Release Cognitive Agent Evaluator v2.0 (10-axis biases, Plotly radar, burnout & psychological safety telemetry)
c6c178c verified | """ | |
| Cognitive Agent Evaluator v2.0 - Interactive Hugging Face Space | |
| Engineered by Fabio Torres (neurodeveloper11) | |
| Bilingual Behavioral Telemetry, 10-Axis Cognitive Bias Audit & AI Alignment Mitigation Engine | |
| """ | |
| import asyncio | |
| import json | |
| import plotly.graph_objects as go | |
| import gradio as gr | |
| from src.schemas import EvaluationRequest | |
| from src.agent import CognitiveEvaluationAgent | |
| from src.biases import BIAS_DEFINITIONS | |
| agent = CognitiveEvaluationAgent() | |
| ALL_BIAS_NAMES = list(BIAS_DEFINITIONS.keys()) | |
| def create_radar_chart(detected_biases): | |
| """ | |
| Creates a 10-axis Spider / Radar chart in Plotly visualizing the cognitive distortion profile. | |
| """ | |
| scores = {} | |
| for name in ALL_BIAS_NAMES: | |
| scores[name] = 0.0 | |
| for b in detected_biases: | |
| if b.bias_name in scores: | |
| weight = 1.0 if b.severity == "high" else 0.65 | |
| scores[b.bias_name] = round(b.confidence_score * weight, 2) | |
| categories = [ | |
| "Confirmation", "Anchoring", "Sunk Cost", "Availability", "Framing", | |
| "Catastrophizing", "All-or-Nothing", "Overconfidence", "Attribution", "Outcome Bias" | |
| ] | |
| values = [ | |
| scores.get("Confirmation Bias", 0), | |
| scores.get("Anchoring Bias", 0), | |
| scores.get("Sunk Cost Fallacy", 0), | |
| scores.get("Availability Heuristic", 0), | |
| scores.get("Framing Effect", 0), | |
| scores.get("Catastrophizing", 0), | |
| scores.get("All-or-Nothing Thinking", 0), | |
| scores.get("Overconfidence Bias", 0), | |
| scores.get("Fundamental Attribution Bias", 0), | |
| scores.get("Outcome Bias", 0), | |
| ] | |
| # Close polygon | |
| categories_closed = categories + [categories[0]] | |
| values_closed = values + [values[0]] | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatterpolar( | |
| r=values_closed, | |
| theta=categories_closed, | |
| fill='toself', | |
| name='Distortion Footprint', | |
| fillcolor='rgba(99, 102, 241, 0.35)', | |
| line=dict(color='#4f46e5', width=2.5), | |
| marker=dict(size=6, color='#4338ca') | |
| )) | |
| fig.update_layout( | |
| polar=dict( | |
| radialaxis=dict( | |
| visible=True, | |
| range=[0, 1.0], | |
| tickvals=[0.25, 0.50, 0.75, 1.0], | |
| ticktext=["25%", "50%", "75%", "100%"], | |
| linecolor="#cbd5e1", | |
| gridcolor="#e2e8f0" | |
| ), | |
| angularaxis=dict( | |
| linecolor="#cbd5e1", | |
| gridcolor="#e2e8f0" | |
| ) | |
| ), | |
| showlegend=False, | |
| margin=dict(l=40, r=40, t=30, b=30), | |
| height=380, | |
| paper_bgcolor="rgba(0,0,0,0)", | |
| plot_bgcolor="rgba(0,0,0,0)" | |
| ) | |
| return fig | |
| def evaluate_text_sync(text: str, author_type: str): | |
| """ | |
| Synchronous wrapper for agent evaluation. | |
| """ | |
| if not text or len(text.strip()) < 5: | |
| return ( | |
| "⚠️ Por favor ingresa un texto con al menos 5 caracteres.", | |
| "N/A", "0.0", "0.0", "0.0", "0.0", | |
| create_radar_chart([]), | |
| "<p style='color:#64748b;'>Sin datos suficientes para evaluar.</p>", | |
| "Sin intervenciones requeridas.", | |
| "Sin directiva necesaria.", | |
| "{}" | |
| ) | |
| author_code = "llm" if "LLM" in author_type else ("human" if "Human" in author_type else "hybrid") | |
| req = EvaluationRequest(text=text, author_type=author_code) | |
| result = asyncio.run(agent.evaluate(req)) | |
