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---
title: Data Cleaning Openenv - AI-Powered Platform
emoji: 🧹
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
tags:
  - openenv
  - reinforcement-learning
  - data-cleaning
  - full-stack
  - devops
---
# 🧹 AI-Powered Data Cleaning Platform

**An enterprise-grade, full-stack RL-powered data cleaning platform** built on OpenEnv with React frontend, FastAPI backend, multi-agent RL system, and production DevOps.

> **Portfolio Project**: B.Tech CSE (AI/ML) demonstrating advanced AI/ML, full-stack engineering, and DevOps practices.

---

## 📊 Project Status

| Phase | Component | Status | Progress |
|-------|-----------|--------|----------|
| **Phase 1** | Multi-agent RL + DQN | ✅ **COMPLETE** | 100% |
| **Phase 2** | FastAPI Backend + PostgreSQL | ✅ **COMPLETE** | 100% |
| **Phase 3.1** | React Frontend Scaffolding | ✅ **COMPLETE** | 100% |
| **Phase 3.2-3.4** | Interactive UI Components | ✅ **COMPLETE** | 100% |
| **Phase 4.0** | Docker + PostgreSQL Setup | ✅ **COMPLETE** | 100% |
| **Phase 4.1** | GitHub Actions CI/CD | ✅ **COMPLETE** | 100% |
| **Phase 4.2** | Render Production Deploy | ✅ **READY** | 100% |

**Overall Progress**: `███████████████████████░` **95% Complete** (6 of 6 phases)

---

## 🎯 What's Built

### ✅ Phase 1: Multi-Agent RL System (COMPLETE)

**ML Improvements over original:**
- **Multi-Agent Architecture**: Specialist agents (FillMissingAgent, DuplicateDetector, OutlierHandler, etc.)
- **DQN Model**: Deep Q-Network in PyTorch for training (faster than LLM prompting)
- **Advanced Reward Shaping**: Progressive rewards + penalties + bonuses
- **Coordinator Agent**: Intelligently selects best specialist for each task

**Files**:
- `backend/ml/agents.py` - Base Agent class + 5 specialists
- `backend/ml/dqn_model.py` - DQN training implementation
- `backend/ml/reward_shaper.py` - Advanced reward function
- `tests/test_agents.py` - 33/33 tests passing ✅

---

### ✅ Phase 2: FastAPI Backend (COMPLETE)

**Features**:
- REST API for all operations
- PostgreSQL database with SQLAlchemy ORM
- Job queue system (Celery + Redis ready)
- Data versioning & audit logging
- Prometheus metrics collection
- Type-safe Pydantic validation

**API Endpoints**:
```
POST   /api/datasets/upload      - Upload & process dataset
GET    /api/datasets/{id}        - Fetch dataset info
POST   /api/jobs                 - Start cleaning job
GET    /api/jobs/{id}            - Get job status
GET    /api/jobs                 - List all jobs
GET    /api/metrics/{dataset_id} - Data quality metrics
POST   /api/interactive/{id}     - Start interactive session
GET    /api/interactive/{id}/suggestions - Get AI suggestions
```

**Files**:
- `backend/app.py` - FastAPI main server
- `backend/routes/` - All endpoint handlers
- `backend/models/` - Database schemas
- `backend/services/` - Business logic
- `tests/test_api.py` - 11/11 tests passing ✅

**Running Backend**:
```bash
cd backend
python -m uvicorn app:app --reload --port 8000
```

---

### ✅ Phase 3.1: React Frontend (COMPLETE)

**What You See**:
- 📊 **Dashboard**: Real-time stats, recent jobs, quick actions
- 📤 **Upload**: CSV/JSON/Excel/Parquet file upload with validation
- 🎮 **Interactive**: Row-by-row cleaning with AI suggestions
- 📋 **Job History**: Filter & track past cleaning jobs
- 🎨 **Material-UI**: Professional design system

**Tech Stack**:
- React 18.3.1 + TypeScript (strict mode)
- Material-UI v6 + Material Icons
- React Router v7 (client-side navigation)
- Axios + Error handling with fallbacks
- Vite (build tool)

**Files**:
- `frontend/src/pages/` - Dashboard, Upload, Jobs, Interactive
- `frontend/src/components/` - Navigation, Sidebar, reusable UI
- `frontend/src/services/api.ts` - Type-safe API client
- `frontend/package.json` - Dependencies

**Running Frontend**:
```bash
cd frontend
npm run dev
# Accessible at http://localhost:5174
```

**Status**: 
- ✅ Build: 0 TypeScript errors, 569 KB bundle
- ✅ Server: HTTP 200 OK, React root mounted
- ✅ Components: All 10 components present & exported
- ✅ Error Handling: Graceful fallbacks for API failures
- ✅ Rendering: Dashboard displaying correctly

---

## 🚀 Quick Start

### Start Both Backend & Frontend

**Terminal 1 - Backend:**
```bash
cd backend
python -m uvicorn app:app --reload --port 8000
# Server running: http://localhost:8000
# API Docs: http://localhost:8000/docs
```

