Deploy AEGIS Economics Stability Analysis App

Dockerfile

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+# Use a lightweight Python base instead of heavy CUDA
+FROM python:3.12-slim
+
+# Set environment variables for stability
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+
+# Force install the CPU version of Torch to avoid CUDA bloat
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Copy application files and your trained model
+COPY econ.py .
+COPY aegis_window2_econ_v1.zip .
+
+# Hugging Face Spaces port
+EXPOSE 7860
+
+# Launch using Uvicorn (FastAPI)
+CMD ["uvicorn", "econ:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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----
-title: Econ
-emoji: 🏢
-colorFrom: purple
-colorTo: yellow
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: AEGIS Economic Stability Analysis
+emoji: 📊
+colorFrom: blue
+colorTo: green
+sdk: docker
+pinned: false
+license: mit
+---
+
+# AEGIS Economic Stability Analysis
+
+This application provides economic stability predictions based on viral outbreak scenarios using advanced TabNet regression models.
+
+## Features
+
+- **Virus Risk Assessment**: Analyzes economic impact based on virus characteristics
+- **Population Exposure Modeling**: Considers population density and exposure patterns
+- **Economic Buffer Analysis**: Incorporates IMF-WEO economic resilience indicators
+- **Real-time Predictions**: FastAPI-powered REST API for stability scoring
+
+## API Endpoints
+
+### POST /predict
+Predict economic stability score for a given scenario.
+
+**Request Body:**
+```json
+{
+    "virus_name": "COVID-19",
+    "econ_buffer": 0.7,
+    "population_exposure": 50000
+}
+```
+
+**Response:**
+```json
+{
+    "virus": "COVID-19",
+    "stability_score": 0.6234,
+    "alert_level": "MONITOR"
+}
+```
+
+### GET /health
+Check system status and hardware configuration.
+
+## Usage
+
+The application runs on port 7860 and provides a REST API for economic stability analysis.
+
+Built with FastAPI, PyTorch, and TabNet for robust economic modeling.

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import pandas as pd
+import numpy as np
+import torch
+from pytorch_tabnet.tab_model import TabNetRegressor
+from contextlib import asynccontextmanager
+import os
+
+# Define the data schema for Window 7 Conductor requests
+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)
+
+# Global container for the model
+aegis_brain = {}
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Load model on startup
+    model_path = "aegis_window2_econ_v1.zip"
+    regressor = TabNetRegressor()
+    if os.path.exists(model_path):
+        regressor.load_model(model_path)
+        aegis_brain["model"] = regressor
+        # Load risk data for cross-referencing
+        aegis_brain["rankings"] = pd.read_csv("https://huggingface.co/gsstec/aegis_window2_econ_v1/resolve/main/Rankings.csv")
+        print("✅ Window 2 Brain & Rankings Loaded.")
+    yield
+    aegis_brain.clear()
+
+app = FastAPI(lifespan=lifespan, title="Aegis Econ API")
+
+@app.post("/predict")
+async def get_stability_score(data: EconRequest):
+    model = aegis_brain.get("model")
+    df_rankings = aegis_brain.get("rankings")
+    
+    if not model or df_rankings is None:
+        raise HTTPException(status_code=500, detail="Model not initialized.")
+
+    # 1. Fetch risk score from Rankings.csv
+    virus_row = df_rankings[df_rankings['Virus Name'] == data.virus_name]
+    if virus_row.empty:
+        raise HTTPException(status_code=404, detail="Virus not found in Risk Drive.")
+    
+    base_score = virus_row.iloc[0]['Original Score']
+    risk_impact = base_score * 0.7
+    
+    # 2. Vectorize for TabNet
+    input_vector = np.array([[base_score, risk_impact, data.econ_buffer, data.population_exposure]])
+    
+    # 3. Predict Continental Stability
+    prediction = model.predict(input_vector)
+    stability_score = float(prediction[0][0])
+    
+    return {
+        "virus": data.virus_name,
+        "stability_score": round(stability_score, 4),
+        "alert_level": "CRITICAL" if stability_score < 0.1 else "MONITOR"
+    }
+
+@app.get("/health")
+async def health():
+    return {"status": "operational", "hardware": "T4 GPU Active" if torch.cuda.is_available() else "CPU Mode"}

requirements.txt

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+# AEGIS Economic Stability Analysis - FastAPI Requirements
+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+pydantic==2.5.0
+pandas==2.1.4
+numpy==1.24.4
+torch==2.1.1
+pytorch-tabnet==4.1.0
+requests==2.31.0
+python-multipart==0.0.6