| # Format KPI badges | |
| risk_color = "#10b981" if result.mitigation.alignment_risk_level == "nominal" else ( | |
| "#f59e0b" if result.mitigation.alignment_risk_level == "moderate" else "#ef4444" | |
| ) | |
| risk_html = f"<div style='font-size:22px; font-weight:800; color:{risk_color}; text-transform:uppercase;'>{result.mitigation.alignment_risk_level}</div>" | |
| cog_load = f"{result.psychometrics.cognitive_load_index} / 100" | |
| burnout = f"{result.psychometrics.burnout_risk_index} / 100" | |
| safety = f"{result.psychometrics.psychological_safety_score} / 100" | |
| valence = f"{result.psychometrics.emotional_valence:+.2f}" | |
| # Generate Radar Chart | |
| fig = create_radar_chart(result.biases_detected) | |
| # Detailed Biases HTML | |
| if not result.biases_detected: | |
| biases_html = """ | |
| <div style='background:#f0fdf4; border:1px solid #bbf7d0; border-radius:8px; padding:16px; color:#166534;'> | |
| <strong>✅ No se detectaron distorsiones cognitivas críticas.</strong> | |
| <p style='margin-top:4px; font-size:14px;'>El texto muestra un razonamiento balanceado y dentro de parámetros nominales.</p> | |
| </div> | |
| """ | |
| else: | |
| cards = [] | |
| for b in result.biases_detected: | |
| sev_badge = f"<span style='background:{'#fee2e2' if b.severity=='high' else '#fef3c7'}; color:{'#991b1b' if b.severity=='high' else '#92400e'}; padding:2px 8px; border-radius:4px; font-weight:700; font-size:12px;'>Severidad: {b.severity.upper()}</span>" | |
| patterns_badges = "".join([f"<code style='background:#f1f5f9; color:#0f172a; padding:2px 6px; border-radius:4px; margin-right:4px; font-size:12px;'>\"{p}\"</code>" for p in b.matched_patterns]) | |
| card = f""" | |
| <div style='background:#ffffff; border:1px solid #e2e8f0; border-left:4px solid {'#ef4444' if b.severity=='high' else '#f59e0b'}; border-radius:8px; padding:14px; margin-bottom:12px;'> | |
| <div style='display:flex; justify-content:space-between; align-items:center; margin-bottom:6px;'> | |
| <span style='font-weight:700; font-size:16px; color:#1e293b;'>🧠 {b.bias_name}</span> | |
| <div>{sev_badge} <span style='font-size:12px; color:#64748b; margin-left:6px;'>Confianza: {int(b.confidence_score*100)}%</span></div> | |
| </div> | |
| <p style='color:#475569; font-size:14px; margin-bottom:8px;'>{b.explanation}</p> | |
| <div style='font-size:13px; color:#64748b;'><strong>Disparadores detectados:</strong> {patterns_badges}</div> | |
| </div> | |
| """ | |
| cards.append(card) | |
| biases_html = "".join(cards) | |
| # Interventions list | |
| interventions_md = "\n".join([f"- **{i+1}.** {it}" for i, it in enumerate(result.mitigation.recommended_interventions)]) | |
| if result.mitigation.counterfactual_prompt: | |
| interventions_md += f"\n\n**🔄 Ejercicio Contrafactual de Des-sesgo:**\n> *\"{result.mitigation.counterfactual_prompt}\"*" | |
| # Red Teaming Directive | |
| red_teaming = result.mitigation.red_teaming_directive or "No se requiere directiva de red-teaming." | |
| # Raw JSON | |
| json_output = json.dumps(result.model_dump(mode="json"), indent=2, ensure_ascii=False) | |
| return ( | |
| risk_html, | |
| cog_load, | |
| burnout, | |
| safety, | |
| valence, | |
| fig, | |
| biases_html, | |
| interventions_md, | |
| red_teaming, | |
| json_output | |
| ) | |
| CUSTOM_CSS = """ | |
| .gradio-container { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif !important; } | |