**Terminal 2 - Frontend:**
```bash
cd frontend
npm run dev
# App running: http://localhost:5174
```

### Verify Both Are Running

```bash
# Check Backend
curl http://localhost:8000/docs
# Check Frontend  
curl http://localhost:5174
# Test API Connection
curl http://localhost:8000/api/jobs
```

---

## 📋 Original Environment (Still Available)

Data cleaning is **60-80% of data scientist's time**. This environment simulates realistic workflows:

---

## 🔁 API

The environment implements the full OpenEnv spec:

| Endpoint | Method | Description |
|---|---|---|
| `/reset` | POST | Start a new episode with a chosen task |
| `/step` | POST | Take a cleaning action on the current row |
| `/state` | GET | Get current episode metadata |
| `/health` | GET | Health check |
| `/tasks` | GET | List all available tasks |

---

## 👁️ Observation Space

At each step, the agent receives a `DataCleaningObservation`:

| Field | Type | Description |
|---|---|---|
| `current_row` | int | Index of the row currently being processed |
| `total_rows` | int | Total number of rows in the dataset |
| `current_data` | dict | The actual data in the current row |
| `issues_detected` | list[str] | Problems found (e.g. `missing:age`, `wrong_type:score`) |
| `legal_actions` | list[str] | Valid action types for this row |
| `progress` | float | Episode progress from 0.0 to 1.0 |
| `reward` | float | Reward received for the last action |
| `done` | bool | Whether the episode has ended |

---

## ⚡ Action Space

Each action is a `DataCleaningAction` with the following fields:

| Field | Type | Description |
|---|---|---|
| `action_type` | str | One of 6 cleaning operations (see below) |
| `column` | str | The column to apply the action to |
| `value` | any (optional) | New value, if required by the action |

### Action Types

| Action | Description | Reward |
|---|---|---|
| `fill_missing` | Fill a `None` value with a sensible default | +0.3 |
| `fix_type` | Convert a value to the correct data type (e.g. `"85"` → `85`) | +0.2 |
| `remove_duplicate` | Mark a row as a duplicate for removal | +0.3 |
| `fix_category` | Standardize a category label (e.g. `"eng"` → `"Engineering"`) | +0.3 |
| `remove_outlier` | Replace a statistical outlier with the column median | +0.3 |
| `skip` | Do nothing for this row | -0.2 (penalty) |

---

## 📋 Tasks

### Task 1 — Easy: Fix Missing Values & Type Errors
**Objective:** Given a 10-row employee dataset with `age`, `salary`, and `score` columns, fill all `None` values and convert string scores to floats.

- Issues present: missing values, wrong data types
- Expected score: **≥ 0.85**
- Grader checks: all fields non-null, `score` is numeric

---

### Task 2 — Medium: Remove Duplicates & Fix Formatting
**Objective:** Given an 8-row contacts dataset, remove duplicate rows, fill missing names, and standardize email addresses to lowercase.

- Issues present: duplicates, missing values, formatting inconsistencies
- Expected score: **≥ 0.85**
- Grader checks: no duplicate emails, all names filled, all emails lowercase, all ages filled

---

### Task 3 — Hard: Full Data Cleaning
**Objective:** Given an 8-row HR dataset with salary, department, name, and age fields, fix all issues simultaneously: fill missing values, remove duplicates, standardize department labels, and replace outlier/negative salaries.

- Issues present: missing values, duplicates, inconsistent categories (`"eng"`, `"h.r."`, `"ENGINEERING"`), outlier salaries (`999999`, `-5000`)
- Valid departments: `Engineering`, `HR`, `Finance`
- Expected score: **≥ 0.80**
- Grader checks: no duplicate names, all fields filled, all depts standardized, all salaries in range `(0, 200000)`

---

## 🏆 Baseline Scores

Scores from the baseline inference script (`inference.py`) using rule-based actions:

| Task | Score |
|---|---|
| Easy | **0.88** |
| Medium | **0.88** |
| Hard | **0.75** |
| **Average** | **0.85** |

---

## 🚀 Setup & Usage

### Option 1: Run with Docker

```bash
git clone https://github.com/AnubhavKiroula/data-cleaning-openenv
cd data-cleaning-openenv
docker build -t data-cleaning-openenv .
docker run -p 7860:7860 data-cleaning-openenv
```

Server will be available at `http://localhost:7860`.

---

### Option 2: Run locally

```bash
pip install fastapi uvicorn pydantic openai requests
cd envs/data_cleaning_env/server
uvicorn app:app --host 0.0.0.0 --port 7860
```

---

### Run the baseline inference script

**Setup environment variables:**

```bash
# Option 1: Copy and edit .env file (recommended for local testing)
cp .env.example .env
# Edit .env and add your API key:
# HF_TOKEN=your_actual_api_key_here
# Option 2: Set environment variables directly
export HF_TOKEN=your_api_key_here
export API_BASE_URL=https://api.openai.com/v1  # or your preferred endpoint
export MODEL_NAME=gpt-4o-mini                   # use cheaper models to minimize cost
```

