🩺 MedicalAssistantGPT

Welcome to MedicalAssistantGPT, a simple yet powerful Streamlit dashboard that leverages MongoDB, Streamlit, and OpenAI to help users explore medical symptom-disorder relationships. This repository is part of the Big Data Engineering course for the academic year 2024–2025 🎓.

✨ Features

• 📊 Interactive dashboard built with Streamlit
• 🧠 Uses OpenAI GPT to generate simple medical explanations
• 🗃️ Data storage and querying powered by MongoDB Atlas
• 🔄 Easy CSV data import via load_data.py
• 🔍 Symptom-based disorder filtering
• 📋 Real-time relation type stats

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📁 Project Structure

.
├── .streamlit/
│   └── secrets.toml         # Stores API keys (OpenAI)
├── .gitignore
├── dashboard.py             # Streamlit app for interactive exploration
├── LICENSE
├── load_data.py             # Script to load CSV data into MongoDB Atlas
├── README.md
├── relations.csv            # Dataset: [source, relationType, destination]
└── requirements.txt         # Python dependencies

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🚀 Getting Started

1. 🧪 Install Dependencies

Make sure you are using Python 3.8+. Then install all required libraries:

pip install -r requirements.txt

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2. ☁️ Set Up MongoDB Atlas

1. Create a free MongoDB Atlas account.
2. Create a new cluster and database named healthcare.
3. Inside the healthcare database, create a collection named relations.
4. Whitelist your IP and get your connection string.
5. Update the MongoDB connection string inside dashboard.py and load_data.py:

client = MongoClient("your-mongodb-connection-string")

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3. 🔐 Set Up OpenAI API Key

In the file .streamlit/secrets.toml, store your OpenAI key like this:

[openai]
api_key = "your-openai-api-key"

🔒 This file is ignored by Git. Don’t commit your secrets!

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4. 📥 Load the CSV into MongoDB

Use the load_data.py script to populate the relations collection:

python load_data.py

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5. 🖥️ Run the Dashboard

Launch the Streamlit dashboard:

streamlit run dashboard.py

Then visit http://localhost:8501 in your browser 🚀

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🧠 How It Works

• The dashboard lets users select two or more symptoms from the sidebar.
• It queries MongoDB to find disorders that match all selected symptoms.
• Then, it uses GPT to explain those disorders in simple terms—perfect for educational and demo purposes!

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📚 Example Use Case

Select:

• Symptom A: Fever
• Symptom B: Cough

➡️ The app identifies disorders like Influenza associated with both symptoms and provides a plain-language description and potential treatments.

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🧾 License

This project is licensed under the MIT License.

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🙌 Acknowledgments

Made with ❤️ for students of Big Data Engineering 2024–25
Instructors: Antonio Romano, Giuseppe Riccio, Vincenzo Moscato 👨‍🏫