Feedback Management System 🎯

A sophisticated feedback analysis platform built with Python and Django, leveraging AI-powered insights to transform raw feedback data into actionable intelligence.

📊 System Architecture

High-Level Architecture

graph TB
    Client[Client Browser] -->|HTTPS| NX[Nginx Reverse Proxy]
    NX --> Web[Django Web Server]
    Web -->|REST API| AS[FastAPI Analysis Service]
    Web --> DB[(PostgreSQL DB)]
    AS -->|Vector Search| VS[(FAISS Vector Store)]
    AS -->|LLM Requests| LLM[Ollama LLM]
    AS -->|Async Queue| AQ[Async Task Queue]
    AQ -->|ThreadPoolExecutor| TP[Thread Pool]
    TP -->|Results| AS
    
    subgraph Docker Container Architecture
        subgraph Core Service
            Web
            DB
        end
        
        subgraph Analysis Service
            AS
            VS
            LLM
            AQ
            TP
        end
        
        subgraph Nginx Service
            NX
        end
    end

    style Client fill:#f9f,stroke:#333,stroke-width:2px
    style Core Service fill:#dfd,stroke:#333,stroke-width:2px
    style Analysis Service fill:#ddf,stroke:#333,stroke-width:2px

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Task Processing Architecture

graph LR
    FI[Feedback Input] --> PP[Preprocessor]
    PP --> AQ[Async Queue]
    AQ --> |Concurrent Tasks| TP[Thread Pool Executor]
    TP --> CA[Column Analysis]
    TP --> TA[Text Analysis]
    TP --> NA[Numerical Analysis]
    
    CA & TA & NA --> IS[Insight Storage]
    IS --> VG[Visualization Generator]
    IS --> RG[Report Generator]
    
    VG & RG --> UI[User Interface]

    style FI fill:#f9f,stroke:#333,stroke-width:2px
    style AQ fill:#dfd,stroke:#333,stroke-width:2px
    style IS fill:#ffd,stroke:#333,stroke-width:2px
    style UI fill:#ddf,stroke:#333,stroke-width:2px

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🚀 Features

Core Features

• Multi-User Support

  • Admin: Complete system control and analytics
  • Teachers: Class management and feedback analysis
  • Students: Feedback submission and tracking
  • Public: Access to public statistics

• AI-Powered Analysis

  • Sentiment analysis using LangChain
  • Pattern recognition in feedback
  • RAG-based contextual understanding
  • Automated insight generation

• High-Performance Architecture

  • FastAPI-powered analysis service
  • Async task processing
  • Concurrent analysis with ThreadPoolExecutor
  • Efficient FAISS vector search

Technical Features

• Containerized Architecture

  • Multi-container Docker setup
  • Nginx reverse proxy
  • Independent service scaling
  • Container health monitoring

• Normalized Database

  • 5NF compliant schema
  • Optimized query performance
  • Referential integrity
  • Transactional consistency

• Async Processing

  • Custom async task queue
  • Parallel column analysis
  • Configurable concurrency limits
  • Task status tracking

🛠️ Technology Stack

Backend

• Django 5.2 (Core Service)
• FastAPI 0.115.12 (Analysis Service)
• PostgreSQL (5NF Database)
• FAISS (Vector Storage)
• Ollama (LLM Integration)

Infrastructure

• Docker Compose
• Nginx
• Gunicorn (WSGI Server)
• Uvicorn (ASGI Server)

Tools & Libraries

• LangChain
• Pandas & NumPy
• ThreadPoolExecutor
• AsyncIO

🔧 Installation

Using Docker (Recommended)

1. Clone the repository:

git clone https://github.com/sathwikshetty33/FeedbackManagementSystem.git
cd FeedbackManagementSystem

2. Create environment files:

cp .env.example .env
# Edit .env with your configuration

3. Start services:

docker-compose up -d

Manual Installation

1. Install dependencies:

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate
pip install -r requirements.txt

2. Start core service:

cd core
python manage.py migrate
python manage.py runserver

3. Start analysis service:

cd analysis-service
uvicorn app:app --workers 4

📊 Async Task Processing

The system uses a custom async task processing architecture:

• Task Manager

  • Async queue for task management
  • Configurable concurrency limits
  • Task status tracking and monitoring
  • Error handling and recovery

• Parallel Processing

  • ThreadPoolExecutor for CPU-bound tasks
  • Concurrent column analysis
  • Efficient resource utilization
  • Automatic load balancing

• Task Types

  • Feedback Analysis (High Priority)
  • Text Processing (Medium Priority)
  • Report Generation (Low Priority)

💾 Database Schema

The database follows 5NF principles with these main entities:

• Users (Admin, Teachers, Students, Public)
• Feedback Events
• Responses
• Analysis Results
• System Configurations

Each entity is fully normalized to:

• Eliminate redundancy
• Ensure data integrity
• Optimize query performance
• Support complex relationships

🔐 Security Features

• Token-based authentication
• Role-based access control
• Rate limiting
• SQL injection protection
• XSS prevention

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

📧 Contact

Sathwik Shetty - @sathwikshetty33

Project Link: https://github.com/sathwikshetty33/FeedbackManagementSystem

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Last Updated: 2025-06-05 17:42:06 UTC