ArchCoder/Hostel-Management-and-Greivance-Redressal-System

Hostel Grievance Redressal System

Overview

The Hostel Grievance Redressal System is designed to efficiently manage and resolve grievances raised by residents. By leveraging AI/ML functionalities, the system aims to enhance communication, streamline grievance handling, and provide timely resolutions. This document outlines the implementation plans for various AI/ML features, system architecture, and usage instructions.

Table of Contents

1. 1.System Architecture Overview
2. 2.AI/ML Functionalities
3. 3.1. Intelligent Routing and Workflow Automation
4. 4.2. Advanced Sentiment and Emotional Intelligence Analysis
5. 5.3. Multilingual Translation in Chatroom
6. 6.4. Worker Job Recommendation
7. 7.Directory Structure
8. 8.Conclusion
9. 9.License
10. 10.Contact

System Architecture Overview

The Hostel Grievance Redressal System is built as a centralized Flask API server that hosts all AI/ML models. This architecture allows different services and applications to interact with the models by sending HTTP requests containing input data and receiving model predictions in response. Each AI/ML functionality is exposed through distinct endpoints, enabling modularity and scalability.

Key Components

1. 1.Flask API Server
2. 2.Central hub for all AI/ML models.
3. 3.RESTful API design for standardized interactions.
4. 4.Authentication and authorization mechanisms.

1. 1.Model Endpoints
2. 2./api/intelligent-routing - Endpoint for intelligent routing and workflow automation.
3. 3./api/sentiment-analysis - Endpoint for advanced sentiment and emotional intelligence analysis.
4. 4./api/multilingual-translation - Endpoint for multilingual translation in chatroom.
5. 5./api/job-recommendation - Endpoint for worker job recommendation.

1. 1.Data Handling and Validation
2. 2.Input validation using libraries like pydantic or marshmallow.

1. 1.Scalability and Deployment
2. 2.Docker for containerization.

AI/ML Functionalities

1. Intelligent Routing and Workflow Automation

Purpose: Efficiently assign grievances to the most suitable personnel or department based on various factors.

Model Design Pipeline:

• —Data Collection: Grievance data, staff data, historical assignments.
• —Data Preprocessing: Cleaning, feature engineering, encoding.
• —Model Selection: Reinforcement Learning (RL) and Multi-Criteria Decision-Making (MCDM).
• —Training and Evaluation: Define environment, implement reward functions, and evaluate using metrics like resolution time.

API Endpoint: https://archcoder-hostel-management-and-greivance-redres-2eeefad.hf.space/api/intelligent-routing

Example Input:

{
  "grievance_id": "G12346",
  "category": "electricity",
  "submission_timestamp": "2023-10-02T08:15:00Z",
  "student_room_no": "204",
  "hostel_name": "bh2",
  "floor_number": 2,
  "current_staff_status": [
    {
      "staff_id": "S67890",
      "department": "electricity",
      "current_workload": 3,
      "availability_status": "Available",
      "past_resolution_rate": 0.95
    },
    {
      "staff_id": "S67891",
      "department": "plumber",
      "current_workload": 2,
      "availability_status": "Available",
      "past_resolution_rate": 0.90
    }
  ],
  "floor_metrics": {
    "number_of_requests": 15,
    "total_delays": 1
  },
  "availability_data": {
    "staff_availability": [
      {
        "staff_id": "S67890",
        "time_slot": "08:00-12:00",
        "availability_status": "Available"
      }
    ],
    "student_availability": [
      {
        "student_id": "STU204",
        "time_slot": "08:00-10:00",
        "availability_status": "Unavailable"
      }
    ]
  }
}

Example Output:

{
  "job_id": "J12346",
  "assigned_worker_id": "W67890",
  "assignment_timestamp": "2023-10-02T08:16:00Z",
  "expected_resolution_time": "1 hour",
  "location": {
  "grievance_id": "G12346",
  "assigned_staff_id": "S67890",
  ...
}

2. Advanced Sentiment and Emotional Intelligence Analysis

Purpose: Detect complex emotional states in grievances to enhance responses from administrators.

Model Design Pipeline:

• —Data Collection: Grievance texts and emotional labels.
• —Data Preprocessing: Text cleaning, tokenization, and normalization.
• —Model Selection: Transformer-based models like BERT.

