semvec/pypi-packages huggingface dataset:
npc-session 0.1.30
Basic tools for parsing subject, session, date and time associated with data from the Mindscope Neuropixels team, in the cloud.
License: MIT
Requires Python: >=3.9

# npc_session

**n**euro**p**ixels **c**loud **session**

Basic tools for parsing subject, session, date and time associated with data from the
Mindscope Neuropixels team, in the cloud.

[![PyPI](https://img.shields.io/pypi/v/npc-session.svg?label=PyPI&color=blue)](https://pypi.org/project/npc-session/)
[![Python version](https://img.shields.io/pypi/pyversions/npc-session)](https://pypi.org/project/npc-session/)

[![Coverage](https://img.shields.io/codecov/c/github/alleninstitute/npc_session?logo=codecov)](https://app.codecov.io/github/AllenInstitute/npc_session)
[![CI/CD](https://img.shields.io/github/actions/workflow/status/alleninstitute/npc_session/publish.yml?label=CI/CD&logo=github)](https://github.com/alleninstitute/npc_session/actions/workflows/publish.yml)
[![GitHub issues](https://img.shields.io/github/issues/alleninstitute/npc_session?logo=github)](https://github.com/alleninstitute/npc_session/issues)


## quickstart

```bash
pip install npc_session
```

Parse a normalized ID from a path or string:
```python
>>> from npc_session import SessionRecord;

>>> s = SessionRecord('//allen/programs/mindscope/workgroups/templeton/TTOC/2022-07-26_14-09-36_366122')
>>> s
'366122_2022-07-26'
>>> s.subject
366122
>>> s.date
'2022-07-26'
>>> s.date.year
2022

```
llmanalyst 0.0.1
Talk to your CSV data with your huggingface llm models
License: MIT
Requires Python: >=3.10

# Language Model Analyst

The Language Model Analyst is a Python package and Streamlit app that enables natural language generation and analysis using HuggingFace-based language models (LLMs) and OpenAI GPT-3. This package and app are designed to simplify and streamline interactions with these powerful language models.

## Usage

### Package: LLMAnalyst

The `LLMAnalyst` class allows you to interact with HuggingFace-based language models. Follow these steps to use the package:

1. Install the package:

    ```bash
    pip install git+https://github.com/eersnington/LLMAnalyst.git

    # For using GPTQ models
    pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/  
    ```

2. Import and create an instance of LLMAnalyst:

    ```python
    from LLMAnalyst import LLMAnalyst
    llm_analyst = LLMAnalyst("TheBloke/CodeLlama-13B-Instruct-GPTQ")
    ```

3. 
    ```python
    query = "How many number of rows are there?"
    df = pd.read_csv("data.csv")
    result = llm_analyst.conversational_chat(query, df)
    ```


The OpenAIGPTAnalyst class enables interaction with the OpenAI GPT-3 model. Follow these steps to use the package:


1. Import and create an instance of OpenAIGPTAnalyst:

    ```python
    from LLMAnalyst import OpenAIGPTAnalyst

    openai_analyst = OpenAIGPTAnalyst(
        api_key='YOUR_OPENAI_API_KEY'
    )
    ```

2. Communicate with GPT-3:

    ```python
    query = "Calculate the mean of monthly sold data."
    df = pd.read_csv("data.csv")
    result = openai_analyst.conversational_chat(query, your_dataframe)
    ```