Candidate_Selection / Readme.md
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# SelectRight
## Overview
This project aims to rank candidates for a role by comparing their resumes and interview transcripts using a language model.
## Folder Structure
```
MLE_Trial_Task/
β”œβ”€β”€ data/
β”‚ └── candidates.csv (optional, can be uploaded via the app)
β”œβ”€β”€ core_services/
β”‚ └── bot9_ai/
β”‚ └── modules/
β”‚ └── LLM/
β”‚ └── OpenAi.py
β”œβ”€β”€ src/
β”‚ β”œβ”€β”€ __init__.py
β”‚ β”œβ”€β”€ data_preparation.py
β”‚ β”œβ”€β”€ model.py
β”‚ β”œβ”€β”€ evaluation.py
β”‚ β”œβ”€β”€ bias_analysis.py
β”‚ └── report_generation.py
β”œβ”€β”€ app.py
β”œβ”€β”€ requirements.txt
└── README.md
```
## Setup
1. Clone the repository.
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Run the Streamlit app:
```bash
streamlit run app.py
```
## Files
- `data/candidates.csv`: The dataset file (optional, can be uploaded via the app).
- `llmservice/OpenAi.py`: Contains the `OpenAi` class.
- `src/data_preparation.py`: Script for loading the dataset.
- `src/model.py`: Script for defining the model.
- `src/evaluation.py`: Script for evaluating the model.
- `src/bias_analysis.py`: Script for analyzing biases.
- `src/report_generation.py`: Script for generating the report.
- `app.py`: Streamlit app script.
- `requirements.txt`: List of dependencies.
- `README.md`: Project overview and setup instructions.