README.md

metadata

title: 🤖 VCell BioModel Chatbot
emoji: 🧬
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.44.1
app_file: app.py
pinned: true
license: mit
short_description: Demo Chatbot to query VCell modeling resources

VCell BioModel Chatbot Demo

This demo presents an intelligent chatbot interface for querying and interpreting biomodels stored in the VCell BioModel Database. It enables users to interact with biological modeling resources using natural language and provides structured outputs, model metadata, downloadable files, and visualizations in real time. This work was developed as a demo for the NRNB Organization for Google Summer of Code 2025.

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Overview

This chatbot is an AI-powered assistant capable of:

• Interpreting natural language queries
• Extracting structured parameters
• Querying the VCell API
• Summarizing model data in a human-readable way using a large language model (LLM)
• Visualizing model diagrams
• Providing downloadable model files

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Features

• Natural Language Interface — Query the database with simple English prompts
• LLM Parameters Extraction — Uses LLaMA 3 to extract structured parameters
• VCell API Integration — Supports dynamic querying with filters like author, category, biomodelid...
• Summarization — Generates high-level descriptions of model contents in a human [gif of parameters extraction, API response and summarization]
• Visualization — Displays system reaction diagrams from the API
• Download Options — Direct links to SBML and VCML formats for downstream use [gif of downloading the provided files and visualizations]
• Streamlit UI — Minimalist, responsive, and easy to deploy

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Technologies Used

• Python
• Streamlit
• Groq API for the LLM (LLaMA 3.3-70B)
• VCell Public API
• Pydantic (parameter schema)
• Dotenv (secret management)

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Getting Started

Prerequisites

• Python 3.10 or higher
• A valid Groq API key

Installation

git clone https://github.com/KacemMathlouthi/VCell-Demo.git
cd VCell-Demo
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Configuration

Create a .env file at the root of the project:

LLM_API_KEY=your_groq_api_key_here

Run the Application

streamlit run app.py

Visit http://localhost:8501 to start using the chatbot.

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Example Prompts

Here are some questions the chatbot can understand:

• List all public models by user ion
• Find the model with ID 201844485
• Show VCell models related to calcium

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Project Structure

├── app.py                     # Main application entry point
├── requirements.txt           # Project dependencies
├── .env                       # Environment variable for API key
├── .streamlit/config.toml     # UI configuration
├── vcelldb/
│   ├── vcell_api.py           # Wrapper for VCell API calls
│   ├── diagram.py             # Utilities for diagrams and downloads
│   └── params_model.py        # Schema definitions
└── utils/
    ├── llm_helper.py          # LLM instance creation and response generation
    └── params_extraction.py   # Prompt-to-parameter process

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License

This project is licensed under the MIT License. You are free to use, modify, and distribute the software with proper attribution.

Special thanks to the Virtual Cell (VCell) team and the National Resource for Network Biology (NRNB) for their support.

For more information, visit vcell.org.