Analytico

A full-stack AI charting platform designed to make data visualization zero-friction. Upload a CSV, ask a question in plain English, and get an instant chart. No manual configuration required, unless wanted.

The Stack

• Engine: Python / FastAPI / Pandas
• Interface: TypeScript / Next.js / Tailwind CSS
• Visualization: Recharts / Framer Motion
• Intelligence: OpenAI GPT-4o-mini

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Technical Features

1. Smart Data Ingestion ("Data Janitor")

The backend includes a Python module that automatically cleans uploaded CSVs without user intervention:

• Header Normalization: Converts verbose survey-style headers into clean column names

  • "What is your annual salary?" → annual_salary
  • "How old are you?" → age

• Type Detection: Identifies currency ($45,000 → numeric), percentages, and dates by analyzing column content patterns

• Missing Value Handling: Automatically fills missing numeric values with zeros and logs the action for transparency

# The 'Data Janitor' uses multi-phase regex cleanup
def normalize_header(header: str) -> str:
    # 1. Full replacements for known survey patterns
    # 2. Prefix removals (e.g., 'what_is_your_')
    # 3. Suffix removals (e.g., '_choose_all_that_apply')
    # 4. 20-character truncation for clean UI display
    ...

2. Semantic Column Analysis

Before generating visualizations, the system classifies each column into semantic types to prevent nonsensical aggregations:

Type	Example	Behavior
Metric	Revenue, Salary	Can be summed, averaged
Categorical	Department, Status	Used for grouping, auto-switches to COUNT
Identifier	Employee ID, Zip Code	Blocked from sum/mean operations
Temporal	Date, Month	Used as X-axis, auto-resampled

This prevents common mistakes like summing zip codes or averaging ID numbers.

3. Natural Language Querying

Users describe what they want in plain English. The system:

1. Sends the user's question + dataset metadata to GPT-4o-mini
2. Receives a structured JSON response with chart configuration
3. Validates the response against actual column names
4. Renders the chart dynamically

User: "Show me average salary by industry"

→ AI returns:
{
  "xAxisKey": "industry",
  "yAxisKeys": ["annual_salary"],
  "aggregation": "mean",
  "chartType": "bar",
  "title": "Average Salary by Industry"
}

4. Smart Aggregation

The backend handles edge cases that would otherwise produce unusable charts:

• High Cardinality: Groups categories beyond the top 19 into "Others"
• Date Resampling: Automatically groups timestamps into years/months/weeks based on date range
• Aggregation Passthrough: Respects the requested aggregation type (mean, sum, count) through all processing steps

5. Dynamic Visualization

The frontend renders charts using Recharts with:

• Responsive containers that adapt to screen size
• Memoized tooltip and legend components for performance
• Automatic number formatting (currency, percentages, compact notation)
• Framer Motion animations for smooth transitions

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Project Structure

analytico/
├── backend/
│   ├── main.py          # FastAPI app, all endpoints
│   ├── requirements.txt
│   └── venv/
├── frontend/
│   ├── src/
│   │   ├── app/         # Next.js app router
│   │   ├── components/  # React components
│   │   ├── context/     # Global state (DataContext)
│   │   └── lib/         # API client
│   └── package.json
└── README.md

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Running Locally

Backend

cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload

Frontend

cd frontend
npm install
npm run dev

Open http://localhost:3000

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API Endpoints

Method	Endpoint	Description
POST	/upload	Upload CSV, returns cleaned data profile
POST	/query	Natural language → chart configuration
POST	/aggregate	Manual chart configuration
GET	/	Health check

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Future Improvements

• Support for Excel files
• Chart export (PNG, SVG)
• Saved queries / dashboard persistence
• User authentication
• Improved recommendations
• Better dataset cleaning