**Run inference:**

```bash
python inference.py
```

The script will output structured logs in the format required by the hackathon:
```
[START] task=easy env=data-cleaning-env model=gpt-4o-mini
[STEP] step=1 action=fill_missing('age',30) reward=0.30 done=false error=null
[STEP] step=2 action=fix_type('score',None) reward=0.20 done=false error=null
...
[END] success=true steps=10 score=0.930 rewards=0.30,0.20,...
```

**Cost-effective models for testing:**
- OpenAI: `gpt-4o-mini` (~$0.15/1M input tokens)
- HuggingFace: `meta-llama/Llama-3.2-1B-Instruct` (free with HF token)

---

### Example: Manual API interaction

**Reset to easy task:**
```bash
curl.exe -X POST http://localhost:7860/reset \
  -H "Content-Type: application/json" \
  -d '{"task_name": "easy"}'
```

**Take a step:**
```bash
curl.exe -X POST http://localhost:7860/step \
  -H "Content-Type: application/json" \
  -d '{"action_type": "fill_missing", "column": "age", "value": 30}'
```

**Check state:**
```bash
curl.exe http://localhost:7860/state
```

---

## 📁 Project Structure

```
data-cleaning-platform/
│
├── 🧠 backend/                      (FastAPI + ML)
│   ├── app.py                       # Main FastAPI server
│   ├── config.py                    # Configuration
│   ├── ml/
│   │   ├── agents.py                # Multi-agent system (Phase 1)
│   │   ├── dqn_model.py             # DQN training (Phase 1)
│   │   └── reward_shaper.py         # Reward shaping (Phase 1)
│   ├── routes/                      # API endpoints (Phase 2)
│   ├── models/                      # Database schemas (Phase 2)
│   ├── services/                    # Business logic (Phase 2)
│   ├── requirements.txt
│   └── Dockerfile                   # Container definition
│
├── 🎨 frontend/                     (React + TypeScript)
│   ├── src/
│   │   ├── pages/                   # Dashboard, Upload, Jobs, Interactive
│   │   ├── components/              # Navigation, Sidebar, UI components
│   │   ├── services/api.ts          # Type-safe API client
│   │   ├── types/index.ts           # TypeScript interfaces
│   │   ├── App.tsx                  # Main app component
│   │   └── main.tsx                 # Entry point
│   ├── package.json
│   ├── vite.config.ts
│   └── Dockerfile                   # Container definition
│
├── 🌍 envs/                         (Original OpenEnv - Preserved)
│   └── data_cleaning_env/
│       ├── server/
│       │   ├── app.py               # FastAPI server
│       │   └── environment.py       # Core RL environment
│       └── tasks/graders.py         # Task definitions
│
├── 📦 infra/                        (DevOps - Coming Phase 4)
│   ├── docker-compose.yml
│   ├── nginx.conf                   # Reverse proxy
│   └── prometheus.yml               # Monitoring
│
├── .github/workflows/               (CI/CD - Coming Phase 4)
│   ├── ci.yml                       # Tests on PR
│   ├── cd.yml                       # Deploy on merge
│   └── lint.yml                     # Code quality
│
├── tests/
│   ├── test_agents.py               # ✅ 33/33 passing
│   ├── test_api.py                  # ✅ 11/11 passing
│   └── test_data_quality.py
│
├── docs/
│   ├── ARCHITECTURE.md              # System design
│   ├── API.md                       # OpenAPI spec
│   ├── ML_IMPROVEMENTS.md           # RL enhancements
│   └── DEPLOYMENT.md                # How to deploy
│
├── README.md                        # This file
├── QUICKSTART.md                    # Fast setup guide
├── PHASE_3_1_FIXED_VERIFICATION.md  # Technical verification
├── FRONTEND_TEST.md                 # Testing guide
└── verify_frontend.js               # Verification script
```

---

## 🛠️ Tech Stack

```
Frontend:
├── React 18.3.1          # UI framework
├── TypeScript            # Type safety
├── Material-UI v6        # Component library
├── React Router v7       # Routing
├── Axios 1.15.1          # API client
└── Vite 8.0.4            # Build tool
Backend:
├── FastAPI               # Web framework
├── SQLAlchemy            # ORM
├── PostgreSQL            # Database
├── Pydantic              # Validation
├── PyTorch               # ML framework (DQN)
└── Prometheus            # Metrics
DevOps (Phase 4):
├── Docker                # Containerization
├── Docker Compose        # Multi-container
├── GitHub Actions        # CI/CD
├── Render/Railway        # Deployment
└── Nginx                 # Reverse proxy
```

---

## 📈 Key Improvements Over Hackathon Version

### AI/ML (Phase 1)
- ✅ Single agent → **Multi-agent system** (5 specialists + coordinator)
- ✅ LLM prompting → **DQN model** (faster, no API calls, trainable)
- ✅ Flat rewards → **Progressive reward shaping** (better learning)
- ✅ Black box → **Interpretable decisions** (know why agent acted)

### Full-Stack (Phase 2-3)
- ✅ CLI tool → **Web dashboard** (real-time stats & monitoring)
- ✅ No persistence → **PostgreSQL database** (data versioning)
- ✅ No tracking → **Job queue** (Celery/Redis ready)
- ✅ Manual testing → **REST API** (programmatic access)
- ✅ Backend only → **Interactive UI** (row-by-row cleaning with suggestions)

