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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.