---
title: Final Assignment - Agent Course by Hugging Face
emoji: 🕵🏻‍♂️
colorFrom: indigo
colorTo: indigo
# sdk: gradio
# sdk_version: 5.25.2
# app_file: app.py
sdk: docker
app_port: 7860
pinned: false
hf_oauth: true
# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
hf_oauth_expiration_minutes: 480
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# Agent Course Final Assignment by Hugging Face 

This project contains a Gradio application for evaluating a LangChain agent based on the GAIA (General AI Assistant) benchmark. The agent is designed to answer questions using a variety of tools, and its performance is scored by an external API.

## Features

-   **Gradio Interface**: An easy-to-use web interface for running the evaluation and viewing the results.
-   **LangChain Agent**: A sophisticated agent built with LangChain, capable of using tools to answer questions.
-   **Multi-Tool Integration**: The agent can interact with multiple tools, such as a browser (via Playwright) and a YouTube transcript fetcher.
-   **Docker Support**: The entire application can be built and run using Docker and Docker Compose, ensuring a consistent environment.
-   **Observability**: Integrated with Langfuse for tracing and monitoring the agent's behavior.

## Installation

1.  **Clone the repository:**

    ```bash
    git clone https://huggingface.co/spaces/hf-agent-course/final-assignment-template
    cd final-assignment-template
    ```

2.  **Create a virtual environment and install dependencies:**

    ```bash
    python -m venv .venv
    source .venv/bin/activate  # On Windows, use `.venv\Scripts\activate`
    pip install -r requirements.txt
    ```

3.  **Install Playwright browsers:**

    ```bash
    npx playwright install
    ```

4.  **Set up environment variables:**

    Create a `.env` file in the root of the project and add the following variables:

    ```
    HF_TOKEN=<your-hugging-face-token>
    GOOGLE_API_KEY=<your-google-api-key>
    LANGFUSE_PUBLIC_KEY=<your-langfuse-public-key>
    LANGFUSE_SECRET_KEY=<your-langfuse-secret-key>
    ```

## Usage

To run the Gradio application locally, use the following command:

```bash
python app.py
```

This will start a local web server, and you can access the application in your browser at `http://127.0.0.1:7860`.

## Docker

This project includes `Dockerfile` and `docker-compose.yml` for running the application in a containerized environment.

### Build and Run with Docker Compose

To build and run the application using Docker Compose, use the following command:

```bash
docker-compose up --build
```

This will build the Docker image and start the application. You can access the Gradio interface at `http://localhost:7860`.

### Development Environment

A `Dockerfile.dev` is also provided for development purposes. To build and run the development environment, use the following command:

```bash
docker-compose -f docker-compose.yml -f docker-compose.dev.yml up --build
```

This will mount the local code into the container, allowing for live reloading of changes.

## Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.