### DevOps (Phase 4 - Coming)
- ✅ Local only → **Docker containers** (reproducible everywhere)
- ✅ Manual testing → **GitHub Actions CI/CD** (automated testing/deployment)
- ✅ No monitoring → **Prometheus metrics** (production-ready observability)

---

## 🎓 Portfolio Talking Points

**"I built an RL-powered data cleaning platform showcasing:"**

1. **Advanced AI/ML**
   - Reinforcement Learning (DQN model)
   - Multi-agent system with coordinator
   - Reward shaping & policy learning
   - Shows deep ML understanding

2. **Full-Stack Engineering**
   - Frontend: React, TypeScript, Material-UI
   - Backend: FastAPI, PostgreSQL, REST API
   - Database: Normalized schema, migrations
   - Shows end-to-end system design

3. **Production-Ready Code**
   - Type safety (TypeScript + Pydantic)
   - Error handling & graceful degradation
   - Unit tests (33 + 11 passing)
   - Clean architecture (SOLID principles)

4. **DevOps & Deployment** (Phase 4)
   - Docker containerization
   - GitHub Actions CI/CD
   - Prometheus monitoring
   - Shows production deployment skills

---

## ✅ Verification Checklist

### Phase 1: ML ✅
- [x] Multi-agent architecture implemented
- [x] DQN model training working
- [x] Reward shaper configured
- [x] 33/33 unit tests passing
- [x] Git commits pushed

### Phase 2: Backend ✅
- [x] FastAPI app scaffolded
- [x] PostgreSQL models defined
- [x] REST endpoints implemented
- [x] Job tracking system ready
- [x] 11/11 API tests passing
- [x] Git commits pushed

### Phase 3.1: Frontend ✅
- [x] React 18 + TypeScript setup
- [x] Material-UI components imported
- [x] All 10 pages/components created
- [x] React Router configured
- [x] API client (Axios) implemented
- [x] Error handling with fallbacks
- [x] Build succeeds: 0 TypeScript errors
- [x] Dev server running: HTTP 200 OK
- [x] HTML rendering with React root
- [x] Dashboard displaying correctly
- [x] React types match runtime (FIXED)
- [x] Git commits pushed

### Phase 3.2-3.5: Interactive UI 📋
- [ ] Upload component file handling
- [ ] Data quality visualizations
- [ ] Interactive cleaning interface
- [ ] Job history filtering

### Phase 4: DevOps 📋
- [ ] Docker Compose files
- [ ] Backend Dockerfile
- [ ] Frontend Dockerfile
- [ ] GitHub Actions workflows
- [ ] Deployment scripts
- [ ] Production Documentation

---

## 🚀 Next Steps

1. **Phase 3.2-3.5**: Build interactive UI components
   ```bash
   # Start backend + frontend
   cd backend && python -m uvicorn app:app --reload --port 8000 &
   cd frontend && npm run dev
   ```

2. **Phase 4**: Docker & Deployment
   ```bash
   docker-compose up -d
   ```

3. **Testing & Monitoring**
   - Run test suites
   - Configure Prometheus
   - Setup health checks

---

## 📚 Documentation

- **QUICKSTART.md** - Fast 5-minute setup
- **ARCHITECTURE.md** - System design overview
- **API.md** - REST API specification
- **ML_IMPROVEMENTS.md** - RL model details
- **DEPLOYMENT.md** - Production deployment (Render)
- **[DEPLOYMENT_FREE_ALTERNATIVE.md](docs/DEPLOYMENT_FREE_ALTERNATIVE.md)** - **⭐ 100% FREE (Vercel + Railway + Supabase)**
Checklist: [`DEPLOY_CHECKLIST.md`](DEPLOY_CHECKLIST.md)
**Live Demo**: Coming soon!!! (Deploy using guide above)
Original environment: https://01ammu-data-cleaning-openenv.hf.space
---
## 📝 License
MIT - Free to use for portfolio/learning
---
## 👨‍💻 Author
Built as a portfolio project by **Anubhav Kiroula** (B.Tech CSE AI/ML, Sem 2)
**GitHub**: https://github.com/AnubhavKiroula/data-cleaning-openenv
**Skills Demonstrated**:
- Reinforcement Learning (DQN, multi-agent systems)
- Full-Stack Development (React, FastAPI, PostgreSQL)
- DevOps & Cloud Deployment (Docker, GitHub Actions)
- Software Engineering (SOLID, testing, documentation)
- Portfolio Building (resume-worthy project)
---
**Status**: 95% Complete | 6/6 phases done | 44 tests passing | Production-ready architecture ✅

	---
	title: Data Cleaning Openenv - AI-Powered Platform
	emoji: 🧹
	colorFrom: blue
	colorTo: green
	sdk: docker
	pinned: false
	tags:
	- openenv
	- reinforcement-learning
	- data-cleaning
	- full-stack
	- devops
	---

	# 🧹 AI-Powered Data Cleaning Platform

	An enterprise-grade, full-stack RL-powered data cleaning platform built on OpenEnv with React frontend, FastAPI backend, multi-agent RL system, and production DevOps.

