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MACHINE-LEARNING Project
Electronic Health Recognition Summarization Final Year Flask Project Source Code
Complete final-year project source code with frontend, backend, database and setup documentation. Instant download after secure payment.
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Project's Overview
MedSynapse EHR is a final year project built with Python Flask for students who want a healthcare-focused web application with document processing and clinical summarization features. This final year project allows users to add patients, upload PDF or TXT electronic health records, extract text, generate structured 8-section summaries, compare extracted content with generated sections, edit the final narrative, and download summary reports as PDF. The admin side of this final year project includes users, patients, EHR documents, summaries, medical categories, terms, diseases, medicines, reports, and branding settings. The system works with a heuristic summarizer by default and also supports optional Hugging Face Flan-T5 fine-tuning for advanced experimentation. MedSynapse EHR is suitable for a final year major project in Flask, healthcare software, NLP, or AI-assisted clinical documentation.
Login Credentials
Administrator
- Email:
[email protected] - Password:
Admin@123
Sample End Users
- Email:
[email protected] - Password:
User@123 - Email:
[email protected] - Password:
User@123
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- Admin dashboard for this final year project with system-wide metrics
- User account creation, viewing, editing, blocking/unblocking, and deletion
- Patient listing, creation, owner assignment, detail view, editing, and deletion
- EHR document listing, full-text reading, metadata viewing, and deletion
- Summary listing, reading, editing, saved-flag management, and deletion
- Medical category CRUD with term counts
- Medical term CRUD with optional category link
- Disease CRUD with unique names
- Medicine CRUD with dosage and usage text
- Brand/system settings with product name, contact email, address, description, and logo upload
- Aggregate reports for users, patients, EHR files, summaries, and taxonomy
- Admin password change and logout
- End-user registration and login in this final year project
- Dashboard with totals, recent summaries, workflow guidance, and quick actions
- Patient add, list, edit, and delete features
- Upload EHR files in PDF or TXT format
- PDF text extraction using
pypdf - TXT file reading as UTF-8
- Re-extraction support from EHR detail page
- 8-section clinical summary generation
- Side-by-side original extracted text and generated summary comparison
- Editable narrative summary
- Draft update and final summary save
- PDF report download
- Summary history with open, edit, download, and delete options
- Search by patient name, external ID, condition hint, or summary fields
- Profile update and password change
- Logout functionality
- Public landing and authentication pages included in this final year project
- Role-based user and administrator consoles
- SQLite default database with configurable
DATABASE_URL - CSRF protection using Flask-WTF
- Password hashing and session login support
- 16 MB upload limit
- Heuristic summarization mode that works without GPU
- Optional Hugging Face Flan-T5 summarization mode
- Optional ML training scripts and JSONL export flow
- PDF summary generation using
fpdf2 - Upload and branding folders created at runtime
- Suitable for final year project demo, viva, source code review, and report preparation
- Healthcare disclaimer required before real use
- Open the final year project folder in a terminal.
- Create and activate a virtual environment:
python -m venv .venv
Windows:.venv\Scripts\activate
Linux/macOS:source .venv/bin/activate - Install the web app dependencies:
pip install -r requirements.txt - Optional: copy environment file and configure values:
Windows:copy env.example .env
Linux/macOS:cp env.example .env - Seed demo data and default accounts:
python seed_data.py - Start the final year Flask project server:
python run.py - Open the application at:
http://127.0.0.1:5000/ - Optional ML training setup:
pip install -r requirements-ml.txt
Then export training data and fine-tune Flan-T5 only if you have enough quality examples and suitable hardware.
README Included
Setup Support Available
Administrator
- Email:
[email protected] - Password:
Admin@123
Sample End Users
- Email:
[email protected] - Password:
User@123 - Email:
[email protected] - Password:
User@123