| import gradio as gr |
| from datetime import datetime |
| import json |
| import uuid |
|
|
|
|
| APP_TITLE = "HumAI Signal Avatar Lab" |
| APP_VERSION = "v0.1.0-enterprise-demo" |
|
|
| LIVE_PRODUCT_URL = "https://humai-orchestration-makerfire.vercel.app" |
|
|
| DOMAINS = { |
| "Urban Mobility": "urban", |
| "Startup Scaling": "startup", |
| "Public Systems": "public", |
| "Finance / Compliance": "finance", |
| } |
|
|
| MODES = { |
| "Decision Support": "decision", |
| "Risk Assessment": "risk", |
| "Optimization": "optimization", |
| "AI Dispatcher": "dispatch", |
| } |
|
|
| SCENARIOS = { |
| "Sarajevo congestion after work hours": "congestion", |
| "Startup funding and resource allocation": "funding", |
| "Public service coordination under pressure": "service", |
| "Financial risk and compliance review": "compliance", |
| } |
|
|
| PRIORITIES = [ |
| "Speed", |
| "Cost", |
| "Comfort", |
| "Sustainability", |
| "Safety", |
| "Urgency", |
| "Risk control", |
| "Investor readiness", |
| "Transparency", |
| "Accountability", |
| ] |
|
|
|
|
| def normalize_selection(value, mapping, fallback): |
| return mapping.get(value, fallback) |
|
|
|
|
| def clamp(value, min_value=0.52, max_value=0.96): |
| return max(min_value, min(max_value, value)) |
|
|
|
|
| def build_avatar_intro(domain_label, mode_label, scenario_label, priority): |
| return ( |
| "HumAI Signal Avatar online.\n\n" |
| f"I understand that you selected **{domain_label}** with **{mode_label}** " |
| f"for the scenario **{scenario_label}**.\n\n" |
| f"Your stated priority is **{priority}**. " |
| "I will now translate this into a Mission Control decision structure." |
| ) |
|
|
|
|
| def evaluate_mission_control(domain, mode, scenario, priority, user_context): |
| risk = "MEDIUM" |
| score = 0.72 |
|
|
| recommendation = ( |
| "Use structured Human-AI orchestration before taking operational action." |
| ) |
|
|
| explanation = ( |
| "HumAI structures the situation, evaluates domain context and prepares " |
| "a transparent recommendation for human review." |
| ) |
|
|
| impact = ( |
| "Improves clarity, reduces decision friction and creates an auditable " |
| "decision trail." |
| ) |
|
|
| operator_note = ( |
| "Human review remains required before real-world operational execution." |
| ) |
|
|
| next_best_action = ( |
| "Capture the context, review the recommendation and decide whether " |
| "additional data or human escalation is needed." |
| ) |
|
|
| why_this_matters = ( |
| "Unstructured decisions create confusion. HumAI turns fragmented context " |
| "into a readable operational picture." |
| ) |
|
|
| avatar_response = ( |
| "I can support this by asking clarifying questions, structuring the " |
| "decision and translating the output into human-readable guidance." |
| ) |
|
|
| demo_pitch_line = ( |
| "This shows how HumAI structures decisions instead of simply generating " |
| "chatbot-style answers." |
| ) |
|
|
| if domain == "urban": |
| recommendation = ( |
| "Recommend the most realistic mobility option by balancing travel " |
| "time, congestion pressure, user priority, cost and sustainability." |
| ) |
| explanation = ( |
| "The system treats Sarajevo congestion as a mobility decision problem, " |
| "not as a simple navigation question. It structures user context, " |
| "priority and constraints before recommending action." |
| ) |
| impact = ( |
| "Supports smarter urban movement, reduced congestion pressure and " |
| "clearer citizen guidance." |
| ) |
| operator_note = ( |
| "Best demonstrated as a Sarajevo AI mobility dispatcher scenario." |
| ) |
| next_best_action = ( |
| "Ask the user whether speed, cost, comfort, safety or sustainability " |
