Delete app.py

app.py

@@ -1,721 +0,0 @@
-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 Output", open=False):
-        json_output = gr.Code(
-            label="Mission Control JSON",
-            language="json",
-            value="{}",
-        )
-
-    gr.HTML(
-        """
-        <div id="footer-note">
-            <p>
-                <strong>Public demonstrator note:</strong>
-                This Hugging Face Space currently runs as a deterministic enterprise
-                avatar laboratory. It is designed for demo safety, explainability and
-                future integration with Gemini / Vertex AI, Firebase, Google Maps,
-                Azure AI Foundry, Hugging Face model artifacts, NVIDIA-oriented
-                inference infrastructure and PitchAvatar or custom avatar layers.
-            </p>
-            <p>
-                Product boundary: HumAI is advisory, human-in-the-loop and
-                demonstration-oriented in this version. It does not replace human
-                judgment, public authority, legal review or operational command.
-            </p>
-        </div>
-        """
-    )
-
-    run_button.click(
-        fn=run_humai_avatar,
-        inputs=[
-            domain_input,
-            mode_input,
-            scenario_input,
-            priority_input,
-            user_context_input,
-        ],
-        outputs=[
-            avatar_output,
-            mission_output,
-            json_output,
-        ],
-    )
-
-    clear_button.click(
-        fn=clear_inputs,
-        inputs=[],
-        outputs=[
-            domain_input,
-            mode_input,
-            scenario_input,
-            priority_input,
-            user_context_input,
-            avatar_output,
-            mission_output,
-            json_output,
-        ],
-    )
-
-
-if __name__ == "__main__":
-    demo.launch()