feat: Implement auto-seeding on backend startup and absolute DB path

Dockerfile

@@ -12,6 +12,8 @@ RUN npm run build
 # Final Image
 FROM python:3.12-slim
 WORKDIR /app
+ENV DATABASE_URL="sqlite:////app/backend/andesops.db"
+ENV PYTHONUNBUFFERED=1
 
 # Install Node.js (for running frontend in dev/ssr mode) and Nginx
 RUN apt-get update && apt-get install -y \

backend/app/main.py

@@ -30,6 +30,86 @@ app.include_router(analysis.router, prefix="/api", tags=["Analysis"])
 app.include_router(company.router, prefix="/api", tags=["Company"])
 app.include_router(documents.router, prefix="/api", tags=["Documents"])
 
+@app.on_event("startup")
+async def startup_event():
+    from app.database import SessionLocal
+    from app.models.tender import TenderModel
+    from app.models.analysis import AnalysisHistoryModel
+    from app.models.company import CompanyProfileModel
+    from datetime import datetime, timedelta
+    import json
+
+    db = SessionLocal()
+    try:
+        count = db.query(TenderModel).count()
+        if count == 0:
+            print("Database empty. Seeding data...")
+            # 1. Company Profile
+            profile = CompanyProfileModel(
+                name="Andes Digital Solutions",
+                industry="Software Engineering & AI",
+                services="Machine Learning, Custom ERP, Cloud Infrastructure",
+                experience="10+ years delivering enterprise software for the public sector.",
+                certifications="AWS Partner, ISO 9001, SCRUM Master Team",
+                regions="Metropolitana, Valparaíso, Biobío, Araucanía",
+                documents_available="RUT, Financial Statements 2023, Technical Portfolio, Staff Certifications"
+            )
+            db.add(profile)
+
+            # 2. Software Tenders
+            tenders_data = [
+                TenderModel(
+                    code="2394-15-LR24",
+                    name="Implementación Sistema ERP para Red de Salud Oriente",
+                    description="Suministro, instalación y soporte de sistema de gestión de recursos empresariales para red hospitalaria.",
+                    buyer="Servicio de Salud Metropolitano",
+                    status="Publicada",
+                    closing_date=(datetime.now() + timedelta(days=20)).strftime("%Y-%m-%d"),
+                    estimated_amount=450000000,
+                    region="Metropolitana",
+                    sector="Tecnología de la Información",
+                    source="Mercado Público"
+                ),
+                TenderModel(
+                    code="5021-10-LP24",
+                    name="Plataforma de IA para Análisis de Datos Criminalísticos",
+                    description="Desarrollo de algoritmos de visión computacional y análisis predictivo para seguridad ciudadana.",
+                    buyer="Subsecretaría de Prevención del Delito",
+                    status="Publicada",
+                    closing_date=(datetime.now() + timedelta(days=12)).strftime("%Y-%m-%d"),
+                    estimated_amount=180000000,
+                    region="Metropolitana",
+                    sector="Software & IA",
+                    source="Mercado Público"
+                )
+            ]
+            for t in tenders_data:
+                db.add(t)
+            
+            # 3. Initial Analysis
+            history = AnalysisHistoryModel(
+                tender_code="5021-10-LP24",
+                tender_name="Plataforma de IA para Análisis de Datos Criminalísticos",
+                decision="Recommended",
+                score=92,
+                summary="Oportunidad estratégica de alto valor. Tenemos el stack tecnológico (Gemini, Python) y la experiencia previa en seguridad ciudadana.",
+                risks=json.dumps([
+                    {"severity": "High", "description": "Requisito de disponibilidad 99.9% 24/7"},
+                    {"severity": "Medium", "description": "Integración con bases de datos legacy de Carabineros"}
+                ]),
+                technical_analysis="Factibilidad técnica excelente.",
+                legal_analysis="Cumplimos con las bases.",
+                commercial_analysis="ROI del 35%.",
+                proposal_draft="Propuesta técnica...",
+                report_markdown="# Reporte",
+                created_at=datetime.now()
+            )
+            db.add(history)
+            db.commit()
+            print("Database seeded successfully!")
+    finally:
+        db.close()
+
 @app.get("/")
 def read_root():
     return {"message": "Welcome to AndesOps AI API"}