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Cortex-Nexus-Bridge

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SperanzaMax commited on Apr 10

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app.py +408 -0

app.py CHANGED Viewed

@@ -62,10 +62,17 @@ async def dashboard_home():
             .stat-label { font-size: 0.75rem; color: #94a3b8; font-weight: 600; text-transform: uppercase; }
             .stat-value { font-size: 1.5rem; font-weight: 800; color: #38bdf8; margin-top: 5px; }
             .status-dot { height: 10px; width: 10px; background-color: #10b981; border-radius: 50%; display: inline-block; margin-right: 8px; }
         </style>
     </head>
     <body>
         <div class="container">
             <p style="text-align: center; font-size: 0.8rem; color: #64748b;"><span class="status-dot"></span> SISTEMA ONLINE</p>
             <h1>Córtex-Nexus Live</h1>
             <div class="stats">
@@ -274,3 +281,404 @@ async def chat_proxy(request: Request):
         return resp_json
     except Exception as e:
         return JSONResponse(status_code=500, content={"error": str(e)})

             .stat-label { font-size: 0.75rem; color: #94a3b8; font-weight: 600; text-transform: uppercase; }
             .stat-value { font-size: 1.5rem; font-weight: 800; color: #38bdf8; margin-top: 5px; }
             .status-dot { height: 10px; width: 10px; background-color: #10b981; border-radius: 50%; display: inline-block; margin-right: 8px; }
+            .nav { display: flex; justify-content: center; gap: 12px; margin-bottom: 16px; }
+            .nav a { color: #94a3b8; text-decoration: none; padding: 6px 14px; border-radius: 8px; font-size: 0.8rem; background: #334155; }
+            .nav a.active, .nav a:hover { background: #38bdf8; color: #fff; }
         </style>
     </head>
     <body>
         <div class="container">
+            <div class="nav">
+                <a href="/" class="active">🧠 Live</a>
+                <a href="/analytics">🔬 Analytics</a>
+            </div>
             <p style="text-align: center; font-size: 0.8rem; color: #64748b;"><span class="status-dot"></span> SISTEMA ONLINE</p>
             <h1>Córtex-Nexus Live</h1>
             <div class="stats">
         return resp_json
     except Exception as e:
         return JSONResponse(status_code=500, content={"error": str(e)})
+# ============================================================
+# ANALYTICS API
+# ============================================================
+import math
+import csv
+import io
+from fastapi.responses import StreamingResponse
+def _get_snapshots(limit=None, hours=None):
+    """Helper: obtiene snapshots con filtro opcional de tiempo"""
+    conn = sqlite3.connect(DB_PATH)
+    conn.row_factory = sqlite3.Row
+    c = conn.cursor()
+    if hours:
+        since = (datetime.datetime.utcnow() - datetime.timedelta(hours=hours)).isoformat()
+        c.execute("SELECT * FROM snapshots WHERE timestamp > ? ORDER BY id ASC", (since,))
+    elif limit:
+        c.execute("SELECT * FROM snapshots ORDER BY id DESC LIMIT ?", (limit,))
+    else:
+        c.execute("SELECT * FROM snapshots ORDER BY id ASC")
+    rows = [dict(r) for r in c.fetchall()]
+    conn.close()
+    return rows if not limit or hours else rows[::-1]
+def _calc_stats(values):
+    """Calcula media, std, min, max, tendencia de una lista de floats"""
+    if not values:
+        return {"mean": 0, "std": 0, "min": 0, "max": 0, "trend": 0}
+    n = len(values)
+    mean = sum(values) / n
+    variance = sum((x - mean) ** 2 for x in values) / max(n - 1, 1)
+    std = math.sqrt(variance)
+    # Tendencia: pendiente de regresión lineal simple
+    if n > 1:
+        x_mean = (n - 1) / 2
+        num = sum((i - x_mean) * (v - mean) for i, v in enumerate(values))
+        den = sum((i - x_mean) ** 2 for i in range(n))
+        trend = num / den if den != 0 else 0
+    else:
+        trend = 0
+    return {
+        "mean": round(mean, 4),
+        "std": round(std, 4),
+        "min": round(min(values), 4),
+        "max": round(max(values), 4),
+        "trend": round(trend, 6)
+    }
+def _detect_patterns(rows):
+    """Detecta patrones activos en los datos"""
+    patterns = []
+    if len(rows) < 5:
+        return patterns
+    qualias = [r["qualia"] for r in rows]
