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BCA Project Report
BCA Mental Health Chatbot Using Machine Learning and Flask Report
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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Abstract
-
—
Table of Content
-
01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
MindCare is a mental health chatbot web application built with Python Flask and Machine Learning. It helps users with mental wellness support, mood tracking, self-assessments, chat history, and emotional analysis. The system uses NLP, TF-IDF, Logistic Regression, and rule-based response selection to generate chatbot replies from local training data. It also includes a powerful admin panel to manage users, chatbot training data, emotion labels, assessments, reports, and wellness content.
This Flask mental health project is designed for academic projects, final year projects, portfolio websites, and machine learning demos. OpenAI integration is optional and can be enabled in code for AI-generated responses.
Login Credentials
Admin Login
- URL:
http://127.0.0.1:5000/admin/login - Username:
admin - Password:
admin123
Demo User Login
- URL:
http://127.0.0.1:5000/login
Demo accounts:
alice/password123bob/password123carol/password123david/password123emma/password123frank/password123grace/password123henry/password123
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- Separate admin login system
- Admin dashboard with user, chat, mood, assessment, and feedback statistics
- User management with search, edit, delete, activate, and deactivate options
- Training data management for chatbot patterns, intents, emotions, and responses
- Train and retrain ML models directly from admin panel
- Emotion label management
- Chat record monitoring with filters by user, date, and emotion
- Mood record management
- Assessment question management
- Assessment result filtering
- Suggestions and motivational quote management
- Emergency help content management
- Feedback management
- CSV export and PDF report generation
- Admin profile update and password change
- Secure admin logout
- User registration with validation
- User login using username or email
- Forgot password with token-based reset flow
- User profile management
- Mental wellness chatbot with AJAX chat interface
- Chat history with search and delete options
- Daily mood tracker
- Mood history
- Self-assessment questionnaire
- Assessment history
- Feedback submission
- Emergency help page
- Secure logout
- Built with Python 3 and Flask 3
- Uses Flask-SQLAlchemy for database operations
- Supports SQLite by default, with optional MySQL/PostgreSQL
- Implements Flask-Login authentication
- Uses Werkzeug password hashing
- Frontend created with HTML, CSS, JavaScript, and Bootstrap 5
- Natural Language Processing with NLTK
- Text preprocessing with stopword removal, tokenization, and stemming
- TF-IDF vectorization with up to 5,000 features and 1–2 gram support
- Logistic Regression classification for intent and emotion prediction
- Pattern-first response matching
- Keyword-based heuristic overrides for better chatbot accuracy
- Joblib model saving and loading
- Optional OpenAI API integration
- PDF report export using ReportLab
- Environment configuration with python-dotenv
- Suitable for major project, mini project, capstone project, and portfolio showcase
Prerequisites
- Python 3.10+
- pip
Steps to run
cd "path/to/Mental HEalth Chatbot Using ML"
python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt
Optional environment setup
Create a .env file in the project root:
SECRET_KEY=your-secret-key-for-production
OPENAI_API_KEY=
OPENAI_MODEL=gpt-4o-mini
Seed database
python seed_database.py
Start project
python run.py
Open in browser
http://127.0.0.1:5000
README Included
Setup Support Available
Admin Login
- URL:
http://127.0.0.1:5000/admin/login - Username:
admin - Password:
admin123
Demo User Login
- URL:
http://127.0.0.1:5000/login
Demo accounts:
alice/password123bob/password123carol/password123david/password123emma/password123frank/password123grace/password123henry/password123