--- title: Hostel Management System emoji: 🏨 colorFrom: blue colorTo: green sdk: docker app_port: 7860 pinned: false --- # 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. [System Architecture Overview](#system-architecture-overview) 2. [AI/ML Functionalities](#aiml-functionalities) - [1. Intelligent Routing and Workflow Automation](#1-intelligent-routing-and-workflow-automation) - [2. Advanced Sentiment and Emotional Intelligence Analysis](#2-advanced-sentiment-and-emotional-intelligence-analysis) - [3. Multilingual Translation in Chatroom](#3-multilingual-translation-in-chatroom) - [4. Worker Job Recommendation](#4-worker-job-recommendation) 3. [Directory Structure](#directory-structure) 4. [Conclusion](#conclusion) 5. [License](#license) 6. [Contact](#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. **Flask API Server** - Central hub for all AI/ML models. - RESTful API design for standardized interactions. - Authentication and authorization mechanisms. 2. **Model Endpoints** - `/api/intelligent-routing` - Endpoint for intelligent routing and workflow automation. - `/api/sentiment-analysis` - Endpoint for advanced sentiment and emotional intelligence analysis. - `/api/multilingual-translation` - Endpoint for multilingual translation in chatroom. - `/api/job-recommendation` - Endpoint for worker job recommendation. 3. **Data Handling and Validation** - Input validation using libraries like `pydantic` or `marshmallow`. 4. **Scalability and Deployment** - 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:** ```json { "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:** ```json { "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:** ```json { "grievance_id": "G12349", "text": "Why hasn't the maintenance team fixed the leaking roof yet?" } ``` **Example Output:** ```json { "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:** ```json { "user_message": "toilet me paani nahi aa rha hain", "source_language": "Hindi", "target_language": "English" } ``` **Example Output:** ```json { "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:** ```json { "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:** ```json { "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](LICENSE). ## Contact For any questions or feedback, please contact [imt_2022089@iiitm.ac.in](mailto:imt_2022089@iiitm.ac.in).