README.md

# Responsible-AI-Moderation Model 

## Table of Contents

- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Set Configuration Variables](#set-configuration-variables)
- [Models Required](#models-required)
- [Running the Application](#running-the-application)
- [Docker Image](#Docker-Image) 
- [License](#license)
- [Contact](#contact)
  
## Introduction
The **Moderation Model** module acts as a central hub for machine learning models for prompt injection, toxicity, jailbreak, restricted topic, custom theme and refusal checks. It provides the endpoints to utilize the response generated by these models.

## Features
The **Moderation Model** module acts as a wrapper for the traditional AI models we are using for various checks like prompt injection, jailbreak, toxicity etc. 

## Installation
To run the application, first we need to install Python and the necessary packages:

1. Install Python (version 3.11.x) from the [official website](https://www.python.org/downloads/) and ensure it is added to your system PATH.

2. Clone the repository : responsible-ai-mm-flask:
    ```sh
    git clone <repository-url>
    ```

3. Navigate to the `responsible-ai-mm-flask` directory:
    ```sh
    cd responsible-ai-mm-flask
    ```

4. Create a virtual environment:
    ```sh
    python -m venv venv
    ```

5. Activate the virtual environment:
    - On Windows:
        ```sh
        .\venv\Scripts\activate
         ```

6. Go to the `requirements` directory where the `requirement.txt` file is present.
    In the `requirement.txt` file comment the 
    ```sh
    lib/torch-2.2.0+cu118-cp39-cp39-linux_x86_64.whl  
    ```
    **Note:** Download appropriate torch version supporting python version which is installed [i.e if Python version is 3.10 use torch-2.2.0+cu118-**cp310**-**cp310**-**linux**_x86_64.whl, where cp310 denotes python version 3.10 and linux denotes OS which can be linux/win and **_not applicable for Mac_**]
   
    **Note:** If working in windows as this is for linux and replace 
    ```sh
    lib/
    ```
    with 
    ```sh
    ../lib/
    ```
  **Note:** If working in Mac Os, run the below command after running requirement.txt
     ```sh
   pip install --pre torch torchvision torchaudio \--extra-index-url https://download.pytorch.org/whl/nightly/cpu
    ```
  
  Download and place the en_core_web_lg-3.5.0-py3-none-any.whl inside the lib folder.
    [en_core_web_lg](https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.5.0/en_core_web_lg-3.5.0-py3-none-any.whl) and install the requirements:
    
    ```sh
    pip install -r requirement.txt
    ```
    
  **Note:** when running requirement.txt, if getting error related to "cuda-python" then comment cuda-python from 
          requirement.txt file and run pip install again
    Install the fastapi library as well, use the following command:
    ```sh
    pip install fastapi
    ```
## Set Configuration Variables
After installing all the required packages, configure the variables necessary to run the APIs.

1. Navigate to the `src` directory:
    ```sh
    cd ..
    ```

2. Locate the `.env` file, which contains keys like the following:

  ```sh
  workers=1
  WORKERS="${workers}"
  # DB_NAME="${dbname}"
  # DB_USERNAME="${username}"
  # DB_PWD="${password}"
  # DB_IP="${ipaddress}"
  # DB_PORT="${port}"
  # MONGO_PATH="mongodb://${DB_USERNAME}:${DB_PWD}@${DB_IP}:${DB_PORT}/"
  # MONGO_PATH= "mongodb://localhost:27017/"
  ```

3. Replace the placeholders with your actual values.

## Models Required
The following models are required to run the application. Download all the model files from the links provided, and place it in the folder name provided.

1. [Prompt Injection](https://huggingface.co/deepset/deberta-v3-base-injection/tree/main)
Files required to download here are : model.safetensors, config.json, tokenizer_config.json, tokenizer.json, special_tokens_map.json.
Name the folder as 'dbertaInjection'.

2. [Restricted Topic](https://huggingface.co/MoritzLaurer/deberta-v3-base-zeroshot-v2.0/tree/main)
Files required to download here are : model.safetensors, added_tokens.json, config.json, special_tokens_map.json, spm.model, tokenizer.json, tokenizer_config.json.
Name the folder as 'restricted-dberta-base-zeroshot-v2'.

3. [Sentence Transformer Model](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1/tree/main)
Files required to download here are : 1_Pooling folder, pytorch_model.bin, vocab.txt, tokenizer.json, tokenizer_config.json, special_tokens_map.json, sentence_bert_config.json, modules.json, config.json, config_sentence_transformers.json.
Name the folder as 'multi-qa-mpnet-base-dot-v1'.

4. [Detoxify](https://huggingface.co/FacebookAI/roberta-base/tree/main)
Files required to download here are : vocab.json, tokenizer.json, merges.txt, config.json.
Now download the model checkpoint file from this url and keep it under this folder -
[toxic_model_ckpt_file](https://github.com/unitaryai/detoxify/releases/download/v0.3-alpha/toxic_debiased-c7548aa0.ckpt)
Name the folder as 'detoxify'.

5. [Gibberish](https://huggingface.co/madhurjindal/autonlp-Gibberish-Detector-492513457)
Files required to download here are : vocab.json, tokenizer.json,config.json,pytorch_model.bin, tokenizer_config.json,special_tokens_map.json.
Name the folder as 'gibberish'.

6. [Bancode](https://huggingface.co/vishnun/codenlbert-tiny)
Files required to download here are : vocab.txt, tokenizer.json,config.json,pytorch_model.bin, tokenizer_config.json,special_tokens_map.json.
Name the folder as 'bancode'.

7. [Restricted Topic](https://huggingface.co/cross-encoder/nli-MiniLM2-L6-H768)
Files required to download here are : vocab.json, tokenizer.json,config.json,merges.txt,pytorch_model.bin, tokenizer_config.json,special_tokens_map.json.
Name the folder as 'nli-MiniLM2-L6-H768'.

Place the above folders in a folder named 'models' in the following way: 'responsible-ai-mm-flask/models'.

## Running the Application
Once we have completed all the aforementioned steps, we can start the service.

1. Navigate to the `src` directory:

2. Run `main_MM.py` file:
    ```sh
    python main_MM.py
     ```

3. PORT_NO : Use the Port No that is configured in .env file.

   Open the following URL in your browser:
  `http://localhost:8000/rai/v1/raimoderationmodels/docs`

**Note:** :
1. To address the issue where the Passport Number is not recognized in Privacy, modify the "piiEntitiesToBeRedacted" field in the privacy() under service.py file (line no: 98) from None to an empty list []. This adjustment ensures that the Passport Number is correctly identified.

2. Do not use this Moderation Model repository as a standalone repository. It serves as the base or dependency for the Moderation Layer repository, which provides the 'Guardrail' functionality, so access this repository APIs through Moderation layer.

## Docker Image
The Docker image for the ModerationModel module has been published on Docker Hub. You can access it here: [ModerationModel image](https://hub.docker.com/repository/docker/infosysresponsibleaitoolkit/responsible-ai-moderationmodel)

## License
The source code for the project is licensed under the MIT license, which you can find in the [LICENSE.txt](LICENSE.txt) file.

## Contact
If you have more questions or need further insights please feel free to connect with us @ Infosysraitoolkit@infosys.com