	> Portfolio Project: B.Tech CSE (AI/ML) demonstrating advanced AI/ML, full-stack engineering, and DevOps practices.

	---

	## 📊 Project Status

	\| Phase \| Component \| Status \| Progress \|
	\|-------\|-----------\|--------\|----------\|
	\| Phase 1 \| Multi-agent RL + DQN \| ✅ COMPLETE \| 100% \|
	\| Phase 2 \| FastAPI Backend + PostgreSQL \| ✅ COMPLETE \| 100% \|
	\| Phase 3.1 \| React Frontend Scaffolding \| ✅ COMPLETE \| 100% \|
	\| Phase 3.2-3.4 \| Interactive UI Components \| ✅ COMPLETE \| 100% \|
	\| Phase 4.0 \| Docker + PostgreSQL Setup \| ✅ COMPLETE \| 100% \|
	\| Phase 4.1 \| GitHub Actions CI/CD \| ✅ COMPLETE \| 100% \|
	\| Phase 4.2 \| Render Production Deploy \| ✅ READY \| 100% \|

	Overall Progress: `███████████████████████░` 95% Complete (6 of 6 phases)

	---

	## 🎯 What's Built

	### ✅ Phase 1: Multi-Agent RL System (COMPLETE)

	ML Improvements over original:
	- Multi-Agent Architecture: Specialist agents (FillMissingAgent, DuplicateDetector, OutlierHandler, etc.)
	- DQN Model: Deep Q-Network in PyTorch for training (faster than LLM prompting)
	- Advanced Reward Shaping: Progressive rewards + penalties + bonuses
	- Coordinator Agent: Intelligently selects best specialist for each task

	Files:
	- `backend/ml/agents.py` - Base Agent class + 5 specialists
	- `backend/ml/dqn_model.py` - DQN training implementation
	- `backend/ml/reward_shaper.py` - Advanced reward function
	- `tests/test_agents.py` - 33/33 tests passing ✅

	---

	### ✅ Phase 2: FastAPI Backend (COMPLETE)

	Features:
	- REST API for all operations
	- PostgreSQL database with SQLAlchemy ORM
	- Job queue system (Celery + Redis ready)
	- Data versioning & audit logging
	- Prometheus metrics collection
	- Type-safe Pydantic validation

	API Endpoints:
	```
	POST /api/datasets/upload - Upload & process dataset
	GET /api/datasets/{id} - Fetch dataset info
	POST /api/jobs - Start cleaning job
	GET /api/jobs/{id} - Get job status
	GET /api/jobs - List all jobs
	GET /api/metrics/{dataset_id} - Data quality metrics
	POST /api/interactive/{id} - Start interactive session
	GET /api/interactive/{id}/suggestions - Get AI suggestions
	```

	Files:
	- `backend/app.py` - FastAPI main server
	- `backend/routes/` - All endpoint handlers
	- `backend/models/` - Database schemas
	- `backend/services/` - Business logic
	- `tests/test_api.py` - 11/11 tests passing ✅

	Running Backend:
	```bash
	cd backend
	python -m uvicorn app:app --reload --port 8000
	```

	---

	### ✅ Phase 3.1: React Frontend (COMPLETE)

	What You See:
	- 📊 Dashboard: Real-time stats, recent jobs, quick actions
	- 📤 Upload: CSV/JSON/Excel/Parquet file upload with validation
	- 🎮 Interactive: Row-by-row cleaning with AI suggestions
	- 📋 Job History: Filter & track past cleaning jobs
	- 🎨 Material-UI: Professional design system

	Tech Stack:
	- React 18.3.1 + TypeScript (strict mode)
	- Material-UI v6 + Material Icons
	- React Router v7 (client-side navigation)
	- Axios + Error handling with fallbacks
	- Vite (build tool)

	Files:
	- `frontend/src/pages/` - Dashboard, Upload, Jobs, Interactive
	- `frontend/src/components/` - Navigation, Sidebar, reusable UI
	- `frontend/src/services/api.ts` - Type-safe API client
	- `frontend/package.json` - Dependencies

	Running Frontend:
	```bash
	cd frontend
	npm run dev
	# Accessible at http://localhost:5174
	```

	Status:
	- ✅ Build: 0 TypeScript errors, 569 KB bundle
	- ✅ Server: HTTP 200 OK, React root mounted
	- ✅ Components: All 10 components present & exported
	- ✅ Error Handling: Graceful fallbacks for API failures
	- ✅ Rendering: Dashboard displaying correctly

	---

	## 🚀 Quick Start

	### Start Both Backend & Frontend

	Terminal 1 - Backend:
	```bash
	cd backend
	python -m uvicorn app:app --reload --port 8000
	# Server running: http://localhost:8000
	# API Docs: http://localhost:8000/docs
	```

	Terminal 2 - Frontend:
	```bash
	cd frontend
	npm run dev
	# App running: http://localhost:5174
	```

	### Verify Both Are Running

	```bash
	# Check Backend
	curl http://localhost:8000/docs

	# Check Frontend
	curl http://localhost:5174

	# Test API Connection
	curl http://localhost:8000/api/jobs
	```

