https://github.com/abhigyan126/prompt2query
A Python desktop application for streamlined data analysis, enabling users to generate and execute Pandas and SQL queries with ease. Focus on reducing analysis time through an intuitive interface and efficient workflows
https://github.com/abhigyan126/prompt2query
data-analysis data-science data-visualization database gemini generative-ai ide llm pandas pandas-interface python sql-interface
Last synced: 7 days ago
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A Python desktop application for streamlined data analysis, enabling users to generate and execute Pandas and SQL queries with ease. Focus on reducing analysis time through an intuitive interface and efficient workflows
- Host: GitHub
- URL: https://github.com/abhigyan126/prompt2query
- Owner: Abhigyan126
- Created: 2024-09-02T10:25:25.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-22T17:21:27.000Z (9 months ago)
- Last Synced: 2025-04-28T14:47:07.779Z (6 months ago)
- Topics: data-analysis, data-science, data-visualization, database, gemini, generative-ai, ide, llm, pandas, pandas-interface, python, sql-interface
- Language: Python
- Homepage:
- Size: 9.51 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Prompt2query
# Prompt2Query
**Prompt2Query** is a desktop application built using `tkinter` that enables users to input natural language prompts, generate corresponding code, and execute it using the `llm_pandas` library. This tool is specifically designed for interacting with data using Python code, providing an intuitive interface to query datasets with natural language.
## Flow chart
```mermaid
%%{init: {'theme': 'black', 'themeVariables': { 'fontSize': '16px'}, "securityLevel": "loose"}}%%
graph TD
A[User Input] --> B{Mode Selection}
B -->|Pandas| C[Pandas Mode]
C --> C1[LLMHandler]
C1 --> C2[Generate Code]
C2 --> C3[Execute Code]
C3 --> C4[Generate Natural Language]
C4 --> R[Display Results]
B -->|SQL| D[SQL Mode]
D --> D1[LLMMySQLHandler]
D1 --> D2[Generate SQL]
D2 --> D3[Execute SQL]
D3 --> D4[Format Results]
D4 --> R
B -->|REPandas| E[Recursive Mode]
E --> E1[Generate Plan]
E1 --> E2[Execute Phases]
E2 --> E3{Phase Complete?}
E3 -->|No| E2
E3 -->|Yes| R
B -->|Default| F[Default Mode]
F --> F1[LLM Processing]
F1 --> R
G[File Operations] --> G1{Action Type}
G1 -->|Load| G2[Load CSV]
G1 -->|Save| G3[Save Results]
G2 --> C1
G3 --> H[File System]
I[History Management] --> I1[Track Selected Items]
I1 --> I2[Update Context]
I2 --> C1
I2 --> D1
```
## Features
- **Natural Language to Code**: Convert user queries written in natural language to executable code using `llm_pandas`.
- **Multiple Modes**: Currently supports both Pandas and SQL modes for generating queries.
- **File Attachment**: Load CSV files to query data.
- **Code Execution**: Execute the generated code directly within the application.
- **Scrollable Result Logs**: View query results and logs in a clean, organized, scrollable interface.
- **Interactive Interface**: Buttons and dropdowns for a seamless user experience.
- **Query History**: Track and review previous queries and their corresponding code and results.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/Abhigyan126/Prompt2query.git
```
2. Navigate into the project directory:
```bash
cd prompt2query
```
3. Install the required dependencies:
```bash
pip install tkinter google-generativeai
```
4. Run the application:
```bash
python Begin.py
```
## Usage
- **Load a Dataset**: Use the "Attachment" button to upload a CSV file.
- **Enter Query**: Type your query in the input box at the bottom right of the window.
- **Execute**: Choose between Pandas or SQL mode from the dropdown, and click the "Execute" button to generate and run the corresponding code.
- **View Results**: The generated code and results will appear in the respective panes on the left-hand side.
- **Log**: All executed queries and results are logged for easy reference.
## Dependencies
- `tkinter`: For the graphical user interface.
- `llm_pandas`: To handle natural language queries and generate code.
- `datetime`: For timestamping query logs.
## Project Structure
```bash
.
├── Icons # Folder containing application icons
├── Begin.py # Main application script
├── llm_pandas # Library to generate code
├── llm.py # Library to comunicate with LLM here in this cose gemini by google
└── README.md # Project documentation