+    frustraciones = [r["frustracion"] for r in rows]
+    fatigas = [r["fatiga"] for r in rows]
+    confianzas = [r["autoConfianza"] for r in rows]
+    # Meseta emocional: volatilidad < 0.01 en últimas 50 épocas
+    recent_q = qualias[-min(50, len(qualias)):]
+    if len(recent_q) > 10:
+        vol = _calc_stats(recent_q)["std"]
+        if vol < 0.01:
+            patterns.append({"type": "MESETA", "severity": "warning", "msg": f"Qualia estancado (vol={vol:.4f}). Necesita estímulos nuevos."})
+    # Ciclo de frustración: frustración sube >3x en ventana de 20
+    if len(frustraciones) >= 20:
+        inicio = sum(frustraciones[-20:-15]) / 5 if frustraciones[-20:-15] else 0.001
+        fin = sum(frustraciones[-5:]) / 5
+        if inicio > 0.001 and fin / max(inicio, 0.001) > 3:
+            patterns.append({"type": "FRUSTRACION_CICLO", "severity": "danger", "msg": f"Frustración se triplicó en 20 épocas ({inicio:.3f}→{fin:.3f})"})
+    # Wireheading: qualia sube monótonamente sin caídas
+    if len(qualias) >= 20:
+        increases = sum(1 for i in range(1, min(20, len(qualias))) if qualias[-i] >= qualias[-i-1])
+        if increases >= 18:
+            patterns.append({"type": "WIREHEADING", "severity": "danger", "msg": "Qualia sube sin parar. Anti-wireheading podría estar fallando."})
+    # Fatiga terminal: fatiga > 0.8 sostenida
+    if len(fatigas) >= 10:
+        high_fatigue = sum(1 for f in fatigas[-10:] if f > 0.8)
+        if high_fatigue >= 8:
+            patterns.append({"type": "FATIGA_TERMINAL", "severity": "danger", "msg": "Fatiga crítica. El sistema necesita 'dormir' (consolidación)."})
+    # Personalidad emergente (positivo): drift > 5% en confianza
+    if len(confianzas) >= 50:
+        inicio_c = sum(confianzas[:10]) / 10
+        fin_c = sum(confianzas[-10:]) / 10
+        drift = abs(fin_c - inicio_c) / max(inicio_c, 0.001)
+        if drift > 0.05:
+            direction = "↑" if fin_c > inicio_c else "↓"
+            patterns.append({"type": "PERSONALIDAD_EMERGENTE", "severity": "success", "msg": f"Drift de confianza {direction} ({inicio_c:.3f}→{fin_c:.3f}, {drift*100:.1f}%)"})
+    return patterns
+@app.get("/api/analytics")
+async def get_analytics():
+    """Métricas derivadas con ventanas temporales"""
+    try:
+        windows = {}
+        for label, hours in [("1h", 1), ("6h", 6), ("24h", 24), ("7d", 168)]:
+            rows = _get_snapshots(hours=hours)
+            if not rows:
+                windows[label] = {"count": 0, "metrics": {}}
+                continue
+            metrics = {}
+            for col in ["qualia", "frustracion", "autoConfianza", "fatiga", "aburrimiento"]:
+                vals = [r.get(col, 0) for r in rows]
+                metrics[col] = _calc_stats(vals)
+            # Métricas derivadas
+            qualias = [r.get("qualia", 0) for r in rows]
+            fatigas = [r.get("fatiga", 0) for r in rows]
+            ratios = [q / max(f, 0.01) for q, f in zip(qualias, fatigas)]
+            metrics["ratio_qualia_fatiga"] = _calc_stats(ratios)
+            # Entropía emocional (Shannon)
+            entropies = []
+            for r in rows:
+                vec = [max(r.get(k, 0.001), 0.001) for k in ["qualia", "frustracion", "autoConfianza", "fatiga", "aburrimiento"]]
+                total = sum(vec)
+                probs = [v / total for v in vec]
+                entropy = -sum(p * math.log2(p) for p in probs if p > 0)
+                entropies.append(entropy)
+            metrics["entropia_emocional"] = _calc_stats(entropies)
+            windows[label] = {"count": len(rows), "metrics": metrics}
+        # Patrones activos
+        all_rows = _get_snapshots(hours=24)
+        patterns = _detect_patterns(all_rows)
+        # Arquetipo actual
+        arquetipo = {}
+        if vector_global and vector_global.memoria:
+            arquetipo = vector_global.memoria.cargar_meta_vector()
+        return {
+            "windows": windows,
+            "patterns": patterns,
+            "arquetipo": arquetipo,
+            "timestamp": datetime.datetime.utcnow().isoformat()
+        }
+    except Exception as e:
+        return {"error": str(e)}
+@app.get("/api/export")
+async def export_csv():
+    """Exporta todos los snapshots como CSV descargable"""
+    try:
+        rows = _get_snapshots()
