docs : added endpoint urls

README.md

@@ -8,6 +8,327 @@ app_port: 7860
 pinned: false
 ---
 
-# Hostel Management System API
+# Hostel Grievance Redressal System
 
-This is a Flask-based API for hostel management with ML capabilities.
+## 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.
+   - Kubernetes or cloud-based services for orchestration.
+
+---
+
+## 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).