Automated-Meeting-Analysis-Systems

AMAS 360

AMAS 360 (Automated Meeting Analysis System 360) is a comprehensive application designed to streamline meeting analysis and HR insights. The system integrates two major components:

• Chatbot: An AI-powered conversational interface (using an LLM such as Groq or similar) that helps employees, managers, and HR interactively query meeting transcripts and related data.
• HR Dashboard: An interactive dashboard that displays comprehensive meeting analytics, user participation, and transcript performance through graphical visualizations and detailed summaries.

AMAS 360 is built using Flask for the backend, SQLAlchemy for database interactions (with a PostgreSQL database based on the AMAS schema), and Matplotlib for generating visual insights.

Table of Contents

• Features
• Architecture
• Project Structure
• Installation
• Configuration
• Usage
• Troubleshooting
• Future Enhancements
• Contributing
• License

Features

• Multi-Role Chatbot:

  • Three separate chatbot endpoints for Employees, Managers, and HR.
  • Role-specific responses powered by a large language model (LLM) to provide relevant insights.
  • Employee chatbot restricts data access so that users only see their own data.

• HR Dashboard:

  • Displays overall summary metrics: total users, meetings, transcript counts, and average transcript durations.
  • Visualizes meeting participation per user and transcript trends through bar and line charts.
  • Provides user-specific analysis: HR can select an individual to view detailed performance and meeting data.

• Data Integration:

  • Uses a PostgreSQL database with an AMAS schema that includes tables for users, meetings, meeting participants, and meeting transcripts.
  • SQLAlchemy ORM facilitates smooth database interaction.

• Visualization:

  • Generates charts using Matplotlib (with a non-interactive Agg backend) and embeds them in the dashboard.
  • Dynamic plot generation based on live data queries.

Architecture

AMAS 360 is built on a modular architecture:

• Backend:

  • Flask: Serves as the web framework, handling routes for the chatbot and dashboard.
  • SQLAlchemy: Manages ORM mapping and database sessions.
  • LLM Integration: A module integrates with Groq LLM (or a similar service) to generate chatbot responses.

• Frontend:

  • Jinja2 Templates: Render dynamic HTML pages for the HR dashboard and chatbot interfaces.
  • Static Assets: Charts are generated and stored in the static folder and then displayed on the dashboard.

• Data Storage:

  • A PostgreSQL database based on the provided AMAS schema.
  • Tables include users, meetings, meeting_participants, and meeting_transcripts.

Project Structure

AMAS_dashboard/
├── app.py                # Main Flask application with routes for chatbot and HR dashboard
├── models.py             # SQLAlchemy models matching the AMAS schema
├── analysis.py           # Functions for computing analytics and generating plots
├── requirements.txt      # List of project dependencies
├── README.md             # This documentation file
├── static/
│   └── plots/            # Generated plot images are saved here
└── templates/
    ├── hr_dashboard.html # HR dashboard landing and user-specific analysis pages
    └── user_analysis.html # (Optional) Separate template for detailed user analysis

Installation

Prerequisites

• Python (>= 3.8)
• PostgreSQL
• Git (optional, for cloning the repository)

Steps

1. Clone the Repository

   git clone https://github.com/Dadiya-Harsh-wappnet/Automated-Meeting-Analysis-Systems.git
   cd AMAS-360

2. Create a Virtual Environment (Recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

4. Set Up the PostgreSQL Database

   • Create a database (for example, meeting_analysis):

   CREATE DATABASE meeting_analysis;

   • Update the db_url in app.py (and optionally in models.py) with your PostgreSQL credentials:

   db_url = "postgresql+psycopg2://your_user:your_password@localhost/meeting_analysis"

Configuration

• Database URL: Edit the db_url variable in app.py to match your PostgreSQL settings.

• Matplotlib Backend: Ensure that analysis.py uses the Agg backend to prevent GUI errors:

  import matplotlib
  matplotlib.use("Agg")

• LLM Integration (Chatbot): If you plan to use an LLM (such as Groq), configure the API key and endpoints in your chatbot module accordingly.

Usage

Running the Application

1. Start the Flask Server

   python app.py

   You should see output similar to:

   * Running on http://127.0.0.1:5000
   

2. Access the HR Dashboard Open your browser and go to:

   http://127.0.0.1:5000/hr/dashboard
   

   The dashboard will display overall summary metrics and visualizations.

3. User-Specific Analysis From the dashboard, select a user from the dropdown list to view user-specific charts and details.

4. Chatbot Interaction Access chatbot endpoints for different roles to query meeting data:

   • Employee: /chatbot/employee
   • Manager: /chatbot/manager
   • HR: /chatbot/hr

Troubleshooting

• Template Not Found Error: Ensure that the templates/ directory exists at the same level as app.py and that all template file names match exactly (case-sensitive).

• Matplotlib GUI Warning: If you see warnings about starting a Matplotlib GUI outside of the main thread, confirm that the Agg backend is set at the top of analysis.py:

  import matplotlib
  matplotlib.use("Agg")

• Database Connection Issues: Verify your DATABASE_URL settings and ensure your PostgreSQL server is running.

• 404 on Favicon: You may ignore the favicon 404 error or add a favicon file in the static folder.

Future Enhancements

• Advanced Chatbot NLP: Integrate a state-of-the-art LLM (e.g., Groq, GPT) for dynamic, context-aware responses.

• Interactive Visualizations: Use libraries like Plotly or Chart.js for interactive dashboards.

• User Authentication & Authorization: Implement secure login and restrict data access based on user roles.

• Real-Time Data Processing: Add live updates and real-time analytics as new meeting data is ingested.

• Enhanced Analytics: Expand performance metrics, add sentiment analysis, topic modeling, and deeper statistical insights.

Contributing

Contributions are welcome! Please fork this repository and create a pull request with your changes. For major changes, please open an issue first to discuss what you would like to change.

License

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