semvec/pypi-packages huggingface dataset:
grid-research 0.1.5
A package for automated research data collection using LLMs and Web Search API
Requires Python: >=3.8

# AI Research CLI Tool

## Overview

The AI Research CLI Tool is a command-line interface designed to assist users in generating structured, tabular data for research purposes. By leveraging the Bing Web Search API and the GPT-4o model, this tool transforms raw research topics into organized data that can be easily exported and utilized.

## Features

- **AI-Suggested Column Headers**: Automatically generate relevant column headers and descriptions for your research data.
- **Custom Header Editing**: Modify or create your own headers to suit your specific research needs.
- **Data Generation**: Generate between 1 to 100 rows of structured research data based on your queries.
- **Beautiful Terminal Interface**: Enjoy a visually appealing and user-friendly command-line interface.
- **Export-Ready Data**: Easily export the generated data in a structured format for further analysis.

## Installation

To install the necessary dependencies, you can use the following command:
```bash
pip install -r requirements.txt
```

## Usage

1. **Start the Application**: Run the CLI tool from your terminal.
   ```bash
   python grid/cli.py
   ```

2. **Input Research Topic**: When prompted, enter the topic you would like to research.

3. **Choose Header Creation Method**:
   - **Get AI Suggestions**: Use the AI to generate suggested headers.
   - **Create Manually**: Input your own headers.

4. **Specify Number of Rows**: Indicate how many rows of data you wish to generate.

5. **Data Generation**: The tool will perform a web search using the Bing API and generate structured data based on the retrieved information.

6. **Export Data**: The generated data will be returned in a pandas.DataFrame, ready for your use.

## Requirements

- Python 3.8 or higher
- OpenAI API Key
- Bing Search API Key

## Environment Variables

Make sure to set the following environment variables in your `.env` file:

```
OPENAI_API_KEY=your_openai_api_key
BING_SEARCH_V7_SUBSCRIPTION_KEY=your_bing_search_api_key
```

## Contributing

Contributions are welcome! If you have suggestions for improvements or new features, feel free to open an issue or submit a pull request.

## License

This project is licensed under the MIT License. See the LICENSE file for more details.

## Acknowledgments

- [OpenAI](https://openai.com) for providing the models.
- [Bing Web Search API](https://www.microsoft.com/en-us/bing/apis/bing-search-api-v7) for enabling web search capabilities.
red-wine-model-matthew9655 0.0.2
A red wine classification model
Requires Python: >=3.10

# Productionized Titanic Classification Model Package

## Run With Tox (Recommended)
- Download the data from: https://www.openml.org/data/get_csv/16826755/phpMYEkMl
- Save the file as `raw.csv` in the classification_model/datasets directory
- `pip install tox`
- Make sure you are in the assignment-section-05 directory (where the tox.ini file is) then run the command: `tox` (this runs the tests and typechecks, trains the model under the hood). The first time you run this it creates a virtual env and installs
dependencies, so takes a few minutes.

## Run Without Tox
- Download the data from: https://www.openml.org/data/get_csv/16826755/phpMYEkMl
- Save the file as `raw.csv` in the classification_model/datasets directory
- Add assignment-section-05 *and* classification_model paths to your system PYTHONPATH
- `pip install -r requirements/test_requirements`
- Train the model: `python classification_model/train_pipeline.py`
- Run the tests `pytest tests`