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| # Hugging Face AI Agents Course - Final Exam Agent | |
| This project contains an AI agent developed for the final exam of the Hugging Face AI Agents Course. The agent is designed to answer a variety of questions by leveraging a suite of powerful tools and a language model. | |
| ## Overview | |
| This agent is built using the `LangGraph` library to create a robust and stateful agent. It can perform a variety of tasks, including web searches, calculations, code execution, and processing different types of media like audio, images, and documents. The project includes a Gradio application for evaluating the agent's performance on a set of questions provided by the course. | |
| ## Features | |
| * **Multi-tool Integration**: The agent can use a wide range of tools to solve complex problems. | |
| * **Conversational AI**: Powered by a capable language model from OpenRouter. | |
| * **Stateful Execution**: Uses `LangGraph` to manage the conversation flow and tool execution in a structured manner. | |
| * **Web Interface**: A Gradio app (`app.py`) is provided to test and evaluate the agent. | |
| * **Extensible**: New tools can be easily added to enhance the agent's capabilities. | |
| ## Tools | |
| The agent has access to the following tools: | |
| ### Community Tools | |
| * **Brave Search**: Performs web searches to find up-to-date information. | |
| * **Python REPL**: Executes Python code to solve logic and math problems. | |
| ### Custom Tools | |
| * **Calculator**: | |
| * `add(a, b)`: Adds two numbers. | |
| * `subtract(a, b)`: Subtracts two numbers. | |
| * `multiply(a, b)`: Multiplies two numbers. | |
| * `divide(a, b)`: Divides two numbers. | |
| * `power(a, b)`: Calculates `a` to the power of `b`. | |
| * **Date & Time**: | |
| * `current_date()`: Returns the current date. | |
| * `day_of_week()`: Returns the current day of the week. | |
| * `days_until(date_str)`: Calculates the number of days until a given date. | |
| * **Media Processing**: | |
| * `transcribe_audio(audio_file, file_extension)`: Transcribes audio files. | |
| * `transcribe_youtube(youtube_url)`: Transcribes YouTube videos. | |
| * `query_image(query, image_url)`: Answers questions about an image. | |
| * **Web & Document Content**: | |
| * `webpage_content(url)`: Extracts text from webpages and PDF files. | |
| * `read_excel(file_path, sheet_name, query)`: Reads data from an Excel file and answers a query about it. | |
| ## How It Works | |
| The agent's logic is defined in `agent.py`. It uses a `StateGraph` from the `LangGraph` library to manage its execution flow. The graph has two main nodes: | |
| 1. **`llm_call`**: This node calls the language model with the current conversation history and a system prompt (`prompt.py`). The LLM decides whether to respond directly to the user or to use one of the available tools. | |
| 2. **`environment`**: If the LLM decides to use a tool, this node executes the tool with the arguments provided by the LLM. | |
| The agent alternates between these two nodes until the LLM generates a final answer for the user. | |
| ## Usage | |
| ### 1. Installation | |
| Clone the repository and install the required dependencies: | |
| ```bash | |
| git clone https://huggingface.co/spaces/YOUR_SPACE_HERE | |
| cd YOUR_REPO | |
| pip install -r requirements.txt | |
| ``` | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |