Deploy full stack Lumina Self-RAG system to Hugging Face Spaces

.dockerignore

@@ -0,0 +1,10 @@
+.git
+.gitignore
+.env
+venv
+__pycache__
+university-advisor-ui/node_modules
+university-advisor-ui/dist
+*.pyc
+.ipynb_checkpoints
+scratch

.gitignore

@@ -3,8 +3,8 @@ venv/
 __pycache__/
 *.pyc
 .env
-vector_store_fallback.pkl
-chroma_db/
+# vector_store_fallback.pkl
+# chroma_db/
 
 # Frontend
 university-advisor-ui/node_modules/

Dockerfile

@@ -0,0 +1,40 @@
+# Stage 1: Build the frontend
+FROM node:20-slim AS build-stage
+WORKDIR /ui-build
+COPY university-advisor-ui/package*.json ./
+RUN npm install
+COPY university-advisor-ui/ ./
+RUN npm run build
+
+# Stage 2: Python environment
+FROM python:3.11-slim
+
+# Install system dependencies for sentence-transformers/chromadb if needed
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+WORKDIR /app
+
+# Install dependencies
+COPY --chown=user requirements.txt .
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy backend code and data
+COPY --chown=user . .
+
+# Copy built frontend from build-stage to 'static' folder
+COPY --chown=user --from=build-stage /ui-build/dist /app/static
+
+# Ensure data directories exist and have correct permissions
+# (COPY --chown handles this mostly, but good to be explicit if they are created at runtime)
+# RUN mkdir -p chroma_db data
+
+# Expose port 7860
+EXPOSE 7860
+
+# Run the app
+CMD ["python", "self_rag_agent.py"]

README.md

@@ -0,0 +1,35 @@
+---
+title: Lumina Intelligent University Advisor
+emoji: 🎓
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+app_port: 7860
+pinned: false
+---
+
+# Lumina: The Intelligent University Advisor
+A production-grade Self-RAG advisory system built with LangGraph, Groq, and React.
+
+## Features
+- **Self-RAG Architecture**: Real-time reflection on retrieval relevance and hallucination detection.
+- **High Performance**: Powered by Groq Llama 3.3 70B for near-instant reasoning.
+- **Glassmorphic UI**: Transparent pipeline visualization showing the agent's internal reasoning steps.
+- **Robust Knowledge Base**: Grounded in official university catalogs and policies.
+
+## Environment Variables
+To run this Space, you need to set the following secrets in your Space settings:
+- `GROQ_API_KEY`: Your Groq API key.
+- `TAVILY_API_KEY`: Your Tavily API key for web search fallback.
+- `GOOGLE_API_KEY`: (Optional) If using Google Generative AI components.
+
+## Local Development
+```bash
+# Install dependencies
+pip install -r requirements.txt
+cd university-advisor-ui && npm install && npm run build
+cd ..
+
+# Run the backend (it will serve the built UI from /static)
+python self_rag_agent.py
+```

chroma_db/997e39d6-02a7-4a01-ae7d-452ccb400534/data_level0.bin

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+version https://git-lfs.github.com/spec/v1
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+size 1676000

chroma_db/997e39d6-02a7-4a01-ae7d-452ccb400534/header.bin

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+size 100

chroma_db/997e39d6-02a7-4a01-ae7d-452ccb400534/length.bin

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+size 4000

chroma_db/chroma.sqlite3

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+oid sha256:998b476044f0719eb63fa274c4bf8d84cd36c5f029369ea5408d17abc7c3aa49
+size 434176

requirements.txt

@@ -12,3 +12,4 @@ sentence-transformers==3.0.1
 langchain-huggingface==0.0.3
 fastapi==0.111.0
 uvicorn==0.30.1
+aiofiles==24.1.0

self_rag_agent.py

@@ -1,5 +1,7 @@
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
 from pydantic import BaseModel
 from typing import List, Optional
 import uvicorn
@@ -107,5 +109,15 @@ async def pipeline_status():
 async def run_pipeline():
     return {"status": "skipped", "message": "Pipeline logic not applicable to RAG Agent"}
 
+# Serve Frontend - This MUST be at the end
+# We assume the built frontend is in a directory named 'static'
+if os.path.exists("static"):
+    app_api.mount("/", StaticFiles(directory="static", html=True), name="static")
+
+    @app_api.get("/{full_path:path}")
+    async def serve_frontend(full_path: str):
+        # Fallback for SPA routing
+        return FileResponse("static/index.html")
+
 if __name__ == "__main__":
-    uvicorn.run("self_rag_agent:app_api", host="0.0.0.0", port=8000, reload=True)
+    uvicorn.run("self_rag_agent:app_api", host="0.0.0.0", port=7860, reload=False)

university-advisor-ui/src/services/api.ts

@@ -1,6 +1,6 @@
 import { WindowMemoryItem } from "../types"
 
-const API_BASE = "http://localhost:8000"
+const API_BASE = ""
 const MOCK_MODE = false // Set to false when backend is running
 
 export interface ChatRequest {

vector_store_fallback.pkl

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+size 82382