+        output = io.StringIO()
+        writer = csv.writer(output)
+        writer.writerow(["id", "timestamp", "qualia", "satisfaccion", "frustracion", "aburrimiento", "autoConfianza", "fatiga", "pregunta", "respuesta", "novedad", "complejidad"])
+        for r in rows:
+            meta = {}
+            try:
+                meta = json.loads(r.get("metadata", "{}"))
+            except:
+                pass
+            writer.writerow([
+                r.get("id", ""), r.get("timestamp", ""),
+                r.get("qualia", 0), r.get("satisfaccion", 0),
+                r.get("frustracion", 0), r.get("aburrimiento", 0),
+                r.get("autoConfianza", 0), r.get("fatiga", 0),
+                meta.get("pregunta", ""), meta.get("respuesta", ""),
+                meta.get("novedad", ""), meta.get("complejidad", "")
+            ])
+        output.seek(0)
+        return StreamingResponse(
+            iter([output.getvalue()]),
+            media_type="text/csv",
+            headers={"Content-Disposition": f"attachment; filename=cortex_nexus_export_{datetime.datetime.utcnow().strftime('%Y%m%d_%H%M')}.csv"}
+        )
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
+@app.post("/api/sleep")
+async def sleep_consolidation():
+    """Ejecuta consolidación nocturna (Sleep Replay) del arquetipo"""
+    try:
+        if not vector_global or not vector_global.memoria:
+            return {"error": "Vector no inicializado"}
+        # Capturar arquetipo ANTES
+        antes = vector_global.memoria.cargar_meta_vector()
+        # Ejecutar consolidación EMA
+        vector_global.consolidar_memoria()
+        # Capturar arquetipo DESPUÉS
+        despues = vector_global.memoria.cargar_meta_vector()
+        drift = {
+            k: round(despues.get(k, 0) - antes.get(k, 0), 6)
+            for k in antes
+        }
+        return {
+            "status": "Consolidación completada",
+            "arquetipo_antes": antes,
+            "arquetipo_despues": despues,
+            "drift": drift,
+            "timestamp": datetime.datetime.utcnow().isoformat()
+        }
+    except Exception as e:
+        return {"error": str(e)}
+# ============================================================
+# ANALYTICS DASHBOARD
+# ============================================================
+@app.get("/analytics", response_class=HTMLResponse)
+async def analytics_dashboard():
+    html = """
+    <!DOCTYPE html>
+    <html lang="es">
+    <head>
+        <meta charset="UTF-8">
+        <title>Cortex-Nexus | Analytics Lab</title>
+        <meta name="viewport" content="width=device-width, initial-scale=1">
+        <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+        <style>
+            * { box-sizing: border-box; margin: 0; padding: 0; }
+            body { font-family: 'Inter', sans-serif; background: #0f172a; color: #f8fafc; padding: 16px; }
+            .container { max-width: 900px; margin: 0 auto; }
+            .card { background: #1e293b; padding: 16px; border-radius: 12px; margin-bottom: 16px; border: 1px solid #334155; }
+            h1 { color: #a78bfa; text-align: center; font-size: 1.5rem; margin-bottom: 16px; }
+            h2 { color: #38bdf8; font-size: 1rem; margin-bottom: 10px; }
+            .nav { display: flex; justify-content: center; gap: 12px; margin-bottom: 16px; }
+            .nav a { color: #94a3b8; text-decoration: none; padding: 6px 14px; border-radius: 8px; font-size: 0.8rem; background: #334155; }
+            .nav a.active, .nav a:hover { background: #a78bfa; color: #fff; }
+            .window-selector { display: flex; gap: 8px; margin-bottom: 12px; }
+            .window-btn { padding: 6px 12px; border: 1px solid #475569; background: #334155; color: #94a3b8; border-radius: 8px; cursor: pointer; font-size: 0.75rem; }
+            .window-btn.active { background: #a78bfa; color: #fff; border-color: #a78bfa; }
+            .metrics-grid { display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px; }
+            .metric { background: #334155; padding: 10px; border-radius: 8px; text-align: center; }
+            .metric-label { font-size: 0.65rem; color: #94a3b8; text-transform: uppercase; }
+            .metric-value { font-size: 1.1rem; font-weight: 800; color: #38bdf8; margin-top: 4px; }