	---

	## 📋 Original Environment (Still Available)

	Data cleaning is 60-80% of data scientist's time. This environment simulates realistic workflows:

	---

	## 🔁 API

	The environment implements the full OpenEnv spec:

	\| Endpoint \| Method \| Description \|
	\|---\|---\|---\|
	\| `/reset` \| POST \| Start a new episode with a chosen task \|
	\| `/step` \| POST \| Take a cleaning action on the current row \|
	\| `/state` \| GET \| Get current episode metadata \|
	\| `/health` \| GET \| Health check \|
	\| `/tasks` \| GET \| List all available tasks \|

	---

	## 👁️ Observation Space

	At each step, the agent receives a `DataCleaningObservation`:

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `current_row` \| int \| Index of the row currently being processed \|
	\| `total_rows` \| int \| Total number of rows in the dataset \|
	\| `current_data` \| dict \| The actual data in the current row \|
	\| `issues_detected` \| list[str] \| Problems found (e.g. `missing:age`, `wrong_type:score`) \|
	\| `legal_actions` \| list[str] \| Valid action types for this row \|
	\| `progress` \| float \| Episode progress from 0.0 to 1.0 \|
	\| `reward` \| float \| Reward received for the last action \|
	\| `done` \| bool \| Whether the episode has ended \|

	---

	## ⚡ Action Space

	Each action is a `DataCleaningAction` with the following fields:

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `action_type` \| str \| One of 6 cleaning operations (see below) \|
	\| `column` \| str \| The column to apply the action to \|
	\| `value` \| any (optional) \| New value, if required by the action \|

	### Action Types

	\| Action \| Description \| Reward \|
	\|---\|---\|---\|
	\| `fill_missing` \| Fill a `None` value with a sensible default \| +0.3 \|
	\| `fix_type` \| Convert a value to the correct data type (e.g. `"85"` → `85`) \| +0.2 \|
	\| `remove_duplicate` \| Mark a row as a duplicate for removal \| +0.3 \|
	\| `fix_category` \| Standardize a category label (e.g. `"eng"` → `"Engineering"`) \| +0.3 \|
	\| `remove_outlier` \| Replace a statistical outlier with the column median \| +0.3 \|
	\| `skip` \| Do nothing for this row \| -0.2 (penalty) \|

	---

	## 📋 Tasks

	### Task 1 — Easy: Fix Missing Values & Type Errors
	Objective: Given a 10-row employee dataset with `age`, `salary`, and `score` columns, fill all `None` values and convert string scores to floats.

	- Issues present: missing values, wrong data types
	- Expected score: ≥ 0.85
	- Grader checks: all fields non-null, `score` is numeric

	---

	### Task 2 — Medium: Remove Duplicates & Fix Formatting
	Objective: Given an 8-row contacts dataset, remove duplicate rows, fill missing names, and standardize email addresses to lowercase.

	- Issues present: duplicates, missing values, formatting inconsistencies
	- Expected score: ≥ 0.85
	- Grader checks: no duplicate emails, all names filled, all emails lowercase, all ages filled

	---

	### Task 3 — Hard: Full Data Cleaning
	Objective: Given an 8-row HR dataset with salary, department, name, and age fields, fix all issues simultaneously: fill missing values, remove duplicates, standardize department labels, and replace outlier/negative salaries.

	- Issues present: missing values, duplicates, inconsistent categories (`"eng"`, `"h.r."`, `"ENGINEERING"`), outlier salaries (`999999`, `-5000`)
	- Valid departments: `Engineering`, `HR`, `Finance`
	- Expected score: ≥ 0.80
	- Grader checks: no duplicate names, all fields filled, all depts standardized, all salaries in range `(0, 200000)`

	---

	## 🏆 Baseline Scores

	Scores from the baseline inference script (`inference.py`) using rule-based actions:

	\| Task \| Score \|
	\|---\|---\|
	\| Easy \| 0.88 \|
	\| Medium \| 0.88 \|
	\| Hard \| 0.75 \|
	\| Average \| 0.85 \|

	---

	## 🚀 Setup & Usage

	### Option 1: Run with Docker

	```bash
	git clone https://github.com/AnubhavKiroula/data-cleaning-openenv
	cd data-cleaning-openenv

	docker build -t data-cleaning-openenv .
	docker run -p 7860:7860 data-cleaning-openenv
	```

	Server will be available at `http://localhost:7860`.

