🎫 AI-Powered Complaint Management System

An intelligent, enterprise-grade complaint management system powered by AI

Features • Demo • Installation • Usage • Configuration • Contributing

---

📋 Table of Contents

• Overview
• Features
• Architecture
• Technologies Used
• Installation
• Configuration
• Usage
• Project Structure
• API Integration
• Database Schema
• Screenshots
• Contributing
• License
• Contact

---

🌟 Overview

The AI-Powered Complaint Management System is a sophisticated web application designed to streamline customer support operations. Built with Streamlit and powered by Google's Gemini AI, this system intelligently handles customer queries, automatically categorizes complaints, assigns agents based on workload and skills, and provides real-time ticket tracking.

🎯 Key Highlights

• 🤖 AI-Powered Chatbot using Google Gemini 2.5 Pro
• 📊 Intelligent Ticket Routing with priority detection
• 👥 Smart Agent Assignment based on workload and skills
• 📧 Email Notifications for ticket status updates
• 🔒 Secure Authentication with password hashing
• 📈 Admin Dashboard for monitoring and management
• 💾 SQLite Database for data persistence
• 🔍 RAG (Retrieval-Augmented Generation) for FAQ matching

---

✨ Features

For Users

• 🗨️ Interactive Chatbot

  • Ask questions and get instant answers from FAQ knowledge base
  • Automatic ticket creation when AI cannot resolve the query
  • Natural language understanding powered by Gemini AI

• 🎫 Ticket Management

  • Real-time ticket tracking with unique IDs
  • View all your tickets in one place
  • Automatic categorization (Shipping, Refund, Login, Cancellation)
  • Priority assignment (High, Medium, Low)

• 📝 Feedback System

  • Provide feedback on resolved tickets
  • Help improve service quality

• 📚 FAQ Access

  • Browse frequently asked questions
  • Self-service support resources

For Administrators

• 📊 Comprehensive Dashboard

  • View all tickets across the system
  • Monitor ticket status and priority
  • Track agent workload distribution

• 👤 Agent Management

  • Assign/reassign tickets to agents
  • View agent workload and availability
  • Skill-based agent matching

• 🔄 Status Updates

  • Update ticket status (Pending, In Progress, Resolved, Closed)
  • Automatic email notifications to users
  • Bulk operations support

• 📈 Analytics

  • Export ticket history to CSV
  • Track resolution times
  • Monitor system performance

---

🏗️ Architecture

graph TD
    A[User Interface - Streamlit] --> B[Authentication Layer]
    B --> C{User Type}
    C -->|User| D[Chatbot Interface]
    C -->|Admin| E[Admin Dashboard]
    D --> F[Gemini AI Engine]
    F --> G{Can Answer?}
    G -->|Yes| H[Return Answer]
    G -->|No| I[Create Ticket]
    I --> J[SQLite Database]
    E --> J
    J --> K[Agent Assignment System]
    K --> L[Email Notification Service]
    L --> M[User]

Loading

---

🛠️ Technologies Used

Technology	Purpose
Python 3.x	Core programming language
Streamlit	Web application framework
Google Gemini AI	Natural language processing and RAG
SQLite	Database management
scikit-learn	TF-IDF vectorization
FAISS	Vector similarity search
pandas	Data manipulation and CSV export
smtplib	Email notification system
hashlib	Password encryption

---

🚀 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager
• Google Cloud account with Gemini API access
• Gmail account for email notifications (optional)

Step-by-Step Setup

1. Clone the Repository

   git clone https://github.com/MuditIsOP/ibm.git
   cd ibm

2. Create Virtual Environment (Recommended)

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

3. Install Dependencies

   pip install -r requirements.txt

4. Set Up Configuration Create a .streamlit/secrets.toml file in the project root:

   GOOGLE_API_KEY = "your-google-gemini-api-key"
   SENDER_EMAIL = "your-email@gmail.com"
   SENDER_EMAIL_PASSWORD = "your-app-password"

5. Run the Application

   streamlit run app.py

6. Access the Application

   • Open your browser and navigate to http://localhost:8501
   • Default admin credentials:
     • Email: admin@test.com
     • Password: admin123

