AutoModeler

AI-powered, conversational data modeling platform. Upload a spreadsheet, chat with an AI assistant, and walk away with a validated ML model running behind a live API and interactive dashboard. No code required.

Built for business analysts who know their data but don't write code.

What It Does

1. Upload a CSV -- AutoModeler instantly profiles it (row counts, types, patterns, anomalies)
2. Explore via natural language -- "Which products are trending up?" returns charts and stats
3. Shape features -- AI suggests transformations, you approve/reject through conversation
4. Model -- recommends and trains appropriate algorithms, shows plain-English comparisons
5. Validate -- cross-validation, confusion matrices, feature importance, per-row explanations
6. Deploy -- one click to a live prediction API endpoint + shareable dashboard

Tech Stack

Layer	Technology
Backend	Python 3.12+, FastAPI, SQLModel (SQLite)
ML	scikit-learn, pandas, numpy
LLM	Anthropic SDK (Claude) for chat orchestration
Frontend	Next.js 15, React 19, TypeScript
UI	Shadcn/UI (Nova), Tailwind CSS, Hugeicons
Charts	Recharts
State	Zustand
Package Mgmt	uv (backend), npm (frontend)

Project Structure

src/
├── backend/
│   ├── api/           # FastAPI route handlers (chat, data, features, models, validation, deploy)
│   ├── core/          # Business logic (analyzer, feature_engine, trainer, validator, explainer, deployer)
│   ├── chat/          # LLM orchestration (prompts, narration, state machine)
│   ├── models/        # SQLModel database models
│   ├── db.py          # Database setup
│   └── main.py        # App entry point
└── frontend/
    ├── app/           # Next.js App Router pages
    │   ├── project/[id]/  # Workspace (chat + data + features + models + validation + deploy)
    │   └── predict/[id]/  # Public prediction dashboard
    ├── components/    # React components (chat, data, features, models, deploy, ui)
    └── lib/           # API client, Zustand stores, types

Getting Started

Prerequisites

• Python 3.12+
• Node.js 18+
• uv package manager
• Anthropic OAuth token (via claude auth login or ANTHROPIC_AUTH_TOKEN env var)

Backend

cd src/backend
uv venv
uv sync
ANTHROPIC_AUTH_TOKEN=sk-ant-oat01-... uv run uvicorn main:app --reload --port 8000

Frontend

cd src/frontend
npm install
npm run dev

The frontend runs on http://localhost:3000 and expects the backend at http://localhost:8000.

Running Tests

# Backend (155 tests)
cd src/backend
uv run pytest

# Frontend
cd src/frontend
npm test

API Overview

Endpoint	Purpose
POST /api/chat/{project_id}	Streamed chat (SSE)
POST /api/data/upload	CSV upload + auto-profiling
GET /api/data/{id}/profile	Data quality report
GET /api/data/{id}/query	Natural language data queries
GET /api/features/{id}/suggestions	AI feature suggestions
POST /api/features/{id}/apply	Apply transformations
POST /api/models/{id}/train	Train models
GET /api/models/{id}/compare	Compare trained models
GET /api/validate/{id}/explain	Feature importance (SHAP-lite)
POST /api/deploy/{id}	Deploy model as API
POST /api/predict/{id}	Make predictions
POST /api/predict/{id}/batch	Batch predictions (CSV)

How It's Built

AutoModeler is built on the code-evolve framework -- a self-evolving project builder. An AI agent reads vision.md and spec.md, then autonomously implements features session after session.

# Run an evolution session (uses OAuth from `claude auth login` or env var)
CLAUDE_CODE_OAUTH_TOKEN=sk-ant-oat01-... ./scripts/evolve.sh

See spec.md for the full feature checklist and JOURNAL.md for the build log.

Current Status

Phases 1-6 complete (foundation through deployment). Phase 7 (polish: onboarding, project management, chat memory, export, responsive design) is next.

License

MIT