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MeHC your ultimate companion for comprehensive health and wellness! It provides you with up to date health and medical articles, ensuring you stay well-informed about the world of healthcare. From resolving medical queries with efficiency to suggesting nearby hospitals for immediate care, MeHC is your go-to resource for all things health-related. The application also assesses your interaction with both the bots to gauge your mental health status as well.
| Frontend |       |
|---|---|
| Backend |      |
- User Login/Registration/Logout functionalities.
- Homepage with relevant daily health and medical articles scraped from top health/medical sites.
- Finding nearby hospitals on allowing geolocation access to your location and displaying it on a constructed map.
- Conversations with MedGPT (ChatGPT 3.5 turbo) which answers only Medical Queries by using a simple filter.
- Conversations with MeHC Chatbot for mental health to assess and understand your mental conditions.
- Viewing previous chats with MedGPT for perfesonal reference or other uses.
- User Status of their mental health via a graph using sentiment analysis of their conversations with the chatbot and suggesting them how to go about their mental health.
You can setup all the files via the clone command using the link:
https://github.com/AAbhijithA/MeHC.git
(Note: Ensure you have python downloaded on your system to run this, if not download from here: Download Python)
All the necessary libraries are listed in the requirements.txt file so you can download them using the command:
pip install -r requirements.txt
(Note: You can do the above in a virtual environment set up in your directory, more details on setup and activation here: Virtual Environment)
Ensure you use your environment variables or secrets to the key for your API's in the given .env file for deployment
You can then run your application by the following command:
python app.py
The information related to training the chatbot can be found in the 'Model_Train' folder.
Click this link to redirect yourself there: Model Training and Saving
- Daily articles are cached in the backend and only update every new day ensuring low runtime to render the page.
- The API is called passing through a filter to rate-limit the API usage by google search for relevant keywords.
- The regression analysis of polarity scores is done optimally to ensure low runtime in the backend.
- Exception handling done to ensure server errors are handled and shown in the frontend in different use-cases.
- HTML | Bootstrap-CSS | JavaScript: For the frontend of the web-application.
- ChartJs: For the display of the chart regarding status of the user.
- Python: Backend programming language.
- BeautifulSoup: For scraping relevant links and info of multiple sites for up-to-date articles.
- Folium: For constructing the map of nearby hospitals with the help of LeafletJs.
- Geoapify API: Using Places-API for receiving hospitals radially nearby users location.
- NLTK: For pre-processing the data we train the model with.
- Numpy: For pre-processing the data before giving it to the model to train with.
- Tensorflow: For training the Chatbot model and reusing them by loading them later.
- Pickle: Saving/Opening the files of pre-processed data and trained model.
- Flask: Web-Application microframework in python.
- SQLite: Serverless database used for storing user information.
- OpenAI API: Using ChatGPT only for medical queries using a simple filter.
- TextBlob: using NLP for sentiment ploarity of chat conversations with chatbot for later use.
Abhijith Ajith : AAbhijithA