---

⚙️ Configuration

Google Gemini API Setup

1. Visit Google AI Studio
2. Create a new API key
3. Add the key to your secrets.toml file

Email Notification Setup

1. Enable 2-Factor Authentication on your Gmail account
2. Generate an App Password
3. Add your email and app password to secrets.toml

Database Configuration

The system automatically creates and initializes the SQLite database (complaints.db) on first run with three tables:

• users: User authentication and profile information
• tickets: Support ticket records
• agents: Agent information and workload tracking

---

📖 Usage

For Users

1. Registration

   • Click "Register Here" on the login page
   • Enter your email and password
   • Confirm password and register

2. Login

   • Use your registered email and password
   • Access the user dashboard

3. Ask a Question

   • Type your query in the chatbot interface
   • Receive instant answers from the AI or get a ticket created

4. Track Tickets

   • View all your tickets in "My Tickets" section
   • Track specific tickets using Ticket ID
   • Provide feedback on resolved tickets

For Administrators

1. Login

   • Use admin credentials: admin@test.com / admin123
   • Access the admin dashboard

2. Manage Tickets

   • View all tickets in the system
   • Update ticket status
   • Assign/reassign tickets to agents

3. Monitor Performance

   • Check agent workload distribution
   • Export ticket data to CSV
   • Review system analytics

---

📁 Project Structure

ibm/
│
├── app.py                  # Main Streamlit application
├── main.py                 # Alternative entry point
├── requirements.txt        # Python dependencies
├── complaints.db          # SQLite database (auto-generated)
│
├── .streamlit/
│   └── secrets.toml       # API keys and credentials (not in repo)
│
└── README.md              # This file

---

🔌 API Integration

Gemini AI Integration

The system uses Google's Gemini 2.5 Pro model for:

1. Query Understanding: Analyzing user intent and sentiment
2. Priority Detection: Assigning urgency levels based on keywords and context
3. RAG Implementation: Matching queries with FAQ knowledge base
4. Automated Responses: Generating contextual answers

Example API Call

def ask_gemini(prompt):
    model = genai.GenerativeModel("gemini-2.5-pro")
    response = model.generate_content(prompt)
    return response.text

---

🗃️ Database Schema

Users Table

CREATE TABLE users (
    email TEXT PRIMARY KEY,
    password_hash BLOB NOT NULL,
    salt BLOB NOT NULL,
    verified BOOLEAN DEFAULT 0
)

Tickets Table

CREATE TABLE tickets (
    ticket_id TEXT PRIMARY KEY,
    query TEXT NOT NULL,
    status TEXT NOT NULL,
    category TEXT,
    priority TEXT,
    assigned_to TEXT,
    timestamp TEXT,
    user_email TEXT,
    feedback TEXT,
    FOREIGN KEY (user_email) REFERENCES users (email),
    FOREIGN KEY (assigned_to) REFERENCES agents (name)
)

Agents Table

CREATE TABLE agents (
    name TEXT PRIMARY KEY,
    category TEXT,
    workload INTEGER DEFAULT 0,
    available BOOLEAN DEFAULT 1,
    skills TEXT
)

---

📸 Screenshots

User Dashboard

Admin Panel

Chatbot Interface

---

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the Repository
2. Create a Feature Branch

   git checkout -b feature/AmazingFeature

3. Commit Your Changes

   git commit -m 'Add some AmazingFeature'

4. Push to the Branch

   git push origin feature/AmazingFeature

5. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guidelines
• Add comments for complex logic
• Update documentation for new features
• Test thoroughly before submitting PR

---

��� Known Issues & Future Enhancements

Current Limitations

• Email notifications require Gmail with app passwords
• Single admin account only
• Limited to English language queries

Planned Features

• Multi-language support
• Advanced analytics dashboard
• Mobile responsive design
• Integration with Slack/Teams
• Voice input support
• Sentiment analysis visualization
• Role-based access control (RBAC)
• API endpoint for external integrations

---

📄 License

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

---

📧 Contact

Mudit

• GitHub: @MuditIsOP
• Project Link: https://github.com/MuditIsOP/ibm

---

🙏 Acknowledgments

• Streamlit for the amazing web framework
• Google Gemini for powerful AI capabilities
• FAISS for efficient similarity search
• All contributors and users of this system

---

⭐ Star this repository if you find it helpful!

Made with ❤️ by Mudit