semvec/pypi-packages huggingface dataset:
prompt-galary 0.1.11
a prompt galary for llm
License: MIT
Requires Python: >=3.9

---
title: ATS Scoring App
emoji: 📝
colorFrom: green
colorTo: blue
sdk: streamlit
sdk_version: "1.38.0"
app_file: app.py
pinned: false
---

# ATS Scoring App

Welcome to the ATS Scoring App. This app evaluates resumes based on their alignment with a job description (JD) using various scoring criteria.

## Features

- **Keyword-Based Similarity:** Measures how well the resume matches the job description based on keywords.
- **Positive/Negative Words:** Assesses the impact of positive and negative words in the resume.
- **Resume Format:** Checks the resume formatting for compliance with best practices.
- **Content Analysis:** Detects images, tables, graphs, and charts in the resume.
- **JD Presence:** Identifies if the job description is included in the resume.
- **Word Matrix Calculation:** Analyzes word matrices using the XYZ formula.
- **Cosine Similarity:** Computes similarity scores using cosine similarity.
- **Experience Match:** Compares the user's experience with the job description requirements.
- **Education Match:** Evaluates the educational qualifications listed in the resume.
- **Resume Parsability:** Determines if the resume is in a format that can be parsed effectively.

## How to Use

1. **Access the App:**
   Visit the [Hugging Face Spaces page for ATS Scoring App](https://huggingface.co/spaces/ijtelgote/ATScanner) to interact with the application.

2. **Upload Your Data:**
   - Upload the job description (JD) and resume files through the provided interface.

3. **Get Scores:**
   - The application will process the inputs and provide scores based on the evaluation criteria.
   
## Configuration

The app allows for customization through the `config.json` file. Here’s how you can configure it:

## Features in Development

- **Customizable Parameters:** Future updates may include options to adjust scoring parameters and keyword lists.
- **Advanced Analytics:** Plans to add more detailed analytics and reports.

## License

This project is licensed under the MIT License.

## Contact

For questions or support, please reach out to [ijaytelgote@gmail.com](mailto:ijaytelgote@gmail.com).
qageneratorllm 0.1.3a0
A Python package for generating educational questions and answers using various LLM providers.
License: MIT
Requires Python: <4.0,>=3.10
Keywords: ai, education, llm, mcq, questions, quiz

# QAGeneratorLLM

A Python package for generating educational questions and answers using various LLM providers.

## Features

- Support for multiple LLM providers (Anthropic, Ollama, OpenAI, XAI)
- Generate both Multiple Choice Questions (MCQ) and Open-Ended Questions
- Choose between structured dataclass or raw JSON output formats
- Batch processing support
- File-based context input

## Installation

```bash
pip install qageneratorllm
```

## Usage

```python
from qageneratorllm import QuestionGenerator, LLMProviderType, QuestionType, OutputType

# Initialize with default settings (Ollama + Open-Ended Questions)
generator = QuestionGenerator()

# Generate open-ended questions from text
result = generator.invoke("Your context text here")

# Generate Multiple Choice Questions using OpenAI
generator = QuestionGenerator(
    provider_type=LLMProviderType.OPENAI,
    question_type=QuestionType.MCQ
)
result = generator.invoke("Your context text here")

# Generate from file
result = generator.invoke_from_file("path/to/your/file.txt")

# Generate with JSON output instead of dataclass
generator = QuestionGenerator(
    question_type=QuestionType.MCQ,
    output_type=OutputType.JSON
)
result = generator.invoke("Your context text here")
# result will be a dictionary instead of a Pydantic model
```

## Command Line Usage

```bash
# Basic usage
python -m qageneratorllm.generator --input input.txt --output questions.json

# Generate MCQs in batch mode from a folder of text files
python -m qageneratorllm.generator --input texts_folder/ --output results/ --batch --questions 5 --output-type json
```

## Gradio Interface

The package includes a Gradio web interface for interactive question generation:

```bash
# Launch the Gradio app
python -m qageneratorllm.gradio_app
```

With the Gradio interface, you can:
- Upload documents (.txt, .md, .pdf)
- View document chunks separated by headers
- Filter chunks by header level
- Select specific chunks for question generation
- Choose question type (MCQ or open-ended)
- Select LLM provider
- Generate questions interactively

## Environment Variables

- `ANTHROPIC_MODEL_NAME`: Anthropic model name (default: claude-3-sonnet-20240229)
- `OLLAMA_MODEL_NAME`: Ollama model name (default: qwen2.5)
- `OPENAI_MODEL_NAME`: OpenAI model name (default: gpt-4o)
- `XAI_MODEL_NAME`: XAI model name (default: grok-beta)

## License

MIT