# StudyMate AI

StudyMate AI is a lightweight multi-agent learning assistant built with Python and Streamlit, powered by Google Gemini.

It routes user prompts to specialized agents for:

• Summarization
• Simple ELI5-style explanations
• Quiz/question generation

Features

• Multi-agent routing based on user intent keywords
• Streamlit web UI for interactive usage
• Gemini-backed text generation via google-genai
• Simple modular structure (agents/ + services/)
• Docker support for containerized deployment

Project Structure

studymate-GenAI/
  app.py                  # Streamlit app entrypoint
  main.py                 # CLI entrypoint (currently references route_task)
  requirements.txt
  Dockerfile
  config.toml             # Streamlit config
  agents/
    __init__.py
    main_agent.py         # Central request router
    summarizer_agent.py   # Summary specialist
    eli5_agent.py         # Explain-like-I'm-5 specialist
    question_agent.py     # Quiz/question specialist
  services/
    __init__.py
    gemini_service.py     # Gemini API integration
    config.toml           # Streamlit config duplicate

How It Works

1) Request Routing

agents/main_agent.py inspects user input and routes by keyword:

• Contains summarize -> Summarizer Agent
• Contains question or quiz -> Question Generator Agent
• Contains explain -> Explain Agent (ELI5)
• No recognized keyword -> defaults to Explain Agent (ELI5)

The router returns a dictionary:

{
  "agent": "Agent Name",
  "response": "Generated text"
}

2) Specialized Agents

Each agent builds a role-specific prompt and calls a shared Gemini service:

• summarizer_agent.handle(text)
• eli5_agent.handle(text)
• question_agent.handle(text)

3) LLM Service Layer

services/gemini_service.py initializes a Gemini client using GEMINI_API_KEY and calls:

• Model: gemini-2.5-flash
• Method: client.models.generate_content(...)

Setup

Prerequisites

• Python 3.10+ (3.11 recommended)
• A Google Gemini API key

1. Clone and enter project

git clone <your-repo-url>
cd studymate-GenAI

If your local folder contains a nested studymate-GenAI directory, run commands from the directory that contains app.py.

2. Create and activate a virtual environment

Windows (PowerShell)

python -m venv .venv
.\.venv\Scripts\Activate.ps1

macOS/Linux

python -m venv .venv
source .venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

Create a .env file in the same folder as app.py:

GEMINI_API_KEY=your_actual_api_key_here

Run the App (Streamlit)

streamlit run app.py

Then open the URL shown in your terminal (typically http://localhost:8501).

Docker Run

Build image:

docker build -t studymate-ai .

Run container:

docker run --rm -p 8080:8080 --env GEMINI_API_KEY=your_actual_api_key_here studymate-ai

App will be available at http://localhost:8080.

Usage Examples

Type prompts like:

• Summarize the history of Rome
• Explain quantum computing
• Give me a quiz on Python lists

Routing behavior is keyword-based, so include words like summarize, explain, question, or quiz for predictable results.

Configuration Notes

The repository includes Streamlit config in both:

• config.toml
• services/config.toml

Both currently define:

• enableCORS = false
• enableXsrfProtection = false
• headless = true

For production deployments, you should review security-related settings (CORS and XSRF) before exposing publicly.

Known Issue

main.py imports route_task from agents/main_agent.py, but the router function currently defined is process_request. This means CLI mode may fail unless updated.

Web mode (app.py) correctly uses process_request and works as intended.

Troubleshooting

• Missing API key error in UI:
  • Ensure .env exists and contains GEMINI_API_KEY.
• Empty/failed responses:
  • Check API key validity and quota.
  • Verify internet connectivity.
• Import errors:
  • Confirm dependencies installed from requirements.txt.

Tech Stack

• Python
• Streamlit
• Google Gemini (google-genai)
• python-dotenv

Future Improvements

• Replace keyword routing with intent classification
• Add unit tests for agent routing and service layer
• Add chat history and session memory
• Add confidence/fallback handling for unsupported requests
• Fix CLI entrypoint (main.py) naming mismatch