API Endpoint: https://archcoder-hostel-management-and-greivance-redres-2eeefad.hf.space/api/sentiment-analysis

Example Input:

{
  "grievance_id": "G12349",
  "text": "Why hasn't the maintenance team fixed the leaking roof yet?"
}

Example Output:

{
  "grievance_id": "G12349",
  "predicted_emotional_label": "Anger",
  ...
}

3. Multilingual Translation in Chatroom

Purpose: Facilitate communication between residents and workers who speak different languages.

Model Design Pipeline:

• —Data Collection: Multilingual conversation logs and translation pairs.
• —Data Preprocessing: Cleaning, tokenization, and alignment.
• —Model Selection: Neural Machine Translation (NMT) models.

API Endpoint: https://archcoder-hostel-management-and-greivance-redres-2eeefad.hf.space/api/multilingual-translation

Example Input:

{
  "user_message": "toilet me paani nahi aa rha hain",
  "source_language": "Hindi",
  "target_language": "English"
}

Example Output:

{
  "translated_message": "There is no water coming in the toilet."
}

4. Worker Job Recommendation

Purpose: Optimize job assignments to workers based on various factors.

Model Design Pipeline:

• —Data Collection: Job requests, worker profiles, historical assignments.
• —Data Preprocessing: Cleaning, feature engineering, encoding.
• —Model Selection: Collaborative Filtering and Decision Trees.

API Endpoint: https://archcoder-hostel-management-and-greivance-redres-2eeefad.hf.space/api/job-recommendation

Example Input:

{
  "job_id": "J12346",
  "type": "Electrical",
  "description": "Fan not working in room 204.",
  "urgency_level": "High",
  "submission_timestamp": "2023-10-02T08:15:00Z",
  "hostel_name": "Hostel A",
  "floor_number": 2,
  "room_number": "204"
}

Example Output:

{
  "job_id": "J12346",
  "assigned_worker_id": "W67890",
  "current_timestamp": "2023-10-02T08:30:00Z",
  "expected_resolution_time": "2023-10-02T10:00:00Z",
  "location": {
    "hostel_name": "Hostel A",
    "floor_number": 2,
    "room_number": "210"
  }
}

Directory Structure

📁 config
  📄 __init__.py
  📄 config.py
📁 docs
  📄 README.md
  📄 ai_plan.md
  📄 data_plan.md
  📄 plan.md
📁 models
  📁 intelligent_routing
    📁 saved_model
      📄 model.keras
    📁 test_data
      📄 __init__.py
      📄 test_data.json
    📁 test_results
      📄 confusion_matrix.png
      📄 roc_curve.png
      📄 test_report.json
    📁 train_data
      📄 __init__.py
      📄 training_data.json
    📄 generate_data.py
    📄 model.py
    📄 test_model.py
    📄 train.py
  📁 job_recommendation
    📁 saved_model
      📄 model.keras
    📁 test_data
      📄 __init__.py
      📄 test_data.json
    📁 test_results
      📄 test_report.json
    📁 train_data
      📄 __init__.py
      📄 training_data.json
    📄 generate_data.py
    📄 model.py
    📄 test.py
    📄 train.py
  📁 multilingual_translation
    📁 test_data
      📄 __init__.py
      📄 test_data.json
    📁 test_results
      📄 test_report.json
    📁 train_data
      📄 __init__.py
      📄 training_data.json
    📄 model.py
    📄 test_model.py
  📁 sentiment_analysis
    📁 test_data
      📄 __init__.py
      📄 test_data.json
    📁 test_results
      📄 test_report.json
    📁 train_data
      📄 __init__.py
      📄 training_data.json
    📄 model.py
    📄 test_model.py
📁 test_results
  📄 endpoint_test_results.json
📁 utils
  📄 __init__.py
  📄 logger.py
📄 .env
📄 .gitignore
📄 app.py
📄 readme.md
📄 requirements.txt
📄 routes.py
📄 test_endpoints.py

To test the application, you can use the test_endpoints.py script, which provides a convenient way to verify the functionality of the API endpoints.

Conclusion

Implementing these AI/ML functionalities will significantly enhance the efficiency and effectiveness of the Hostel Grievance Redressal System. By leveraging advanced technologies and integrating them within a Flask API framework, the system will provide a more responsive, empathetic, and proactive approach to managing resident grievances.

License

This project is licensed under the MIT License.

Contact

For any questions or feedback, please contact imt_2022089@iiitm.ac.in.