Mental Health Chatbot Using Machine Learning and Flask Source Code ( Final Year)
Download clean, well-commented Mental Health Chatbot Using Machine Learning and Flask source code for final year projects — easy to run, demo-ready, and mentor-friendly.
- MACHINE-LEARNING Project
- MySQL / MongoDB
- Setup guide & demo steps
- Beginner-friendly
Keywords: source code, final year project code, Mental Health Chatbot Using Machine Learning and Flask Git, documentation, installation guide, machine-learning project, college project demo.
Download Source Code
Admin Features
- 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
Description
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.
The Mental Health Chatbot Using Machine Learning and Flask final-year Mental Health Chatbot Using Machine Learning and Flask source code is structured for fast setup and easy customization. You get readable code, clear folder architecture, and a guided README so you can run locally and present confidently.
Source Code Overview
Technical snapshot & environment- Project Name
- Mental Health Chatbot Using Machine Learning and Flask
- Language / Stack
- machine-learning
- Database
- MySQL or MongoDB
- Browsers
- Chrome, Firefox, Edge, Opera
- Included in the download
- Frontend,Backend,Database
- Run Scripts
- Documented in README (install, seed, start)
- License
- Academic use for college submission
User Features
- 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
Other Features
- 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
How to run Mental Health Chatbot Using Machine Learning and Flask
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
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
License
Mental Health Chatbot Using Machine Learning and Flask Source Code Tags
Related & Popular Project Reports
Explore more final-year projects you might be interested in
Credit Card Fraud Detection Using Machine Learning — Source Code
Fraud Detection model based on anonymized credit card transaction. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The datasets contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
Concrete Crack Detection Using Machine Learning — Source Code
This project leverages computer vision and machine learning techniques to automate the process of detecting cracks in concrete structures. The primary goal is to provide an efficient and accurate method for damage surveillance in buildings, which is crucial for maintaining structural integrity and safety. The project was developed as an entry for the "PS-1, Concrete Crack Detection". The model has achieved an impressive F1 score of 1, indicating its high accuracy in distinguishing between cracked and non-cracked surfaces.
Fake News Detection System Using Django and Machine Learning — Source Code
<p>Fake News Detection is a Django web application that classifies news articles as Real or Fake using a hybrid machine learning model. The system uses TF-IDF vectorization with Logistic Regression and Random Forest for accurate fake news classification. Users can register, log in, check news by pasting text or article URLs, and view prediction history, analytics, and dashboard reports. The application also supports URL-based article extraction using BeautifulSoup and requests, model transparency with probability scores, admin management tools, staff dashboard access, and report visualization with charts. This project is ideal for demonstrating Django, machine learning, NLP, fake news detection, user authentication, admin panel development, and dashboard analytics in one complete web application</p>
Brain Tumor Detection Using Machine Learning — Source Code
Brain Tumor Detection Using Machine Learning Source Code project implements a U-Net model to segment brain tumors in MRI images. It focuses on identifying and classifying three types of brain tumors: meningioma, glioma, and pituitary tumors. The project uses the dataset from publicly available brain tumor segmentation data and applies deep learning techniques to accurately delineate tumor regions from MRI scans.
Brain Tumor Detection System using Python and Machine Learning — Source Code
<p><strong>NeuroScan</strong> is a college-level <strong>brain tumor detection web application</strong> built with <strong>Python Flask</strong>, <strong>SQLite</strong>, <strong>scikit-learn</strong>, and <strong>OpenCV</strong>. The system allows users to upload <strong>brain MRI images</strong>, preview scans, run <strong>brain tumor classification</strong>, and download <strong>PDF/TXT reports</strong>. It includes a complete <strong>user panel</strong> and <strong>admin dashboard</strong> with authentication, prediction history, model training, dataset management, reporting, and feedback handling.</p> <p>This project performs <strong>local machine learning inference</strong> without using any third-party API. The ML pipeline uses <strong>HOG features</strong>, <strong>image histogram analysis</strong>, <strong>edge detection</strong>, and a <strong>RandomForestClassifier</strong> for <strong>MRI image classification</strong>. It supports both <strong>binary classification</strong> such as <strong>Tumor / No Tumor</strong> and <strong>multiclass brain tumor classification</strong> depending on the training dataset.</p> <p>The project is ideal for <strong>final year students</strong>, <strong>machine learning beginners</strong>, <strong>Flask web development learners</strong>, and <strong>academic demonstration purposes</strong></p>
Agricultural Monitoring and Crop Prediction System with Machine Learning — Source Code
<p><strong>AgriMonitor Pro</strong> is a Flask web application for <strong>smart agricultural monitoring</strong>, <strong>crop recommendation</strong>, <strong>yield prediction</strong>, and <strong>risk classification</strong> using <strong>machine learning</strong>. The system is designed for <strong>farmers</strong> and <strong>administrators</strong> to manage farms, record crop and soil data, train ML models locally, generate predictions, and download reports in <strong>CSV</strong> and <strong>PDF</strong> formats.</p> <p>This agriculture management system uses <strong>Python 3</strong>, <strong>Flask 3</strong>, <strong>SQLAlchemy</strong>, <strong>SQLite</strong>, <strong>pandas</strong>, and <strong>scikit-learn</strong>. It supports <strong>Random Forest classification and regression</strong>, dataset management, user management, farm monitoring, soil health tracking, analytics dashboards, and report generation with <strong>Matplotlib</strong> and <strong>ReportLab</strong>.</p> <p>The platform provides a guided farmer portal for adding farms, entering NPK and weather values, checking crop health, estimating yield, and reviewing prediction history. It also includes a powerful admin panel for managing users, datasets, model training, notifications, feedback, and data exports.</p> <p>This project is suitable for <strong>agriculture technology</strong>, <strong>farm management software</strong>, <strong>smart farming solutions</strong>, <strong>precision agriculture systems</strong>, and <strong>machine learning based crop advisory platforms</strong></p>
Dynamic Event Scheduling and Conflict Resolution System — Source Code
FestivalOS is a smart and scalable festival management web application developed using Python, Flask, SQLAlchemy, SQLite, Bootstrap 5, and scikit-learn. The system is designed for large-scale festival operations and helps manage events, venues, resources, participants, and schedule conflicts through an intelligent web platform. This project includes a public landing page, a secure admin console, and a dedicated user portal for attendees, volunteers, performers, coordinators, and administrators. The core highlight of the system is its constraint-aware event scheduling engine, conflict detection module, and machine-learning–assisted priority scoring for better slot recommendations. FestivalOS is an ideal final year college project for students looking to build a real-world event management and scheduling system using Flask and Python.
Quick actions
What we provide
Project reports, source code, and PPTs tailored for final-year students. Explore, or message us for a custom build.