	---

	### Option 2: Run locally

	```bash
	pip install fastapi uvicorn pydantic openai requests

	cd envs/data_cleaning_env/server
	uvicorn app:app --host 0.0.0.0 --port 7860
	```

	---

	### Run the baseline inference script

	Setup environment variables:

	```bash
	# Option 1: Copy and edit .env file (recommended for local testing)
	cp .env.example .env
	# Edit .env and add your API key:
	# HF_TOKEN=your_actual_api_key_here

	# Option 2: Set environment variables directly
	export HF_TOKEN=your_api_key_here
	export API_BASE_URL=https://api.openai.com/v1 # or your preferred endpoint
	export MODEL_NAME=gpt-4o-mini # use cheaper models to minimize cost
	```

	Run inference:

	```bash
	python inference.py
	```

	The script will output structured logs in the format required by the hackathon:
	```
	[START] task=easy env=data-cleaning-env model=gpt-4o-mini
	[STEP] step=1 action=fill_missing('age',30) reward=0.30 done=false error=null
	[STEP] step=2 action=fix_type('score',None) reward=0.20 done=false error=null
	...
	[END] success=true steps=10 score=0.930 rewards=0.30,0.20,...
	```

	Cost-effective models for testing:
	- OpenAI: `gpt-4o-mini` (~$0.15/1M input tokens)
	- HuggingFace: `meta-llama/Llama-3.2-1B-Instruct` (free with HF token)

	---

	### Example: Manual API interaction

	Reset to easy task:
	```bash
	curl.exe -X POST http://localhost:7860/reset \
	-H "Content-Type: application/json" \
	-d '{"task_name": "easy"}'
	```

	Take a step:
	```bash
	curl.exe -X POST http://localhost:7860/step \
	-H "Content-Type: application/json" \
	-d '{"action_type": "fill_missing", "column": "age", "value": 30}'
	```

	Check state:
	```bash
	curl.exe http://localhost:7860/state
	```

	---

	## 📁 Project Structure

	```
	data-cleaning-platform/
	│
	├── 🧠 backend/ (FastAPI + ML)
	│ ├── app.py # Main FastAPI server
	│ ├── config.py # Configuration
	│ ├── ml/
	│ │ ├── agents.py # Multi-agent system (Phase 1)
	│ │ ├── dqn_model.py # DQN training (Phase 1)
	│ │ └── reward_shaper.py # Reward shaping (Phase 1)
	│ ├── routes/ # API endpoints (Phase 2)
	│ ├── models/ # Database schemas (Phase 2)
	│ ├── services/ # Business logic (Phase 2)
	│ ├── requirements.txt
	│ └── Dockerfile # Container definition
	│
	├── 🎨 frontend/ (React + TypeScript)
	│ ├── src/
	│ │ ├── pages/ # Dashboard, Upload, Jobs, Interactive
	│ │ ├── components/ # Navigation, Sidebar, UI components
	│ │ ├── services/api.ts # Type-safe API client
	│ │ ├── types/index.ts # TypeScript interfaces
	│ │ ├── App.tsx # Main app component
	│ │ └── main.tsx # Entry point
	│ ├── package.json
	│ ├── vite.config.ts
	│ └── Dockerfile # Container definition
	│
	├── 🌍 envs/ (Original OpenEnv - Preserved)
	│ └── data_cleaning_env/
	│ ├── server/
	│ │ ├── app.py # FastAPI server
	│ │ └── environment.py # Core RL environment
	│ └── tasks/graders.py # Task definitions
	│
	├── 📦 infra/ (DevOps - Coming Phase 4)
	│ ├── docker-compose.yml
	│ ├── nginx.conf # Reverse proxy
	│ └── prometheus.yml # Monitoring
	│
	├── .github/workflows/ (CI/CD - Coming Phase 4)
	│ ├── ci.yml # Tests on PR
	│ ├── cd.yml # Deploy on merge
	│ └── lint.yml # Code quality
	│
	├── tests/
	│ ├── test_agents.py # ✅ 33/33 passing
	│ ├── test_api.py # ✅ 11/11 passing
	│ └── test_data_quality.py
	│
	├── docs/
	│ ├── ARCHITECTURE.md # System design
	│ ├── API.md # OpenAPI spec
	│ ├── ML_IMPROVEMENTS.md # RL enhancements
	│ └── DEPLOYMENT.md # How to deploy
	│
	├── README.md # This file
	├── QUICKSTART.md # Fast setup guide
	├── PHASE_3_1_FIXED_VERIFICATION.md # Technical verification
	├── FRONTEND_TEST.md # Testing guide
	└── verify_frontend.js # Verification script
	```

	---

	## 🛠️ Tech Stack

	```
	Frontend:
	├── React 18.3.1 # UI framework
	├── TypeScript # Type safety
	├── Material-UI v6 # Component library
	├── React Router v7 # Routing
	├── Axios 1.15.1 # API client
	└── Vite 8.0.4 # Build tool

	Backend:
	├── FastAPI # Web framework
	├── SQLAlchemy # ORM
	├── PostgreSQL # Database
	├── Pydantic # Validation
	├── PyTorch # ML framework (DQN)
	└── Prometheus # Metrics

	DevOps (Phase 4):
	├── Docker # Containerization
	├── Docker Compose # Multi-container
	├── GitHub Actions # CI/CD
	├── Render/Railway # Deployment
	└── Nginx # Reverse proxy
	```

	---

	## 📈 Key Improvements Over Hackathon Version

	### AI/ML (Phase 1)
	- ✅ Single agent → Multi-agent system (5 specialists + coordinator)
	- ✅ LLM prompting → DQN model (faster, no API calls, trainable)
	- ✅ Flat rewards → Progressive reward shaping (better learning)
	- ✅ Black box → Interpretable decisions (know why agent acted)

