CSV-AI 🧠 v2

Modernized AI-powered CSV analysis — chat with, summarize, and analyze your CSV files using OpenAI, Anthropic, or a local Ollama model. Built for Streamlit Cloud, local laptops, and future API split.

This is the v2 rewrite of Safiullah-Rahu/CSV-AI. The product idea is unchanged; the architecture is modular, the AI stack is provider-agnostic, and the UI is a clean modern dashboard.

Features

• 💬 Chat — schema- and sample-aware Q&A with token-streaming.
• 📝 Summarize — single-call structured overview (replaces the old map-reduce flow).
• 📊 Analyze — deterministic pandas stats + LLM analyst narrative side-by-side, with charts, missingness, and correlations.
• 🔌 Multi-provider — OpenAI, Anthropic Claude, or local Ollama.
• 🎛️ Modern UI — sidebar nav, st.chat_message, light/dark friendly, custom CSS polish.
• 🧱 Modular — clean app/ package; no Streamlit imports in services, so a FastAPI layer can be added later.

Quick start

git clone https://github.com/Safiullah-Rahu/CSV-AI.git
cd CSV-AI

python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt

cp .env.example .env       # then add your API keys
streamlit run streamlit_app.py

Open http://localhost:8501 and upload a CSV.

Configuration

CSV-AI loads settings from (in order): environment variables → .env file → Streamlit secrets. See .env.example and .streamlit/secrets.toml.example.

Variable	Default	Purpose
OPENAI_API_KEY	—	Required if using OpenAI.
ANTHROPIC_API_KEY	—	Required if using Anthropic.
OLLAMA_BASE_URL	http://localhost:11434	Local Ollama endpoint.
DEFAULT_PROVIDER	openai	One of openai, anthropic, ollama.
DEFAULT_MODEL	gpt-4o-mini	Used until the user picks one in the sidebar.
DEFAULT_TEMPERATURE	0.2	0.0–1.5.
DEFAULT_MAX_TOKENS	1024	Response length cap.

Project structure

app/
├── config/        # pydantic-settings (env + secrets)
├── llm/           # provider-agnostic LLM interface + OpenAI / Anthropic / Ollama
├── data/          # CSV loader, profiler, sampler, prompt-context builder
├── prompts/       # versioned system prompts
├── services/      # ChatService, SummaryService, AnalysisService (UI-free)
├── ui/            # Streamlit pages + components + theme + session state
└── utils/         # logging, errors, token counting

tests/             # pytest suite (loader, profiler, factory)
streamlit_app.py   # Streamlit Cloud entry point

See ARCHITECTURE.md for the rationale behind each layer.

Deployment

• Streamlit Community Cloud — point it at streamlit_app.py, add keys to Secrets.
• Local — streamlit run streamlit_app.py.
• Docker — docker compose up --build (uses .env).
• Future API split — services are pure-Python; a FastAPI layer is a small adapter.

Full instructions in DEPLOYMENT.md.

Development

pip install -r requirements-dev.txt
pytest                     # run tests
ruff check .               # lint
black .                    # format

What changed vs. v1

	v1	v2
Architecture	278-line app.py	modular app/ package
LLM	OpenAI only, via LangChain	OpenAI · Anthropic · Ollama via thin native SDKs
Imports	langchain.chat_models, langchain.embeddings (deprecated)	current SDKs
Chat context	FAISS retrieval over CSV chunks	schema + smart sample (cheaper, more accurate)
Summarize	LangChain load_summarize_chain(map_reduce)	single structured prompt
Analyze	create_pandas_dataframe_agent only	deterministic pandas stats + LLM narrative
UI	one selectbox of "functionality"	sidebar nav + tabbed stats + theme polish
Config	os.environ inline	pydantic-settings
Tests	none	pytest suite
Docker	none	Dockerfile + compose

License

MIT — see LICENSE.