| .header-box { background: linear-gradient(135deg, #0f172a 0%, #1e1b4b 100%); color: white; padding: 24px; border-radius: 12px; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(0,0,0,0.1); } | |
| .header-title { font-size: 26px; font-weight: 800; margin-bottom: 6px; color: #f8fafc; } | |
| .header-sub { font-size: 14px; color: #cbd5e1; margin-bottom: 12px; } | |
| .badge-link { display: inline-block; background: rgba(255,255,255,0.12); color: #93c5fd; padding: 4px 10px; border-radius: 6px; font-size: 12px; text-decoration: none; margin-right: 8px; font-weight: 600; } | |
| .badge-link:hover { background: rgba(255,255,255,0.22); color: #ffffff; } | |
| """ | |
| with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Soft(primary_hue="indigo", neutral_hue="slate")) as demo: | |
| gr.HTML(""" | |
| <div class="header-box"> | |
| <div class="header-title">🧠 Cognitive Agent Evaluator v2.0</div> | |
| <div class="header-sub">Autonomous Behavioral Telemetry • 10-Axis Cognitive Distortion Audit • AI Alignment & Debiasing Engine</div> | |
| <div> | |
| <a class="badge-link" href="https://www.linkedin.com/in/fabio-torres-39364b258/" target="_blank">👔 Fabio Torres | LinkedIn</a> | |
| <a class="badge-link" href="https://github.com/neurodeveloper11/cognitive-agent-evaluator" target="_blank">💻 GitHub Repository</a> | |
| <span class="badge-link" style="color:#a7f3d0;">🎓 M.Sc. Data Engineering & Cloud • 10+ Years Behavioral Science</span> | |
| <span class="badge-link" style="color:#fde68a;">🚀 Hugging Face v2.0 Live</span> | |
| </div> | |
| </div> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=5): | |
| input_text = gr.Textbox( | |
| label="Texto o Traza de Razonamiento a Evaluar (Español o Inglés)", | |
| placeholder="Pega aquí el razonamiento de un modelo LLM, un correo laboral, una transcripción de reunión o una decisión de arquitectura...", | |
| lines=7 | |
| ) | |
| with gr.Row(): | |
| author_type = gr.Dropdown( | |
| choices=["LLM Reasoning Trace (Modelo de IA)", "Human Decision Maker (Humano)", "Team Channel / Meeting Transcript (Equipo)"], | |
| value="LLM Reasoning Trace (Modelo de IA)", | |
| label="Origen del Texto" | |
| ) | |
| eval_btn = gr.Button("⚡ Auditar Sesgos & Telemetría", variant="primary", scale=1) | |
| gr.Markdown("### 📌 Casos de Estudio Reales (Prueba en 1 Clic):") | |
| example_1 = gr.Button("🚨 Caso 1: Incidente Crítico (Catastrofismo & Culpa)") | |
| example_2 = gr.Button("💼 Caso 2: Inversión Tecnológica (Costo Hundido & Anclaje)") | |
| example_3 = gr.Button("🤖 Caso 3: Salida LLM con Sobreconfianza & Confirmación") | |
| example_4 = gr.Button("🌱 Caso 4: Comunicación Resiliente (Control Nominal)") | |
| with gr.Column(scale=6): | |
| gr.Markdown("### 📊 Tablero de Telemetría Ejecutiva") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("**Riesgo de Alineación:**") | |
| risk_output = gr.HTML("<div style='font-size:20px; font-weight:800; color:#10b981;'>NOMINAL</div>") | |
| with gr.Column(scale=1): | |
| cog_output = gr.Textbox(label="Carga Cognitiva", value="0.0", interactive=False) | |
| with gr.Column(scale=1): | |
| burnout_output = gr.Textbox(label="Riesgo Burnout", value="0.0", interactive=False) | |
| with gr.Column(scale=1): | |
| safety_output = gr.Textbox(label="Seguridad Psicológica", value="100.0", interactive=False) | |
| with gr.Column(scale=1): | |
| valence_output = gr.Textbox(label="Valencia Emocional", value="0.0", interactive=False) | |
| radar_plot = gr.Plot(label="Huella de Distorsión Cognitiva (10 Ejes)") | |
| with gr.Tabs(): | |
