AI-Powered Content Moderation System
Overview
The AI-Powered Content Moderation System is designed to analyze user-generated content and identify potentially discriminatory, biased, or unethical language. The system leverages OpenAI's large language models (LLM) to provide an in-depth analysis of text content, focusing on detecting and flagging various forms of bias, including racial, gender, religious, political, and general unethical behavior.

This system is ideal for use in any platform or application that needs to monitor and moderate user-generated content to ensure that it aligns with ethical standards and inclusivity. The system provides a severity rating, an explanation of flagged content, and suggestions for improving content to be more inclusive and respectful.

Why This Project?
The internet and digital spaces are increasingly becoming a hub for diverse conversations, making it essential to ensure that user-generated content is respectful, inclusive, and free from harmful biases. Many platforms, such as social media sites, forums, and content-sharing platforms, need automated solutions to help identify and mitigate hate speech, discrimination, and harmful language.

The goal of this project is to provide an easy-to-use, efficient, and effective solution for flagging harmful content, using state-of-the-art AI models to assess content in real time. This will allow organizations to better monitor and manage their communities and ensure safer, more inclusive environments.

Features
Bias Detection: Identifies instances of racial, gender, religious, political bias, and general unethical behavior.
Severity Rating: Provides a severity score from 1 to 10, where 1 represents minimal bias and 10 represents extreme bias.
Explanations and Suggestions: Offers detailed explanations for flagged content and provides suggested improvements for more inclusive language.
Multiple Input Formats: The system supports analyzing content from large text files, PDFs, CSV files, or even direct user input through copy-paste.
Excel Report: Generates a downloadable Excel report that lists flagged content along with its severity, category, explanation, and suggestions.
How It Works

1. Text Input: The system processes text input from various sources like a CSV file, JSON file, or direct user input.

2. AI Analysis: The OpenAI GPT-3 model is used to analyze the content and flag discriminatory or biased language.

3. Severity Rating: The system assigns a severity score based on the level of harmfulness in the content.

4. Explanations & Suggestions: For each flagged piece of content, a detailed explanation and suggestions for improving the text are provided.

5. Excel Report: After analysis, an Excel report is generated, providing a structured output with all the flagged content and corresponding information.

Installation
To use this system, you will need Python 3.x installed on your machine. You will also need to install the required dependencies.

Steps to Set Up
Clone the Repository:
git clone https://github.com/arthvijay/ai-content-moderation-system.git
cd ai-content-moderation-system
Set Up Virtual Environment (optional but recommended):
python -m venv venv
venv\Scripts\activate # On Windows
source venv/bin/activate # On macOS/Linux
Install Dependencies:
pip install -r requirements.txt
Set Up OpenAI API Key:
Create an OpenAI account at OpenAI Signup.

Retrieve your API key from OpenAI API Keys.

Replace the your_openai_api_key_here in the code with your actual OpenAI API key.

Usage
Once the system is set up, you can start using it to analyze content.

1. Analyzing Text Directly
   To analyze content directly, call the analyze_content() function with the text you want to analyze. For example:

text = "Example of content to analyze."
result = analyze_content(text)
print(result)
2. Processing a CSV File
The system supports CSV files where each row contains a piece of text to be analyzed. The function process_csv_input() will read the CSV file, analyze the content, and generate an Excel report.

process_csv_input('input_data.csv')
3. Processing a JSON File
If you have a JSON file containing text data, use the process_json_input() function to analyze the content.

process_json_input('input_data.json')
4. Paste Content Directly
If you want to paste text for analysis, simply call the analyze_content() function as shown in the direct text example above.

5. Generating Reports
   After the analysis, the system will generate an output_report.xlsx file containing the flagged content, severity scores, explanations, and suggested improvements.

Example Output
The output will be an Excel file where each row corresponds to a piece of flagged content, with the following columns:

Content: The original text.
Severity: The severity score of the content.
Category: The type of bias detected (e.g., racial, gender, political, etc.).
Explanation: A detailed explanation of why the content was flagged.
Suggestions: Suggested improvements to make the content more inclusive and respectful.
Contributing
If you would like to contribute to this project, feel free to fork the repository, make changes, and create a pull request. Any contributions to improve the accuracy or efficiency of the system are welcome!

License
This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments
OpenAI for providing access to their powerful language models.
Various libraries such as Pandas, OpenPyXL, and PDFPlumber that help handle data and generate reports.