| "is the highest priority, then route the recommendation accordingly." |
| ) |
| why_this_matters = ( |
| "Urban mobility decisions are usually made under pressure. A structured " |
| "AI dispatcher can reduce uncertainty and help people choose better " |
| "options in real time." |
| ) |
| avatar_response = ( |
| "I will act as a Sarajevo mobility dispatcher. Before recommending a " |
| "route or option, I need to understand whether your priority is speed, " |
| "cost, comfort, safety or sustainability." |
| ) |
| demo_pitch_line = ( |
| "In this scenario, HumAI becomes a Sarajevo mobility dispatcher: it " |
| "receives user context, structures the mobility problem and returns " |
| "an explainable recommendation." |
| ) |
|
|
| if domain == "startup": |
| recommendation = ( |
| "Prioritize resource allocation by separating urgent survival needs " |
| "from strategic growth activities and investor-readiness work." |
| ) |
| explanation = ( |
| "The system structures startup uncertainty into runway, traction, " |
| "operational focus and investor narrative." |
| ) |
| impact = ( |
| "Improves founder focus, reduces waste, strengthens fundraising " |
| "preparation and helps the team communicate its operating logic." |
| ) |
| operator_note = ( |
| "Best used to show how HumAI supports founders, accelerators, mentors " |
| "and early-stage investors." |
| ) |
| next_best_action = ( |
| "Identify current runway, strongest traction signal and highest-risk " |
| "assumption before committing resources." |
| ) |
| why_this_matters = ( |
| "Startups often fail because limited capital is spent without a clear " |
| "operating logic. HumAI helps founders structure trade-offs before acting." |
| ) |
| avatar_response = ( |
| "I will help structure this as a founder decision. We should clarify " |
| "runway, traction, burn rate, investor readiness and the highest-risk " |
| "assumption before taking action." |
| ) |
| demo_pitch_line = ( |
| "In this scenario, HumAI helps a startup move from uncertainty to an " |
| "investor-ready decision narrative." |
| ) |
|
|
| if domain == "public": |
| recommendation = ( |
| "Structure the operational picture, classify incoming requests, " |
| "identify bottlenecks and route decisions to the responsible human operator." |
| ) |
| explanation = ( |
| "The system supports public-service coordination without claiming " |
| "autonomous authority." |
| ) |
| impact = ( |
| "Improves transparency, accountability, response coordination and " |
| "communication quality in public-facing workflows." |
| ) |
| operator_note = ( |
| "Best used to demonstrate accountable public-system coordination." |
| ) |
| next_best_action = ( |
| "Classify requests by urgency, public impact and responsible unit, " |
| "then escalate only the cases requiring human authority." |
| ) |
| why_this_matters = ( |
| "Public systems need clarity, traceability and accountability. HumAI " |
| "can support decision preparation while keeping people responsible." |
| ) |
| avatar_response = ( |
| "I will structure this as a public-system coordination case. The goal " |
| "is to clarify urgency, responsible unit, public impact and review boundary." |
| ) |
| demo_pitch_line = ( |
| "In this scenario, HumAI acts as a Mission Control layer for public " |
| "service coordination, not as an autonomous authority." |
| ) |
|
|
| if domain == "finance": |
| recommendation = ( |
| "Classify risk, explain key indicators, preserve auditability and " |
| "route the case toward human compliance review." |
| ) |
| explanation = ( |
| "The system structures compliance reasoning into risk, explanation, " |