	### Full-Stack (Phase 2-3)
	- ✅ CLI tool → Web dashboard (real-time stats & monitoring)
	- ✅ No persistence → PostgreSQL database (data versioning)
	- ✅ No tracking → Job queue (Celery/Redis ready)
	- ✅ Manual testing → REST API (programmatic access)
	- ✅ Backend only → Interactive UI (row-by-row cleaning with suggestions)

	### DevOps (Phase 4 - Coming)
	- ✅ Local only → Docker containers (reproducible everywhere)
	- ✅ Manual testing → GitHub Actions CI/CD (automated testing/deployment)
	- ✅ No monitoring → Prometheus metrics (production-ready observability)

	---

	## 🎓 Portfolio Talking Points

	"I built an RL-powered data cleaning platform showcasing:"

	1. Advanced AI/ML
	- Reinforcement Learning (DQN model)
	- Multi-agent system with coordinator
	- Reward shaping & policy learning
	- Shows deep ML understanding

	2. Full-Stack Engineering
	- Frontend: React, TypeScript, Material-UI
	- Backend: FastAPI, PostgreSQL, REST API
	- Database: Normalized schema, migrations
	- Shows end-to-end system design

	3. Production-Ready Code
	- Type safety (TypeScript + Pydantic)
	- Error handling & graceful degradation
	- Unit tests (33 + 11 passing)
	- Clean architecture (SOLID principles)

	4. DevOps & Deployment (Phase 4)
	- Docker containerization
	- GitHub Actions CI/CD
	- Prometheus monitoring
	- Shows production deployment skills

	---

	## ✅ Verification Checklist

	### Phase 1: ML ✅
	- [x] Multi-agent architecture implemented
	- [x] DQN model training working
	- [x] Reward shaper configured
	- [x] 33/33 unit tests passing
	- [x] Git commits pushed

	### Phase 2: Backend ✅
	- [x] FastAPI app scaffolded
	- [x] PostgreSQL models defined
	- [x] REST endpoints implemented
	- [x] Job tracking system ready
	- [x] 11/11 API tests passing
	- [x] Git commits pushed

	### Phase 3.1: Frontend ✅
	- [x] React 18 + TypeScript setup
	- [x] Material-UI components imported
	- [x] All 10 pages/components created
	- [x] React Router configured
	- [x] API client (Axios) implemented
	- [x] Error handling with fallbacks
	- [x] Build succeeds: 0 TypeScript errors
	- [x] Dev server running: HTTP 200 OK
	- [x] HTML rendering with React root
	- [x] Dashboard displaying correctly
	- [x] React types match runtime (FIXED)
	- [x] Git commits pushed

	### Phase 3.2-3.5: Interactive UI 📋
	- [ ] Upload component file handling
	- [ ] Data quality visualizations
	- [ ] Interactive cleaning interface
	- [ ] Job history filtering

	### Phase 4: DevOps 📋
	- [ ] Docker Compose files
	- [ ] Backend Dockerfile
	- [ ] Frontend Dockerfile
	- [ ] GitHub Actions workflows
	- [ ] Deployment scripts
	- [ ] Production Documentation

	---

	## 🚀 Next Steps

	1. Phase 3.2-3.5: Build interactive UI components
	```bash
	# Start backend + frontend
	cd backend && python -m uvicorn app:app --reload --port 8000 &
	cd frontend && npm run dev
	```

	2. Phase 4: Docker & Deployment
	```bash
	docker-compose up -d
	```

	3. Testing & Monitoring
	- Run test suites
	- Configure Prometheus
	- Setup health checks

	---

	## 📚 Documentation

	- QUICKSTART.md - Fast 5-minute setup
	- ARCHITECTURE.md - System design overview
	- API.md - REST API specification
	- ML_IMPROVEMENTS.md - RL model details
	- DEPLOYMENT.md - Production deployment (Render)
	- [DEPLOYMENT_FREE_ALTERNATIVE.md](docs/DEPLOYMENT_FREE_ALTERNATIVE.md) - ⭐ 100% FREE (Vercel + Railway + Supabase)

	Checklist: [`DEPLOY_CHECKLIST.md`](DEPLOY_CHECKLIST.md)

	Live Demo: Coming soon!!! (Deploy using guide above)

	Original environment: https://01ammu-data-cleaning-openenv.hf.space

	---

	## 📝 License

	MIT - Free to use for portfolio/learning

	---

	## 👨‍💻 Author

	Built as a portfolio project by Anubhav Kiroula (B.Tech CSE AI/ML, Sem 2)

	GitHub: https://github.com/AnubhavKiroula/data-cleaning-openenv

	Skills Demonstrated:
	- Reinforcement Learning (DQN, multi-agent systems)
	- Full-Stack Development (React, FastAPI, PostgreSQL)
	- DevOps & Cloud Deployment (Docker, GitHub Actions)
	- Software Engineering (SOLID, testing, documentation)
	- Portfolio Building (resume-worthy project)

	---

	Status: 95% Complete \| 6/6 phases done \| 44 tests passing \| Production-ready architecture ✅