⛑️ DocAI

💻 About DocAI

Features

DocAI streamlines the report-making process by automating key tasks while ensuring the authenticity of medical information. Here's how our application addresses these challenges:

1. Automated Report Generation: DocAI leverages natural language processing (NLP) techniques to automate report creation. Doctors can focus on patient care while our system generates accurate and concise reports.

2. User-Friendly Interface: Our intuitive web interface, built using Django and Ajax, provides a seamless experience for both doctors and patients. Users can easily input data, view reports, and access relevant information.

3. Enhanced Readability: We use Plotly and Plotly Express to create interactive visualizations within reports. Graphs, charts, and diagrams help convey complex medical data in a clear and digestible format.

4. Secure Authentication: Google OAuth3 integration ensures secure access for authorized users. Doctors can log in securely and manage patient data.

5. Virtual Assistant Bot: DocAI introduces an NLP-powered chatbot that educates users about minor symptoms, preventive measures, and general health awareness. This innovative feature enhances patient engagement and promotes well-being.

📫 Run Locally

To use DocAI, follow these steps:

1. Clone this repository.

  git clone https://github.com/sajji18/smart-lab-report.git

2. Setup virtual enviroment with: python -m venv <env_name> and activate it using source <env_name>/Scripts/activate on bash. Now, Install the required dependencies using pip install -r requirements.txt.
3. Set up your Google OAuth credentials from the Google API Console and download the required secrets.json. Create your own secrets.json in the base directory and exactly follow the .example.secret.json for format.
4. Create an admin superuser account from terminal with python manage.py createsuperuser.
5. Go to http://localhost:8000/admin, and log in using the superuser credentials.
6. From the sites option in the left sidebar, create a new site with: domain name: localhost:8000 and display name: localhost.
7. Now from social applications option in the left sidebar, create a new application: Set Provider: Google, name as you wish, YOUR client_id, client_secret and add the previously create site from available sites to chosen sites.
8. Create a doctor account from terminal with python manage.py create_user.
9. Now from Test option in the left sidebar, create a new test: (Either Blood Test or Diabetes Test) using the previously created doctor account of your choice.
10. Run the Django development server from the terminal: python manage.py runserver.
11. Access the application at: http://localhost:8000.
12. Create and log into a new customer account.
13. Now again in the admin dashboard, http://localhost:8000/admin, create a Test Application from left sidebar (Means User Applied for a Test), for the previously created customer account with the tests created by the doctor.
14. Now you can operate from the web app only.

📖 Video Demonstration and Presentation

• Presentation
• Video Presentation

🛠️ Tech Stack

Client/FrontEnd:

• Django: Our web application framework of choice for building the backend.

• Ajax: Used for asynchronous communication between the client and server, enhancing the user experience.

• Sqlite3: The lightweight database engine, suitable for development and testing purposes.

• Google OAuth2: For secure authentication and authorization of users.

ML/Data:

• Dash: Utilized for creating interactive, web-based data visualizations to enhance report readability.

• Django all-auth: Provides authentication and authorization features, ensuring secure access to the application.

• Plotly: A powerful visualization library used for creating dynamic and engaging charts within the reports.

• Plotly Express: Simplifies the creation of complex visualizations, further enhancing the report's clarity.

• torch: PyTorch is employed for machine learning tasks, aiding in the automation of report generation.

• nltk: Natural Language Toolkit used for processing natural language, facilitating the integration of text-based features like the chatbot.

🛠️ Challenges Faced

Problem and Approach

Problems	Approach	Status
Database Design	Hit and Trial	Decent
Ajax Dynamic Updates	Planned	Good
Chatbot Integration	Research	Good
Chart Integration	Research	Decent
Database and Chart Compatibility	Hit and Trial	Poor
Route Protection	Planned	Decent
Chat Utility	Planned	Good
Dark Mode Theme	Unplanned	Could be Better
NLP/bot optimization	Research	Could be Better
Bearable Value Visualisation	Unplanned	Didnt Apply
Report Generation and export	Planned	Decent

📃 Future Strategic Objectives

• Database and Chart Compatibility: Designing the database beforehand can often be found a good practice and we found it the hard way.
• Chart Integration: Dash was the first time we used , hence the integration of the Chart utility could have been better if we had known its use better
• Visual Appeal: We could have used React to enhance the UI but integration seemed a bit tiresome since we wanted to use django
• Dark Mode theme: An Additional utility like this could be easily added using React
• NLP Optimization: could be easier with the use of newer methods or by using llama 7b however , we wanted to work it out from scratch
• Bearable Value Visualisation: Customisable Values to have a better showcase of limited or range of values that are normal for health could be added , customising this would be easier in react

✨ Contributors-

• Siddhant Gupta
• Sajal Chauhan