Frontend Basic UI For LangGraph Agent Discussion In AndyLearnsAI

by StackCamp Team 65 views

Hey guys! Let's dive into creating a basic frontend UI for our LangGraph Agent Discussion project. This is part of the AndyLearnsAI series, focusing on building an AI agent for one-on-one check-ins. Our goal is to set up a minimal frontend that can trigger the LangGraph multi-agent system. We'll include a Start button to initiate the agent and ensure there's a simple API handshake to kick things off. Let’s break down the process step by step.

Understanding the Project Scope

Our scope here is pretty straightforward: we're focusing on the frontend UI, a Start button, and the API handshake. We need a user interface that renders correctly, a Start button that sends a request to begin the agent's process, and a backend that responds with a success message or an error if something goes wrong. Error handling is crucial to give the user feedback when errors happen.

Proposed Plan Breakdown

To achieve this, we’ll follow a well-structured plan. This plan involves creating directories and files, setting up the frontend with HTML, CSS, and JavaScript, configuring a Flask server for the backend, and ensuring everything communicates smoothly. Let’s get into the details!

1. Setting Up the Frontend Structure

First, we'll create a new frontend directory at the project root. This directory will house all our frontend-related files. Inside this frontend directory, we'll create an index.html file. This file is the heart of our UI, serving as the entry point for our frontend application.

Crafting the index.html

The index.html file will contain the basic HTML structure needed for our UI. This includes:

  • A title for the page.
  • A heading to give context to the user.
  • A Start button that will trigger the agent.
  • A div element to display feedback messages to the user.

We'll give the Start button an id of startButton and the feedback div an id of feedback. These IDs will be essential for our JavaScript code to interact with these elements.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>LangGraph Agent UI</title>
    <link rel="stylesheet" href="static/style.css">
</head>
<body>
    <h1>LangGraph Agent Discussion</h1>
    <button id="startButton">Start Agent</button>
    <div id="feedback"></div>
    <script src="static/script.js"></script>
</body>
</html>

2. Styling the UI with CSS

Next up, we’ll create a static directory inside the frontend directory. This static directory will hold our static assets, such as CSS and JavaScript files. Inside the static directory, we’ll create a style.css file. This file will contain the CSS rules to style the elements in our index.html file. We’ll focus on making the Start button and feedback area visually appealing and user-friendly.

Creating style.css

The style.css file will define the look and feel of our UI. We might style the button with a specific color, font, and padding to make it stand out. The feedback area might have a border and some padding to make it clearly visible. Here’s a basic example:

/* static/style.css */
body {
    font-family: Arial, sans-serif;
    display: flex;
    flex-direction: column;
    align-items: center;
    padding: 20px;
}

#startButton {
    padding: 10px 20px;
    background-color: #4CAF50;
    color: white;
    border: none;
    cursor: pointer;
    font-size: 16px;
    margin-bottom: 20px;
}

#feedback {
    border: 1px solid #ccc;
    padding: 10px;
    min-height: 50px;
    width: 80%;
    text-align: center;
}

3. Adding Interactivity with JavaScript

Inside the static directory, we’ll also create a script.js file. This file will contain the JavaScript code to handle the Start button click event, make an API request to a new /start_agent endpoint, and update the feedback div with the API response or error messages. We'll be using the fetch API for making the API request.

Implementing script.js

The script.js file will be responsible for making our UI interactive. Here’s what it will do:

  1. Add an event listener to the Start button to listen for click events.
  2. When the button is clicked, it will make an API request to the /start_agent endpoint.
  3. It will then update the feedback div with the response from the API. This could be a success message or an error message if something goes wrong.
// static/script.js
document.addEventListener('DOMContentLoaded', () => {
    const startButton = document.getElementById('startButton');
    const feedbackDiv = document.getElementById('feedback');

    startButton.addEventListener('click', () => {
        feedbackDiv.textContent = 'Starting agent...';
        fetch('/start_agent', {
            method: 'POST',
            headers: {
                'Content-Type': 'application/json',
            },
        })
        .then(response => response.json())
        .then(data => {
            if (data.success) {
                feedbackDiv.textContent = 'Agent started successfully!';
            } else {
                feedbackDiv.textContent = `Error: ${data.message}`;
            }
        })
        .catch(error => {
            feedbackDiv.textContent = `Error: ${error.message}`;
        });
    });
});

4. Setting Up the Backend with Flask

Now, let’s move to the backend. We'll modify the langgraph_ai_checkin/main.py file to set up a basic Flask server. This server will define a /start_agent endpoint. When this endpoint is hit, it will trigger the run_check_in_process function from langgraph_ai_checkin/main.py. The endpoint will return a JSON response indicating success or failure.

Creating the /start_agent Endpoint

We need to set up a Flask server and define a /start_agent route. This route will be responsible for initiating the agent process. We’ll also ensure that CORS (Cross-Origin Resource Sharing) is enabled so that our frontend can communicate with our backend without any issues.

# langgraph_ai_checkin/main.py
from flask import Flask, jsonify
from flask_cors import CORS

app = Flask(__name__)
CORS(app)

@app.route('/start_agent', methods=['POST'])
def start_agent():
    try:
        # Replace with your actual agent initiation logic
        result = run_check_in_process()
        return jsonify({'success': True, 'message': 'Agent started successfully!'})
    except Exception as e:
        return jsonify({'success': False, 'message': str(e)}),

# Dummy function for demonstration
def run_check_in_process():
    return "Agent process initiated"

if __name__ == '__main__':
    app.run(debug=True)

5. Installing Dependencies

To run our Flask server, we need to install a couple of dependencies. We’ll update the langgraph_ai_checkin/requirements.txt file to include Flask and Flask-CORS. This ensures that our server can handle requests and that CORS is properly configured.

Updating requirements.txt

Simply add the following lines to your requirements.txt file:

Flask
Flask-CORS

Then, run pip install -r requirements.txt to install the dependencies.

6. Configuring Flask and CORS

Finally, we’ll modify the langgraph_ai_checkin/main.py file to instantiate the Flask app, configure CORS, and define the /start_agent route. The run_check_in_process function will be called within this route, and the response will be serialized to JSON, including success/error messages.

Implementing the Flask App

Here’s how we’ll set up the Flask app:

  1. Import the necessary modules from Flask and Flask-CORS.
  2. Instantiate the Flask app.
  3. Configure CORS to allow cross-origin requests.
  4. Define the /start_agent route.
  5. Call the run_check_in_process function within the route.
  6. Serialize the response to JSON, including success/error messages.

This setup ensures that our frontend and backend can communicate effectively, and any issues are properly reported to the user.

Acceptance Criteria

To make sure we’re on the right track, let’s define the acceptance criteria for this task:

  • The UI should render correctly in a web browser.
  • The Start button should send a request to the /start_agent endpoint when clicked.
  • The backend should respond with a success message when the agent starts successfully.
  • Error handling should be in place to display error messages in the feedback area if something goes wrong.

By meeting these criteria, we can be confident that our basic frontend UI is functioning as expected and that we have a solid foundation for further development.

Conclusion

So, guys, that’s how we’ll build a basic frontend UI for our LangGraph Agent Discussion project. We've covered everything from setting up the HTML, CSS, and JavaScript to configuring a Flask server with CORS. By following this plan, we’ll have a functional UI that can trigger our agent and provide feedback to the user. Let's get to coding!