from pathlib import Path
# -----------------------------
# Dockerfile
# -----------------------------
Path("Dockerfile").write_text("""FROM python:3.11-slim
RUN useradd -m -u 1000 user
ENV HOME=/home/user
ENV PATH=/home/user/.local/bin:$PATH
ENV PYTHONUNBUFFERED=1
ENV PYTHONDONTWRITEBYTECODE=1
WORKDIR $HOME/app
RUN apt-get update && apt-get install -y --no-install-recommends build-essential curl git && rm -rf /var/lib/apt/lists/*
COPY --chown=user requirements.txt $HOME/app/requirements.txt
RUN pip install --no-cache-dir --upgrade pip
RUN pip install --no-cache-dir -r requirements.txt
COPY --chown=user . $HOME/app
USER user
ENV PORT=7860
ENV LLM_PROVIDER=huggingface
ENV ENABLE_LOCAL_LLM=false
ENV HF_INFERENCE_MODEL=google/flan-t5-base
ENV HF_TIMEOUT_SECONDS=60
ENV UPLOAD_DIR=data/uploads
ENV PROCESSED_DIR=data/processed
ENV QDRANT_LOCAL_PATH=data/qdrant
ENV EVALUATION_DIR=data/evaluation
EXPOSE 7860
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
""", encoding="utf-8")
# -----------------------------
# .dockerignore
# -----------------------------
Path(".dockerignore").write_text(""".git
.gitignore
venv
.venv
env
__pycache__
*.pyc
*.pyo
*.pyd
.env
.env.*
*.log
data/uploads
data/processed
data/qdrant
data/evaluation
outputs
reports
notebooks
*.pt
*.pth
*.bin
*.safetensors
*.onnx
.DS_Store
Thumbs.db
""", encoding="utf-8")
# -----------------------------
# README.md with HF metadata
# -----------------------------
Path("README.md").write_text("""---
title: GraphRAG Research Scientist
emoji: 🧠
colorFrom: indigo
colorTo: blue
sdk: docker
app_port: 7860
pinned: false
---
# GraphRAG Research Scientist
A FastAPI-based GraphRAG research assistant for document-grounded question answering.
## Main endpoints
- `/` health check
- `/demo` simple browser demo
- `/docs` Swagger API docs
- `/deployment/health` deployment health
- `/deployment/config` deployment config
- `/upload` upload document
- `/documents/{document_id}/index` index document
- `/ask` ask question
## Hugging Face Variables
LLM_PROVIDER=huggingface
ENABLE_LOCAL_LLM=false
HF_INFERENCE_MODEL=google/flan-t5-base
HF_TIMEOUT_SECONDS=60
## Hugging Face Secret
HF_API_TOKEN should be added in Space Settings as a secret.
""", encoding="utf-8")
# -----------------------------
# app/deployment/hf_status.py
# -----------------------------
Path("app/deployment/hf_status.py").write_text("""import os
from typing import Dict, Any
from app.core.config import settings
def get_deployment_health() -> Dict[str, Any]:
return {
"status": "healthy",
"service": settings.APP_NAME,
"version": settings.APP_VERSION,
"environment": settings.ENVIRONMENT,
"deployment_target": "hugging_face_spaces",
"port": os.getenv("PORT", "7860"),
"message": "FastAPI app is running and ready for Hugging Face Spaces."
}
def get_deployment_config() -> Dict[str, Any]:
return {
"deployment_target": "hugging_face_spaces",
"llm_provider": settings.LLM_PROVIDER,
"local_llm_enabled": settings.ENABLE_LOCAL_LLM,
"hf_model": settings.HF_INFERENCE_MODEL,
"hf_token_present": bool(settings.HF_API_TOKEN),
"upload_dir": str(settings.UPLOAD_DIR),
"processed_dir": str(settings.PROCESSED_DIR),
"qdrant_path": str(settings.QDRANT_LOCAL_PATH),
"evaluation_dir": str(settings.EVALUATION_DIR),
"reranker_enabled": settings.ENABLE_RERANKER,
"storage_warning": "Local Space storage can reset after restart unless persistent storage is attached."
}
def get_demo_html() -> str:
return \"\"\"
GraphRAG Research Scientist
🧠 GraphRAG Research Scientist
FastAPI backend is running.
Useful links
\"\"\"
""", encoding="utf-8")
# -----------------------------
# Patch main.py
# -----------------------------
main_path = Path("app/main.py")
text = main_path.read_text(encoding="utf-8")
if "from fastapi.responses import HTMLResponse" not in text:
text = text.replace(
"from fastapi.staticfiles import StaticFiles",
"from fastapi.staticfiles import StaticFiles\nfrom fastapi.responses import HTMLResponse"
)
if "from app.deployment.hf_status import" not in text:
insert_after = "from app.generation.llm_service import get_llm_status, get_loaded_llm_info\n"
deployment_import = (
"from app.deployment.hf_status import (\n"
" get_deployment_health,\n"
" get_deployment_config,\n"
" get_demo_html\n"
")\n"
)
if insert_after in text:
text = text.replace(insert_after, insert_after + deployment_import)
else:
text = deployment_import + text
for old in [
"Phase 10 - LLM Provider Abstraction",
"Phase 9 - Answer Evaluation System",
"Phase 8 - Retrieval Evaluation System",
"Phase 7 - Better Local LLM Strategy",
"Phase 6.1 - Clean Answer Refinement",
"Phase 6 - Answer Quality Improvement Layer"
]:
text = text.replace(old, "Phase 11 - Hugging Face Deployment Readiness")
if "# Hugging Face deployment endpoints" not in text:
text += '''
# Hugging Face deployment endpoints
@app.get("/deployment/health")
def deployment_health():
return get_deployment_health()
@app.get("/deployment/config")
def deployment_config():
return get_deployment_config()
@app.get("/demo", response_class=HTMLResponse)
def demo_page():
return get_demo_html()
'''
main_path.write_text(text, encoding="utf-8")
# -----------------------------
# requirements.txt safety
# -----------------------------
req_path = Path("requirements.txt")
req_text = req_path.read_text(encoding="utf-8")
if "requests" not in req_text:
req_text += "\\nrequests\\n"
req_path.write_text(req_text, encoding="utf-8")
print("HF deployment repair files created successfully.")