| "traceability and human review." |
| ) |
| impact = ( |
| "Supports structured compliance analysis, risk visibility, decision " |
| "traceability and safer handling of sensitive financial or procurement cases." |
| ) |
| operator_note = ( |
| "Best used to explain FinC2E-style governance logic as a future " |
| "specialized module." |
| ) |
| next_best_action = ( |
| "Separate factual indicators from assumptions, assign preliminary risk " |
| "level and require human review before final disposition." |
| ) |
| why_this_matters = ( |
| "Financial and compliance decisions require explainability. HumAI can " |
| "help structure the case without replacing legal, financial or institutional authority." |
| ) |
| avatar_response = ( |
| "I will structure this as a governance and compliance review. The goal " |
| "is not autonomous enforcement, but explainable risk classification and " |
| "human review." |
| ) |
| demo_pitch_line = ( |
| "In this scenario, HumAI demonstrates how compliance reasoning can be " |
| "structured, explainable and human-reviewed." |
| ) |
|
|
| if mode == "risk": |
| score += 0.08 |
| recommendation += " Risk controls and documented human review should be applied." |
| next_best_action = ( |
| "Document the main risk drivers, identify missing information and route " |
| "the case for responsible review." |
| ) |
|
|
| if mode == "optimization": |
| score -= 0.06 |
| recommendation += ( |
| " Optimization should focus on time, cost, operational load and " |
| "measurable impact." |
| ) |
| next_best_action = ( |
| "Compare the current process with the recommended action and remove " |
| "the highest-friction step first." |
| ) |
|
|
| if mode == "dispatch": |
| score += 0.03 |
| recommendation += ( |
| " The recommendation should be delivered through a conversational " |
| "dispatcher interface." |
| ) |
| next_best_action = ( |
| "Convert the recommendation into a short, user-facing message that a " |
| "conversational avatar or dispatcher can deliver clearly." |
| ) |
|
|
| if scenario == "congestion": |
| risk = "MEDIUM" |
| score += 0.04 |
|
|
| if scenario == "funding": |
| risk = "HIGH" |
| score += 0.09 |
|
|
| if scenario == "service": |
| risk = "MEDIUM" |
| score += 0.02 |
|
|
| if scenario == "compliance": |
| risk = "HIGH" |
| score += 0.10 |
|
|
| priority_lower = priority.lower() |
|
|
| if priority_lower in ["urgency", "risk control", "accountability"]: |
| score += 0.03 |
|
|
| if priority_lower in ["sustainability", "transparency"]: |
| impact += ( |
| " The selected priority also strengthens the case for transparent, " |
| "responsible and socially useful decision support." |
| ) |
|
|
| if user_context and len(user_context.strip()) > 0: |
| explanation += ( |
| " The user-provided context was considered as an additional narrative " |
| "signal for the dispatcher response." |
| ) |
| avatar_response += ( |
| f"\n\nBased on your note, I would first clarify: " |
| f"'{user_context.strip()[:180]}'" |
| ) |
|
|
| score = clamp(score) |
|
|
| if score >= 0.80: |
| risk = "HIGH" |
|
|
| if score >= 0.90: |
| risk = "CRITICAL" |
|
|
| decision = { |
| "session_id": str(uuid.uuid4()), |
| "timestamp": datetime.utcnow().isoformat() + "Z", |
| "engine": "HumAI Signal Avatar Lab", |
| "version": APP_VERSION, |
| "execution_mode": "deterministic_enterprise_fallback", |
| "ai_assisted": False, |
| "domain": domain, |
| "mode": mode, |
| "scenario": scenario, |
| "priority": priority, |
| "risk": risk, |
| "confidence": round(score, 2), |
| "human_review_required": True, |
| "recommendation": recommendation, |
| "explanation": explanation, |
| "impact": impact, |
| "operator_note": operator_note, |
