semvec/pypi-packages huggingface dataset:
sft-data-generator 0.0.3
A tool for generating data for SFT using LLM
Requires Python: >=3.8

# SFT Data Generator

This is a tool for generating data for Supervised Fine-Tuning (SFT) using large language models like GPT-3.5. It can read data from CSV or Excel files and generate corresponding output data using OpenAI's API based on a given prompt.

## Features

- Supports reading CSV and Excel files
- Uses OpenAI's Chat Completion API to generate data
- Supports customizable prompts and models
- Supports batch generation with customizable batch size
- Supports setting the number of generation epochs, i.e., generating multiple rounds for the entire dataset
- Supports JSON format output
- Can set OpenAI API URL and key via command line arguments or environment variables

## Usage

1. Install dependencies:
   
   ```
   pip install -r requirements.txt
   ```

2. Prepare the input data file (CSV or Excel) and prompt template.

3. Run the command:
   ```bash
   sft-data-generator --file_path <input_file_path> --prompt <prompt_template> --model <model_name> [other optional arguments]
   ```
   or
   ```bash
   python data_generator.py --file_path <input_file_path> --prompt <prompt_template> --model <model_name> [other optional arguments]
   ```

   Required arguments:
   - `--file_path`: Path to the input data file, must be a CSV or Excel file.
   - `--prompt`: Prompt template used for generating data.
   - `--model`: Name of the OpenAI model used for generating data, e.g., `gpt-3.5-turbo`.

   Optional arguments:
   - `--output_file`: Output file path, defaults to input file path + `.output.jsonl`.
   - `--batch_size`: Batch size, i.e., the number of samples per concurrent request, defaults to 1.
   - `--generate_epoch`: Number of generation epochs, i.e., the number of rounds to generate for the entire dataset, defaults to 1.
   - `--openai_base_url`: Base URL for the OpenAI API, if not given, will use the environment variable `OPENAI_API_BASE`.
   - `--openai_api_key`: OpenAI API key, if not given, will use the environment variable `OPENAI_API_KEY`.
   - `--json_output`: Whether to use JSON format output, defaults to False.

4. The generated data will be saved in the specified output file, with each line being a JSON object.

## Example

```bash
sft-data-generator --file_path data.csv --prompt "Please generate a question-answer pair based on the given data:" --model gpt-3.5-turbo --batch_size 10 --generate_epoch 3
```
or
```bash
python data_generator.py --file_path data.csv --prompt "Please generate a question-answer pair based on the given data:" --model gpt-3.5-turbo --batch_size 10 --generate_epoch 3
```

This will read the `data.csv` file, use the prompt "Please generate a question-answer pair based on the given data:", call the `gpt-3.5-turbo` model with a batch size of 10, and generate 3 epochs of data. The output will be saved in the `data.csv.output.jsonl` file.
favapy 1.0.1
Infer Functional Associations using Variational Autoencoders on -Omics data.
Requires Python: >=3.7

# <img src="https://user-images.githubusercontent.com/81096946/177743627-2e6a7447-3fc1-48a8-a6bb-003a3ace223a.png" alt="fava" width="150"/> FAVA: Functional Associations using Variational Autoencoders


[![PyPI version](https://badge.fury.io/py/favapy.svg)](https://badge.fury.io/py/favapy)
[![Documentation Status](https://readthedocs.org/projects/fava/badge/?version=latest)](https://fava.readthedocs.io/en/latest/?badge=latest)
![example workflow](https://github.com/github/docs/actions/workflows/test.yml/badge.svg)

<!-- ![Fava](https://user-images.githubusercontent.com/81096946/177743627-2e6a7447-3fc1-48a8-a6bb-003a3ace223a.png) -->
FAVA is a method used to construct protein networks based on omics data such as single-cell RNA sequencing (scRNA-seq) and proteomics. Existing protein networks are often biased towards well-studied proteins, limiting their ability to reveal functions of understudied proteins. FAVA addresses this issue by leveraging omics data that are not influenced by literature bias.
Read the [documentation](https://fava.readthedocs.io/en/latest/).


![Screenshot 2023-08-17 at 10 14 20](https://github.com/mikelkou/fava/assets/81096946/416deeeb-ce89-4ed7-8e2a-6703616552ab)


## Data availability
[The Combined Network](https://doi.org/10.5281/zenodo.6803472)

## Installation:
```
pip install favapy
```

## favapy as Python library
Read the jupyter-notebook: [How_to_use_favapy_in_a_notebook](https://github.com/mikelkou/fava/blob/main/How_to_use_favapy_in_a_notebook.ipynb)

favapy supports both AnnData objects and count/abundance matrices.


## Command line interface
Run favapy from the command line as follows:
```
favapy <path-to-data-file> <path-to-save-output>
```

### Optional parameters:
```

-t Type of input data ('tsv' or 'csv'). Default value = 'tsv'.

-n The number of interactions in the output file (with both directions, proteinA-proteinB and proteinB-proteinA). Default value = 100000.

-cor Type of correlation method ('pearson' or 'spearman'). Default value = 'pearson'

-c The cut-off on the Correlation scores.The scores can range from 1 (high correlation) to -1 (high anti-correlation). This option overwrites the number of interactions. Default value = None.

-d The dimensions of the intermediate\hidden layer. Default value depends on the input size.

-l The dimensions of the latent space. Default value depends on the size of the hidden layer.

-e The number of epochs. Default value = 50.

-b The  batch size. Default value = 32.


```

If FAVA is useful for your research, consider citing [FAVA BiorXiv](https://doi.org/10.1101/2022.07.06.499022).

#### Other Relevant publications:
[The STRING database in 2023](https://doi.org/10.1093/nar/gkac1000).
banjara 0.1.0
A terminal based point of sale for Stripe
Requires Python: <4.0,>=3.12

# Banjara - TUI Point of Sale

Banjara (terminal) is a text based point of sale system designed for simplicity

## Thesis

## Technology

- Textualize
- Stripe
- Tortoise ORM

## Stripe examples

```python
stripe.PaymentIntent.create(
  currency="aud",
  payment_method_types=["card_present"],
  capture_method="manual",
  amount=1998,
)
```

```python
stripe.terminal.Reader.process_payment_intent(
  "tmr_F",
  payment_intent="pi_3O",
)
```

```python
stripe.terminal.Reader.set_reader_display(
  "tmr_Fal",
  type="cart",
  cart={
    "line_items": [
      {"description": "Caramel latte", "amount": 659, "quantity": 1},
      {"description": "Dozen donuts", "amount": 1239, "quantity": 1},
    ],
    "currency": "aud",
    "tax": 100,
    "total": 1998,
  },
)
```

## License