div>'
+        '<div class="hct-flow-step">'
+        '<div class="hct-flow-step-label">Processed</div>'
+        '<div class="hct-flow-step-desc">Detailed Activity Chronicles<br><span style="color:#8090b0;font-size:10px">weekly narratives · visit summaries</span></div>'
+        '</div>'
+        '<div class="hct-flow-arrow">→</div>'
+        '<div class="hct-flow-step" style="border-color:#b0bce8;background:#f4f6fb;">'
+        '<div class="hct-flow-step-label" style="color:#5060a0;">Prompt 1</div>'
+        '<div class="hct-flow-step-desc" style="color:#3a4a80;">Factual feature extraction<br><span style="color:#8090b0;font-size:10px">no interpretation · pattern identification</span></div>'
+        '</div>'
+        '</div>'
+        '</div>'
+    )
+
     html  = CHAIN_CSS + '<div class="hct-root">'
+    html += flow_banner
     html += stage('s1', 'Stage 01', 'Feature Extraction',    s1_body, s1_on, 1)
     html += arrow('behavioral abstraction', s2_on)
     html += stage('s2', 'Stage 02', 'Behavioral Analysis',   s2_body, s2_on, 2)
@@ -866,8 +924,8 @@ with gr.Blocks(title="HiCoTraj Demo") as app:
     gr.Markdown("## HiCoTraj — Trajectory Visualization & Hierarchical CoT Demo")
     gr.Markdown("*Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory*  ·  ACM SIGSPATIAL GeoGenAgent 2025")
     gr.Markdown("""
-**Dataset:** NUMOSIM[1]
-> [1]Stanford C, Adari S, Liao X, et al. *NUMoSim: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks.* ACM SIGSPATIAL Workshop on Geospatial Anomaly Detection, 2024.
+**Dataset:** NUMOSIM — a synthetic mobility dataset with realistic activity patterns across 6,000 agents.
+> Stanford C, Adari S, Liao X, et al. *NUMoSim: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks.* ACM SIGSPATIAL Workshop on Geospatial Anomaly Detection, 2024.
 """)
 
     gr.Markdown("### Select Agent")
@@ -884,7 +942,7 @@ with gr.Blocks(title="HiCoTraj Demo") as app:
         with gr.Column(scale=1):
             gr.Markdown("### Trajectory Map")
             map_out = gr.HTML()
-            gr.Markdown("### Rule-based Processed Data")
+            gr.Markdown("### NUMOSIM Raw Data")
             with gr.Tabs():
                 with gr.Tab("Summary"):
                     summary_out = gr.Textbox(lines=10, interactive=False, label="", show_label=False)
@@ -940,4 +998,4 @@ with gr.Blocks(title="HiCoTraj Demo") as app:
         outputs=[raw_out, show_all_btn]
     )
 
-app.launch(show_error=True, share=True, theme=gr.themes.Soft())
+app.launch(show_error=True, theme=gr.themes.Soft(), share=True)