+            .metric-trend { font-size: 0.6rem; margin-top: 2px; }
+            .trend-up { color: #10b981; }
+            .trend-down { color: #ef4444; }
+            .trend-flat { color: #94a3b8; }
+            .pattern { padding: 8px 12px; border-radius: 8px; margin-bottom: 8px; font-size: 0.75rem; }
+            .pattern-warning { background: rgba(245,158,11,0.15); border-left: 3px solid #f59e0b; color: #fbbf24; }
+            .pattern-danger { background: rgba(239,68,68,0.15); border-left: 3px solid #ef4444; color: #f87171; }
+            .pattern-success { background: rgba(16,185,129,0.15); border-left: 3px solid #10b981; color: #34d399; }
+            .arquetipo { display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px; }
+            .arq-item { text-align: center; }
+            .arq-label { font-size: 0.65rem; color: #94a3b8; }
+            .arq-value { font-size: 1.2rem; font-weight: 800; color: #a78bfa; }
+            .export-btn { display: block; width: 100%; padding: 12px; background: #a78bfa; color: #fff; border: none; border-radius: 8px; font-size: 0.9rem; cursor: pointer; font-weight: 600; }
+            .export-btn:hover { background: #8b5cf6; }
+            .sleep-btn { display: block; width: 100%; padding: 12px; background: #1e293b; color: #a78bfa; border: 2px solid #a78bfa; border-radius: 8px; font-size: 0.9rem; cursor: pointer; font-weight: 600; margin-top: 8px; }
+            .sleep-btn:hover { background: #a78bfa; color: #fff; }
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <div class="nav">
+                <a href="/">🧠 Live</a>
+                <a href="/analytics" class="active">🔬 Analytics</a>
+            </div>
+            <h1>🔬 Analytics Lab</h1>
+            <div class="window-selector">
+                <button class="window-btn active" onclick="setWindow('1h')">1H</button>
+                <button class="window-btn" onclick="setWindow('6h')">6H</button>
+                <button class="window-btn" onclick="setWindow('24h')">24H</button>
+                <button class="window-btn" onclick="setWindow('7d')">7D</button>
+            </div>
+            <div class="card">
+                <h2>📊 Métricas Derivadas <span id="window-label" style="color:#94a3b8; font-size:0.7rem;">(1h)</span></h2>
+                <div class="metrics-grid" id="metrics-grid">Cargando...</div>
+            </div>
+            <div class="card">
+                <h2>⚡ Volatilidad Emocional</h2>
+                <canvas id="volChart" height="180"></canvas>
+            </div>
+            <div class="card">
+                <h2>🚨 Patrones Detectados</h2>
+                <div id="patterns-box">Analizando...</div>
+            </div>
+            <div class="card">
+                <h2>🧬 Arquetipo (Personalidad Base)</h2>
+                <div class="arquetipo" id="arquetipo-box">Cargando...</div>
+            </div>
+            <div class="card">
+                <a href="/api/export" class="export-btn">📥 Exportar CSV Completo</a>
+                <button class="sleep-btn" onclick="triggerSleep()">😴 Ejecutar Consolidación (Sleep Replay)</button>
+                <div id="sleep-result" style="font-size:0.7rem; color:#94a3b8; margin-top:8px;"></div>
+            </div>
+            <p style="text-align:center; font-size:0.65rem; color:#475569; margin-top:12px;">Arquitectura Emocional v3.0 • Analytics Lab • Maxi Speranza</p>
+        </div>
+        <script>
+            let currentWindow = '1h';
+            const volCtx = document.getElementById('volChart').getContext('2d');
+            const volChart = new Chart(volCtx, {
+                type: 'bar',
+                data: {
+                    labels: ['Qualia', 'Frustración', 'Confianza', 'Fatiga', 'Aburrimiento'],
+                    datasets: [{
+                        label: 'Volatilidad (σ)',
+                        data: [0, 0, 0, 0, 0],
+                        backgroundColor: ['rgba(56,189,248,0.6)', 'rgba(239,68,68,0.6)', 'rgba(16,185,129,0.6)', 'rgba(245,158,11,0.6)', 'rgba(167,139,250,0.6)'],
+                        borderRadius: 6
+                    }]
+                },
+                options: {
+                    responsive: true,
+                    scales: { y: { beginAtZero: true, grid: { color: '#334155' }, ticks: { color: '#94a3b8' } }, x: { ticks: { color: '#94a3b8' } } },
