| --- |
| title: Eduagent |
| emoji: 🚀 |
| colorFrom: red |
| colorTo: red |
| sdk: docker |
| app_port: 8501 |
| tags: |
| - streamlit |
| pinned: false |
| short_description: Educational Agent |
| license: cc |
| --- |
| |
| # 🎓 EduAgent — Educational AI Agent |
|
|
| A minimal but complete **agentic AI tutor** built with Groq + Streamlit. |
| Demonstrates: tool calling, multi-step agent loops, multi-turn memory, and clean UI. |
|
|
| --- |
|
|
| ## What it does |
|
|
| | Tool | Triggered when you ask… | |
| |------|--------------------------| |
| | `explain_concept` | "What is X?", "Explain Y for a beginner" | |
| | `generate_quiz` | "Quiz me on Z", "Give me 3 questions about…" | |
| | `solve_problem` | "Solve:", "Calculate:", "Find the derivative…" | |
| | `create_study_plan` | "Make a study plan for…", "How to learn X in 4 weeks" | |
|
|
| You can chain them: *"Explain recursion, then quiz me on it"* calls two tools in one turn. |
|
|
| --- |
|
|
| ## Groq API — why Groq? |
|
|
| - **Free tier** at [console.groq.com](https://console.groq.com) — no credit card needed |
| - Extremely fast inference (useful for agentic loops with multiple LLM calls) |
| - Supports Llama 3.3 70B, Gemma 2, Mixtral, and more |
|
|
| Get your key: **console.groq.com → API Keys → Create** |
|
|
| --- |
|
|
| ## 🚀 Platform setup |
|
|
| ### Streamlit Cloud (recommended) |
| 1. Fork / push this repo to GitHub |
| 2. Go to [share.streamlit.io](https://share.streamlit.io) → New app |
| 3. Set **main file** to `app.py` |
| 4. Under **Secrets**, add: |
| ```toml |
| GROQ_API_KEY = "gsk_your_key_here" |
| ``` |
| 5. Deploy ✓ |
|
|
| ### Hugging Face Spaces |
| 1. Create a new Space → SDK: **Streamlit** |
| 2. Upload `app.py` and `requirements.txt` |
| 3. Go to **Settings → Repository secrets** → Add `GROQ_API_KEY` |
| 4. The app reads `st.secrets["GROQ_API_KEY"]` automatically ✓ |
|
|
| ### Kaggle Notebook |
| ```python |
| # Cell 1 — install |
| !pip install streamlit groq pyngrok -q |
| |
| # Cell 2 — set key (use Kaggle Secrets: Add-ons → Secrets) |
| import os |
| from kaggle_secrets import UserSecretsClient |
| os.environ["GROQ_API_KEY"] = UserSecretsClient().get_secret("GROQ_API_KEY") |
| |
| # Cell 3 — run with tunnel |
| from pyngrok import ngrok |
| ngrok.set_auth_token("YOUR_NGROK_TOKEN") # free at ngrok.com |
| public_url = ngrok.connect(8501) |
| print("App URL:", public_url) |
| !streamlit run app.py --server.port 8501 --server.headless true |
| ``` |
|
|
| ### Local |
| ```bash |
| pip install -r requirements.txt |
| export GROQ_API_KEY="gsk_..." # or set in .env |
| streamlit run app.py |
| ``` |
|
|
| --- |
|
|
| ## Architecture |
|
|
| ``` |
| User input |
| │ |
| ▼ |
| ┌─────────────────────────────────┐ |
| │ System prompt + chat history │ |
| │ + TOOLS schema (4 tools) │ |
| └──────────────┬──────────────────┘ |
| │ |
| ▼ |
| Groq LLM call |
| │ |
| ┌───────┴────────┐ |
| │ tool_calls? │ No → final answer |
| │ Yes │ |
| ▼ │ |
| execute_tool() │ |
| (second LLM call │ |
| with focused prompt) │ |
| │ │ |
| └──► feed result back to LLM |
| (loop up to 5 iterations) |
| ``` |
|
|
| --- |
|
|
| ## Extending the agent |
|
|
| **Add a real calculator tool** (no LLM needed): |
| ```python |
| # In execute_tool(), add: |
| if tool_name == "calculate": |
| import math |
| return str(eval(tool_args["expression"])) # use safer eval in prod |
| ``` |
|
|
| **Add web search** (using DuckDuckGo, free): |
| ```python |
| from duckduckgo_search import DDGS |
| def web_search(query): |
| with DDGS() as ddgs: |
| return "\n".join(r["body"] for r in ddgs.text(query, max_results=3)) |
| ``` |
|
|
| **Swap Groq for OpenAI-compatible APIs** (same code, change base_url): |
| ```python |
| client = Groq(api_key=key) # Groq (default) |
| # client = OpenAI(api_key=key) # OpenAI |
| # client = OpenAI(api_key=key, # Together.ai / any OpenAI-compatible |
| # base_url="https://api.together.xyz/v1") |
| ``` |