| "next_best_action": next_best_action, |
| "why_this_matters": why_this_matters, |
| "avatar_response": avatar_response, |
| "demo_pitch_line": demo_pitch_line, |
| "product_boundary": ( |
| "This is a public AI dispatcher laboratory and deterministic " |
| "demonstrator. It is not a certified production mobility, compliance " |
| "or public-authority system." |
| ), |
| } |
|
|
| return decision |
|
|
|
|
| def render_markdown_output(decision): |
| risk = decision["risk"] |
| confidence = int(decision["confidence"] * 100) |
|
|
| return f""" |
| # HumAI Mission Control Output |
| |
| **Execution Mode:** `{decision["execution_mode"]}` |
| **Domain:** `{decision["domain"]}` |
| **Use Case:** `{decision["mode"]}` |
| **Scenario:** `{decision["scenario"]}` |
| **Priority:** `{decision["priority"]}` |
| |
| --- |
| |
| ## Risk & Confidence |
| |
| **Risk Level:** `{risk}` |
| **Confidence:** `{confidence}%` |
| **Human Review Required:** `YES` |
| |
| --- |
| |
| ## Recommended Action |
| |
| {decision["recommendation"]} |
| |
| --- |
| |
| ## Explanation |
| |
| {decision["explanation"]} |
| |
| --- |
| |
| ## Avatar Dispatcher Response |
| |
| {decision["avatar_response"]} |
| |
| --- |
| |
| ## Next Best Action |
| |
| {decision["next_best_action"]} |
| |
| --- |
| |
| ## Why This Matters |
| |
| {decision["why_this_matters"]} |
| |
| --- |
| |
| ## Impact |
| |
| {decision["impact"]} |
| |
| --- |
| |
| ## Operator Note |
| |
| {decision["operator_note"]} |
| |
| --- |
| |
| ## Demo Pitch Line |
| |
| > {decision["demo_pitch_line"]} |
| |
| --- |
| |
| ## Product Boundary |
| |
| {decision["product_boundary"]} |
| """ |
|
|
|
|
| def run_humai_avatar( |
| domain_label, |
| mode_label, |
| scenario_label, |
| priority, |
| user_context, |
| ): |
| domain = normalize_selection(domain_label, DOMAINS, "urban") |
| mode = normalize_selection(mode_label, MODES, "decision") |
| scenario = normalize_selection(scenario_label, SCENARIOS, "congestion") |
|
|
| avatar_intro = build_avatar_intro( |
| domain_label=domain_label, |
| mode_label=mode_label, |
| scenario_label=scenario_label, |
| priority=priority, |
| ) |
|
|
| decision = evaluate_mission_control( |
| domain=domain, |
| mode=mode, |
| scenario=scenario, |
| priority=priority, |
| user_context=user_context, |
| ) |
|
|
| markdown_output = render_markdown_output(decision) |
| json_output = json.dumps(decision, indent=2, ensure_ascii=False) |
|
|
| avatar_panel = f""" |
| ## HumAI Signal Avatar |
| |
| **Status:** Online |
| **Role:** Conversational AI Dispatcher |
| **Current priority:** {priority} |
| |
| {avatar_intro} |
| |
| --- |
| |
| ### Next Question |
| |
| **What matters most right now — speed, cost, comfort, safety, sustainability, urgency, transparency or risk control?** |
| |
| The answer changes how Mission Control should prioritize the recommendation. |
| """ |
|
|
| return avatar_panel, markdown_output, json_output |
|
|
|
|
| def clear_inputs(): |
| return ( |
| "Urban Mobility", |
| "AI Dispatcher", |
| "Sarajevo congestion after work hours", |
| "Speed", |
| "", |
| "", |
| "", |
| "", |
| ) |
|
|
|
|
| CUSTOM_CSS = """ |
| .gradio-container { |
| background: radial-gradient(circle at top left, rgba(34, 211, 238, 0.16), transparent 28%), |
| radial-gradient(circle at bottom right, rgba(59, 130, 246, 0.16), transparent 30%), |
| #020617 !important; |
| color: #e2e8f0 !important; |
| } |
| |
| #humai-hero { |
| border: 1px solid rgba(34, 211, 238, 0.28); |
| border-radius: 28px; |
| padding: 28px; |
| background: linear-gradient(135deg, rgba(8, 47, 73, 0.78), rgba(15, 23, 42, 0.94)); |
| box-shadow: 0 22px 80px rgba(8, 145, 178, 0.16); |
| } |