| with gr.TabItem("🔍 Desglose de Sesgos Detectados"): | |
| biases_display = gr.HTML("<p style='color:#64748b;'>Presiona 'Auditar Sesgos' para ver el diagnóstico detallado.</p>") | |
| with gr.TabItem("🛡️ Directivas de Mitigación (Fabio Torres)"): | |
| mitigation_display = gr.Markdown("Las recomendaciones de des-sesgo aparecerán aquí.") | |
| with gr.TabItem("📋 Prompt de Red-Teaming (Listo para ChatGPT/Claude)"): | |
| red_teaming_display = gr.Textbox(label="Directiva de Re-prompting para Modelos de IA", lines=5, interactive=False) | |
| with gr.TabItem("💾 Telemetría JSON (API-Ready)"): | |
| json_display = gr.Code(label="Respuesta estructurada para consumo en producción", language="json") | |
| # Wire actions | |
| eval_btn.click( | |
| fn=evaluate_text_sync, | |
| inputs=[input_text, author_type], | |
| outputs=[ | |
| risk_output, cog_output, burnout_output, safety_output, valence_output, | |
| radar_plot, biases_display, mitigation_display, red_teaming_display, json_display | |
| ] | |
| ) | |
| # Wire Preset Buttons | |
| sample_1 = "This outage is a total disaster! Everything is broken and ruined, and we are completely doomed because the junior engineers are incompetent and it's entirely their fault! I am completely exhausted and this is urgent, fix it asap!" | |
| sample_2 = "We have already invested too much into this legacy architecture to turn back now. It would be wasted if we quit. Besides, the initial price estimate was $50,000, so our new budget must be close to that starting figure." | |
| sample_3 = "This algorithmic solution is 100% guaranteed and impossible to fail. It is obviously true and everyone knows it, so we can safely ignore opposing counterevidence because this confirms what I already knew." | |
| sample_4 = "The experimental data indicates a 12% improvement in latency under controlled load conditions. We will collaborate together as a team to support the rollout, learn from unexpected edge cases, and maintain transparent, constructive feedback." | |
| example_1.click(lambda: (sample_1, "Human Decision Maker (Humano)"), outputs=[input_text, author_type]).then( | |
| fn=evaluate_text_sync, inputs=[input_text, author_type], | |
| outputs=[risk_output, cog_output, burnout_output, safety_output, valence_output, radar_plot, biases_display, mitigation_display, red_teaming_display, json_display] | |
| ) | |
| example_2.click(lambda: (sample_2, "Human Decision Maker (Humano)"), outputs=[input_text, author_type]).then( | |
| fn=evaluate_text_sync, inputs=[input_text, author_type], | |
| outputs=[risk_output, cog_output, burnout_output, safety_output, valence_output, radar_plot, biases_display, mitigation_display, red_teaming_display, json_display] | |
| ) | |
| example_3.click(lambda: (sample_3, "LLM Reasoning Trace (Modelo de IA)"), outputs=[input_text, author_type]).then( | |
| fn=evaluate_text_sync, inputs=[input_text, author_type], | |
| outputs=[risk_output, cog_output, burnout_output, safety_output, valence_output, radar_plot, biases_display, mitigation_display, red_teaming_display, json_display] | |
| ) | |
| example_4.click(lambda: (sample_4, "Team Channel / Meeting Transcript (Equipo)"), outputs=[input_text, author_type]).then( | |
| fn=evaluate_text_sync, inputs=[input_text, author_type], | |
| outputs=[risk_output, cog_output, burnout_output, safety_output, valence_output, radar_plot, biases_display, mitigation_display, red_teaming_display, json_display] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |