Upload app.py

app.py

@@ -9,12 +9,25 @@ import os
 import requests
 from pathlib import Path
 
-# Define the data schema for Window 7 Conductor requests
+# Define the data schema for Window 7 Conductor requests (Virus Analysis)
 class EconRequest(BaseModel):
     virus_name: str
     econ_buffer: float  # Value from 0.1 to 1.0 (IMF-WEO)
     population_exposure: float # Value from 100 to 100,000 (WorldPop)
 
+# Define the data schema for Window 4 Conductor Economic Analysis
+class ConductorEconRequest(BaseModel):
+    conductor_prompt: str
+    year: int
+    stress_factors: list = []
+    singularity_detected: bool = False
+    timestamp: int
+    market_shock_index: float = 0.0
+    impact_classification: str = "Unknown"
+    regional_vulnerabilities: dict = {}
+    contagion_metrics: dict = {}
+    tech_analysis_id: str = None
+
 # Global container for the model
 aegis_brain = {}
 
@@ -105,6 +118,7 @@ app = FastAPI(lifespan=lifespan, title="Aegis Econ API")
 
 @app.post("/predict")
 async def get_stability_score(data: EconRequest):
+    """Original virus-based economic stability prediction"""
     model = aegis_brain.get("model")
     df_rankings = aegis_brain.get("rankings")
     model_loaded = aegis_brain.get("model_loaded", False)
@@ -167,6 +181,148 @@ async def get_stability_score(data: EconRequest):
         "population_risk_factor": round(min(data.population_exposure / 100000, 1.0), 3)
     }
 
+@app.post("/conductor-predict")
+async def get_conductor_economic_analysis(data: ConductorEconRequest):
+    """New conductor-based economic analysis for Window 4 → War → Disease pipeline"""
+    model = aegis_brain.get("model")
+    df_rankings = aegis_brain.get("rankings")
+    model_loaded = aegis_brain.get("model_loaded", False)
+    
+    # Extract economic factors from conductor data
+    base_risk_score = data.market_shock_index if data.market_shock_index > 0 else 0.5
+    
+    # Calculate stress factor impact
+    stress_impact = 0.0
+    if data.stress_factors:
+        stress_impact = sum(factor.get('impact_level', 0.5) for factor in data.stress_factors) / len(data.stress_factors)
+    
+    # Regional vulnerability assessment
+    regional_risk = 0.5
+    if data.regional_vulnerabilities:
+        regional_risk = sum(data.regional_vulnerabilities.values()) / len(data.regional_vulnerabilities)
+    
+    # Contagion metrics assessment
+    contagion_risk = 0.5
+    if data.contagion_metrics:
+        spread_rate = data.contagion_metrics.get('spread_rate', 0.5)
+        containment_difficulty = data.contagion_metrics.get('containment_difficulty', 0.5)
+        contagion_risk = (spread_rate + containment_difficulty) / 2
+    
+    # Singularity detection impact
+    singularity_multiplier = 1.5 if data.singularity_detected else 1.0
+    
+    # Calculate comprehensive economic stability
+    if model_loaded and model is not None:
+        # Use actual TabNet model with conductor data
+        input_vector = np.array([[base_risk_score, stress_impact, regional_risk, contagion_risk]])
+        prediction = model.predict(input_vector)
+        stability_score = float(prediction[0][0]) * singularity_multiplier
+        model_status = "trained_model_conductor"
+    else:
+        # Enhanced mathematical simulation for conductor data
+        economic_stability = (
+            (1 - base_risk_score) * 0.30 +           # Market shock impact
+            (1 - stress_impact) * 0.25 +             # Stress factors
+            (1 - regional_risk) * 0.25 +             # Regional vulnerabilities
+            (1 - contagion_risk) * 0.20              # Contagion metrics
+        ) * singularity_multiplier
+        
+        # Year-based adjustment (future years have more uncertainty)
+        year_factor = max(0.8, 1.0 - (data.year - 2024) * 0.02)
+        stability_score = economic_stability * year_factor
+        
+        # Add realistic variance
+        variance = 0.05 + (stress_impact * 0.03)
+        stability_score += np.random.normal(0, variance)
+        stability_score = max(0.0, min(1.0, stability_score))
+        model_status = "enhanced_simulation_conductor"
+    
+    # Calculate market cap estimate (in billions)
+    market_cap_estimate = max(50.0, 500.0 * stability_score * (2.0 - base_risk_score))
+    
+    # Generate key metrics
+    key_metrics = [
+        {
+            "name": "Market Shock Index",
+            "value": base_risk_score,
+            "change": -15.2 if data.singularity_detected else -5.1,
+            "unit": "index",
+            "risk_level": "CRITICAL" if base_risk_score > 0.8 else "HIGH" if base_risk_score > 0.6 else "MEDIUM",
+            "description": "Overall market disruption indicator"
+        },
+        {
+            "name": "Regional Stability",
+            "value": 1 - regional_risk,
+            "change": -8.3 if regional_risk > 0.7 else 2.1,
+            "unit": "stability",
+            "risk_level": "HIGH" if regional_risk > 0.7 else "MEDIUM" if regional_risk > 0.5 else "LOW",
+            "description": "Cross-regional economic stability"
+        },
+        {
+            "name": "Contagion Risk",
+            "value": contagion_risk,
+            "change": 12.5 if contagion_risk > 0.6 else -3.2,
+            "unit": "risk",
+            "risk_level": "CRITICAL" if contagion_risk > 0.8 else "HIGH" if contagion_risk > 0.6 else "MEDIUM",
+            "description": "Economic contagion spread potential"
+        }
+    ]
+    
+    # Determine alert level
+    if stability_score < 0.25:
+        alert_level = "CRITICAL"
+        status = f"CRITICAL ECONOMIC INSTABILITY DETECTED ({data.year})"
+    elif stability_score < 0.45:
+        alert_level = "HIGH_RISK"
+        status = f"HIGH ECONOMIC RISK IDENTIFIED ({data.year})"
+    elif stability_score < 0.65:
+        alert_level = "MONITOR"
+        status = f"ECONOMIC MONITORING REQUIRED ({data.year})"
+    else:
+        alert_level = "STABLE"
+        status = f"ECONOMIC STABILITY MAINTAINED ({data.year})"
+    
+    if data.singularity_detected:
+        status = f"SINGULARITY EVENT - {status}"
+    
+    # Generate BRICS+ impact analysis
+    brics_impact = {
+        "inflation_pressure": min(15.0, base_risk_score * 20.0),
+        "market_disruption": f"{'Severe' if base_risk_score > 0.7 else 'Moderate' if base_risk_score > 0.4 else 'Limited'} disruption expected across BRICS+ economies",
+        "competitive_dynamics": f"{'Accelerated' if data.singularity_detected else 'Standard'} competitive realignment in emerging markets"
+    }
+    
+    # Generate strategic recommendations
+    recommendations = [
+        f"Implement {'emergency' if alert_level == 'CRITICAL' else 'enhanced'} economic monitoring protocols",
+        f"Activate {'Tier 1' if stability_score < 0.4 else 'Tier 2'} risk mitigation strategies",
+        f"Coordinate with {'all' if data.singularity_detected else 'key'} international economic partners"
+    ]
+    
+    if data.stress_factors:
+        recommendations.append(f"Address {len(data.stress_factors)} identified stress factors immediately")
+    
+    return {
+        "status": status,
+        "market_analysis": f"Comprehensive economic analysis for {data.year} indicates {alert_level.lower()} conditions. Conductor analysis reveals {data.impact_classification.lower()} implications with {len(data.stress_factors)} stress factors identified. {'Singularity event detected - ' if data.singularity_detected else ''}Economic stability score: {stability_score:.3f}",
+        "key_metrics": key_metrics,
+        "market_cap_estimate": round(market_cap_estimate, 1),
+        "operational_risk": f"{'Extreme' if alert_level == 'CRITICAL' else 'High' if alert_level == 'HIGH_RISK' else 'Moderate'} operational risk across {len(data.regional_vulnerabilities) if data.regional_vulnerabilities else 'multiple'} regions",
+        "sovereign_advantage": f"{'Minimal' if stability_score < 0.3 else 'Limited' if stability_score < 0.6 else 'Moderate'} sovereign economic advantage maintained",
+        "recommendations": recommendations,
+        "brics_impact": brics_impact,
+        "processing_time": 1200 + int(len(data.stress_factors) * 150),
+        "confidence_score": max(0.6, min(0.95, stability_score + 0.1)),
+        "conductor_metadata": {
+            "tech_analysis_id": data.tech_analysis_id,
+            "year": data.year,
+            "singularity_detected": data.singularity_detected,
+            "stress_factors_count": len(data.stress_factors),
+            "regional_coverage": len(data.regional_vulnerabilities) if data.regional_vulnerabilities else 0,
+            "model_status": model_status
+        }
+    }
+
 @app.get("/health")
 async def health():
     return {
@@ -182,17 +338,21 @@ async def health():
 async def root():
     return {
         "message": "AEGIS Economic Stability Analysis API",
-        "version": "1.2.0",
+        "version": "2.0.0",
         "model_source": "https://huggingface.co/gsstec/aegis_window2_econ_v1",
         "features": [
             "Economic stability prediction",
             "Multi-virus risk assessment", 
             "Population exposure modeling",
-            "Real-time alert classification"
+            "Real-time alert classification",
+            "Conductor economic analysis",
+            "War-Disease pipeline integration"
         ],
         "endpoints": {
-            "predict": "POST /predict - Get stability prediction",
+            "predict": "POST /predict - Get virus-based stability prediction",
+            "conductor-predict": "POST /conductor-predict - Get conductor-based economic analysis",
             "health": "GET /health - System status",
             "root": "GET / - API information"
-        }
+        },
+        "data_flow": "Conductor → Economic → War → Disease Analysis Pipeline"
     }