Chatbot web interface setup leveraging llm ability, styled with Tailwind CSS for user interaction and response display.

Introduction to the Chatbot Interface Setup Template

This template sets up a web interface for a chatbot leveraging LLM (Large Language Model) abilities. The interface is styled with Tailwind CSS for user interaction and response display. This guide will walk you through the steps to get the template up and running.

Getting Started

To begin using this template, click Start with this Template in the Lazy Builder interface.

Test

Press the Test button to deploy the app. This will launch the Lazy CLI, and you will be prompted for any required user input.

Using the App

Once the app is deployed, you can interact with the chatbot through the web interface. Here’s how to use the interface:

1. Header: The header displays the title "Your Chatbot".
2. Chat Area: The main area of the interface where chat messages are displayed.
3. Input Field: Type your question in the input field at the bottom.
4. Send Button: Click the "Ask" button to send your message.

Integrating the App

If your app requires further integration with external tools, follow these steps:

1. API Integration: If your app provides an API, you will receive a dedicated server link through the Lazy CLI after pressing the Test button. Use this link to interact with the app's API.
2. Frontend Integration: If the app includes a frontend interface, you can embed it into your existing web application or use it as a standalone interface.

Sample Code

Here is the sample code provided in the template:

template.html

```html

Your Chatbot

```

script.js

```javascript document.addEventListener('DOMContentLoaded', (event) => { const sendButton = document.getElementById('sendButton'); const userInput = document.getElementById('userInput'); const chat = document.getElementById('chat');

const sendMessage = () => {
    const userText = userInput.value.trim();
    if (userText !== '') {
        appendMessage(userText, 'user');
        userInput.value = ''; // Clear input after sending
        fetch('/send_message', {
            method: 'POST',
            headers: {
                'Content-Type': 'application/json',
            },
            body: JSON.stringify({ message: userText }),
        })
        .then(response => response.json())
        .then(data => {
            appendMessage(data.message, 'bot');
        })
        .catch((error) => {
            console.error('Error:', error);
            appendMessage('Sorry, there was an error processing your request.', 'bot');
        });
    }
};

const appendMessage = (text, sender) => {
    const messageDiv = document.createElement('div');
    messageDiv.textContent = text;
    messageDiv.className = sender === 'user' 
        ? 'p-2 my-2 text-right bg-blue-100 rounded-lg'
        : 'p-2 my-2 text-left bg-green-100 rounded-lg';
    chat.appendChild(messageDiv);
    chat.scrollTop = chat.scrollHeight;
};

sendButton.addEventListener('click', sendMessage);
userInput.addEventListener('keypress', (e) => {
    if (e.key === 'Enter') {
        sendMessage();
        e.preventDefault(); // Prevent the default action to stop form submission
    }
});

}); ```

main.py

```python import logging

from flask import Flask, render_template, session from flask_session import Session from gunicorn.app.base import BaseApplication

logging.basicConfig(level=logging.INFO) logger = logging.getLogger(name)

app = Flask(name)

Configuring server-side session

app.config["SESSION_PERMANENT"] = False app.config["SESSION_TYPE"] = "filesystem" Session(app)

from abilities import llm_prompt from flask import request, jsonify

@app.route("/") def root_route(): return render_template("template.html")

@app.route("/send_message", methods=['POST']) def send_message(): user_message = request.json['message'] # For now, we'll use a placeholder response response = "Response coming soon!" return jsonify({"message": response})

class StandaloneApplication(BaseApplication): def init(self, app, options=None): self.application = app self.options = options or {} super().init()

def load_config(self):
    config = {
        key: value
        for key, value in self.options.items()
        if key in self.cfg.settings and value is not None
    }
    for key, value in config.items():
        self.cfg.set(key.lower(), value)

def load(self):
    return self.application

Do not remove the main function while updating the app.

if name == "main": options = {"bind": "%s:%s" % ("0.0.0.0", "8080"), "workers": 4, "loglevel": "info"} StandaloneApplication(app, options).run() ```

requirements.txt

Flask cachelib flask flask-session gunicorn werkzeug

By following these steps, you will have a fully functional chatbot interface set up and ready to use. If you need to integrate the app with other tools or services, refer to the provided sample code and instructions.

Here are the top 5 business benefits or applications of this chatbot interface template:

Template Benefits

1. Enhanced Customer Support: This template provides a foundation for implementing a 24/7 customer support chatbot, reducing response times and improving customer satisfaction.

2. Cost-Effective Scalability: By automating responses to common queries, businesses can handle a higher volume of customer interactions without proportionally increasing staff, leading to significant cost savings.

3. Consistent Brand Experience: The customizable interface allows businesses to maintain brand consistency across digital touchpoints, reinforcing brand identity and professionalism.

4. Data Collection and Insights: The chatbot can be used to gather valuable customer data and preferences, providing insights for product development and marketing strategies.

5. Improved Lead Generation: By engaging website visitors in real-time conversations, the chatbot can qualify leads more efficiently, potentially increasing conversion rates and sales opportunities.