Text to Visual

Ask questions in plain English. Get instant charts.
An AI-powered data exploration tool that turns any CSV into interactive visualizations using Google Gemini.

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Features

• Natural Language Queries — Ask things like "Show top 10 by sales" or "Distribution of ages"
• Works with Any CSV — Not locked to any domain. Sales, HR, finance, sports — upload and go
• Powered by Gemini 3.5 Flash — Fast, accurate query-to-chart translation
• 5 Chart Types — Bar, Line, Scatter, Pie, Histogram — picked automatically
• API Key in UI — No .env files needed; paste your key directly in the sidebar
• Premium Dark UI — Built with Streamlit, styled for a polished experience

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Quick Start

Requirements

• Python 3.10 or higher (tested up to 3.14)
• A Google Gemini API key

1. Clone the repo

git clone https://github.com/JayanGupta/Text_to_Visual.git
cd Text_to_Visual

2. Create and activate a virtual environment

Windows:

python -m venv venv
venv\Scripts\activate

Mac / Linux:

python -m venv venv
source venv/bin/activate

You should see (venv) appear at the start of your terminal prompt. This confirms the virtual environment is active.

3. Install dependencies

pip install -r requirements.txt

4. Run the app

streamlit run app.py

5. Use it

1. Paste your Google Gemini API key in the sidebar
2. Upload any CSV file
3. Type a question and hit Generate Visualization

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Why Virtual Environments?

When you clone a Python project on a new machine, none of the packages are pre-installed. A virtual environment (venv) creates an isolated, self-contained Python environment for this project only.

Without venv	With venv
Packages installed globally, can conflict across projects	Each project has its own isolated packages
No guarantee the same versions are used on every machine	requirements.txt pins versions — anyone can reproduce your exact environment
Works on your machine, breaks on others	Works the same everywhere

The golden rule: always activate your venv before running or installing anything for this project.

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Project Structure

Text_to_Visual/
├── app.py                  # Main Streamlit application
├── requirements.txt        # Python dependencies (pip install -r requirements.txt)
├── .gitignore
├── engine/
│   └── query_processor.py  # Gemini-powered NL → Pandas logic engine
└── assets/
    └── charts.py           # Styled Plotly chart library

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Deploy on Render

1. Push this repo to GitHub
2. Go to render.com → New Web Service
3. Connect your GitHub repository
4. Set the following:

Setting	Value
Build Command	pip install -r requirements.txt
Start Command	streamlit run app.py --server.port $PORT --server.address 0.0.0.0

5. Click Deploy

You can optionally set GOOGLE_API_KEY as an environment variable in Render so users don't need to paste it manually.

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Tech Stack

Layer	Technology
UI	Streamlit
AI	Google Gemini 2.5 Flash (google-genai)
Data	Pandas
Charts	Plotly Express

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Example Queries

Query	Chart Type
Top 10 rows by revenue	Bar
Sales trend over time	Line
Age vs salary	Scatter
Revenue share by region	Pie
Distribution of customer ages	Histogram

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Built by Jayan