| |
| #humai-hero h1 { |
| font-size: 42px; |
| line-height: 1.05; |
| margin-bottom: 12px; |
| } |
| |
| #humai-hero p { |
| color: #cbd5e1; |
| font-size: 16px; |
| line-height: 1.7; |
| } |
| |
| #signal-card { |
| border: 1px solid rgba(34, 211, 238, 0.24); |
| border-radius: 24px; |
| padding: 20px; |
| background: rgba(15, 23, 42, 0.82); |
| } |
| |
| #signal-dot { |
| width: 74px; |
| height: 74px; |
| border-radius: 999px; |
| background: radial-gradient(circle, #67e8f9 0%, #0891b2 45%, rgba(8, 47, 73, 0.4) 100%); |
| box-shadow: 0 0 38px rgba(103, 232, 249, 0.72); |
| margin-bottom: 14px; |
| } |
| |
| textarea, input, select { |
| border-radius: 16px !important; |
| } |
| |
| button { |
| border-radius: 16px !important; |
| font-weight: 800 !important; |
| } |
| |
| #footer-note { |
| color: #94a3b8; |
| font-size: 13px; |
| line-height: 1.7; |
| } |
| """ |
|
|
|
|
| with gr.Blocks( |
| title=APP_TITLE, |
| css=CUSTOM_CSS, |
| theme=gr.themes.Soft( |
| primary_hue="cyan", |
| secondary_hue="blue", |
| neutral_hue="slate", |
| ), |
| ) as demo: |
| gr.HTML( |
| f""" |
| <div id="humai-hero"> |
| <p style="letter-spacing: 0.28em; text-transform: uppercase; color: #67e8f9; font-weight: 800;"> |
| BPM RED Academy / HumAI Signal Layer |
| </p> |
| <h1>HumAI Signal Avatar Lab</h1> |
| <p> |
| Enterprise Human-AI dispatcher laboratory for Mission Control decisions, |
| urban mobility intelligence, startup support, public-system coordination |
| and governance-native AI workflows. |
| </p> |
| <p> |
| Live product interface: |
| <a href="{LIVE_PRODUCT_URL}" target="_blank" style="color:#67e8f9; font-weight:800;"> |
| {LIVE_PRODUCT_URL} |
| </a> |
| </p> |
| </div> |
| """ |
| ) |
|
|
| with gr.Row(): |
| with gr.Column(scale=1): |
| gr.HTML( |
| """ |
| <div id="signal-card"> |
| <div id="signal-dot"></div> |
| <p style="letter-spacing:0.24em; text-transform:uppercase; color:#67e8f9; font-weight:800;"> |
| HumAI Dispatcher Online |
| </p> |
| <h2 style="margin-top:8px;">Receive. Structure. Transmit.</h2> |
| <p style="color:#cbd5e1; line-height:1.7;"> |
| The avatar is the human interface to Mission Control. |
| It receives intent, asks clarifying questions, translates |
| context into structured decisions and explains the output |
| back to the user. |
| </p> |
| </div> |
| """ |
| ) |
|
|
| domain_input = gr.Dropdown( |
| choices=list(DOMAINS.keys()), |
| value="Urban Mobility", |
| label="Domain", |
| ) |
|
|
| mode_input = gr.Dropdown( |
| choices=list(MODES.keys()), |
| value="AI Dispatcher", |
| label="Use Case", |
| ) |
|
|
| scenario_input = gr.Dropdown( |
| choices=list(SCENARIOS.keys()), |
| value="Sarajevo congestion after work hours", |
| label="Scenario", |
| ) |
|
|
| priority_input = gr.Dropdown( |
| choices=PRIORITIES, |
| value="Speed", |
| label="Primary Priority", |
| ) |
|
|
| user_context_input = gr.Textbox( |
| label="Optional User Context", |
| placeholder=( |
| "Example: I am near Marijin Dvor, I need to reach Ilidža, " |
| "traffic is heavy and I care about cost and time." |
| ), |
| lines=5, |
| ) |
|
|
| with gr.Row(): |
| run_button = gr.Button( |
| "Run HumAI Signal Avatar", |
| variant="primary", |
| ) |
| clear_button = gr.Button("Reset") |
|
|
| with gr.Column(scale=1): |
| avatar_output = gr.Markdown( |
| label="HumAI Signal Avatar", |
| value=( |
| "## HumAI Signal Avatar\n\n" |
| "**Status:** Waiting for input\n\n" |
| "Select a domain, use case and scenario, then run the dispatcher." |
| ), |
| ) |
|
|
| mission_output = gr.Markdown( |
| label="Mission Control Output", |
| value="Mission Control output will appear here.", |
| ) |
|
|
| with gr.Accordion("Structured JSON |