+                    plugins: { legend: { display: false } }
+                }
+            });
+            function setWindow(w) {
+                currentWindow = w;
+                document.querySelectorAll('.window-btn').forEach(b => b.classList.remove('active'));
+                event.target.classList.add('active');
+                loadAnalytics();
+            }
+            function trendIcon(trend) {
+                if (trend > 0.001) return '<span class="trend-up">▲ +' + trend.toFixed(4) + '</span>';
+                if (trend < -0.001) return '<span class="trend-down">▼ ' + trend.toFixed(4) + '</span>';
+                return '<span class="trend-flat">— estable</span>';
+            }
+            async function loadAnalytics() {
+                try {
+                    const res = await fetch('/api/analytics');
+                    const data = await res.json();
+                    if (data.error) { console.error(data.error); return; }
+                    const w = data.windows[currentWindow];
+                    document.getElementById('window-label').innerText = `(${currentWindow} • ${w.count} épocas)`;
+                    const grid = document.getElementById('metrics-grid');
+                    if (w.count === 0) { grid.innerHTML = '<p style="color:#94a3b8;">Sin datos para esta ventana</p>'; return; }
+                    const m = w.metrics;
+                    const names = {qualia: 'Qualia', frustracion: 'Frustración', autoConfianza: 'Confianza', fatiga: 'Fatiga', aburrimiento: 'Aburrimiento', ratio_qualia_fatiga: 'Q/Fatiga', entropia_emocional: 'Entropía'};
+                    grid.innerHTML = Object.entries(names).map(([k, label]) => {
+                        const s = m[k] || {};
+                        return `<div class="metric"><div class="metric-label">${label}</div><div class="metric-value">${(s.mean||0).toFixed(3)}</div><div class="metric-trend">${trendIcon(s.trend||0)}</div><div style="font-size:0.55rem;color:#64748b;">σ=${(s.std||0).toFixed(3)} [${(s.min||0).toFixed(2)}-${(s.max||0).toFixed(2)}]</div></div>`;
+                    }).join('');
+                    // Volatilidad
+                    volChart.data.datasets[0].data = ['qualia','frustracion','autoConfianza','fatiga','aburrimiento'].map(k => (m[k]||{}).std || 0);
+                    volChart.update('none');
+                    // Patrones
+                    const pBox = document.getElementById('patterns-box');
+                    if (data.patterns.length === 0) {
+                        pBox.innerHTML = '<p style="color:#10b981; font-size:0.8rem;">✅ Sin anomalías detectadas</p>';
+                    } else {
+                        pBox.innerHTML = data.patterns.map(p => `<div class="pattern pattern-${p.severity}">${p.msg}</div>`).join('');
+                    }
+                    // Arquetipo
+                    const aBox = document.getElementById('arquetipo-box');
+                    const a = data.arquetipo;
+                    aBox.innerHTML = `
+                        <div class="arq-item"><div class="arq-label">Meta-Qualia</div><div class="arq-value">${(a.meta_qualia||0).toFixed(4)}</div></div>
+                        <div class="arq-item"><div class="arq-label">Meta-Frustración</div><div class="arq-value">${(a.meta_frustracion||0).toFixed(4)}</div></div>
+                        <div class="arq-item"><div class="arq-label">Meta-Confianza</div><div class="arq-value">${(a.meta_confianza||0).toFixed(4)}</div></div>
+                    `;
+                } catch(e) { console.error("Error analytics:", e); }
+            }
+            async function triggerSleep() {
+                const res = await fetch('/api/sleep', {method: 'POST'});
+                const data = await res.json();
+                const box = document.getElementById('sleep-result');
+                if (data.error) { box.innerHTML = '❌ ' + data.error; return; }
+                box.innerHTML = `✅ Consolidación OK | Drift: Q=${data.drift.meta_qualia}, F=${data.drift.meta_frustracion}, C=${data.drift.meta_confianza}`;
+                loadAnalytics();
+            }
+            setInterval(loadAnalytics, 8000);
+            loadAnalytics();
+        </script>
+    </body>
+    </html>
+    """
+    return HTMLResponse(content=html)