Web Based Chatbot with LLM

By
 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():

About this template

This powerful app skeleton is a great starting place suitable for creating a chatbot. It uses tailwind and llm.

Introduction to the Chatbot Template

Welcome to the Chatbot Template guide. This template provides a solid foundation for building a chatbot application. It includes a user-friendly interface and backend logic to handle user messages and respond accordingly. The template uses HTML, CSS (with TailwindCSS for styling), JavaScript for the frontend, and Python with Flask for the backend server.

Getting Started with the Template

To begin using this template, simply click on "Start with this Template" on the Lazy platform. This will set up the template in your Lazy Builder interface, pre-populating the code so you can start customizing and testing your chatbot right away.

Test: Deploying the App

Once you have customized the template to your liking, press the "Test" button. This will deploy your application on the Lazy platform. If the code requires any user input, the Lazy CLI will prompt you to provide it after pressing the "Test" button.

Using the Chatbot App

After deployment, you will be provided with a dedicated server link to interact with your chatbot. The frontend interface allows users to type messages and receive responses from the chatbot. The messages are sent to the backend, processed, and the chatbot's reply is displayed on the screen.

Integrating the Chatbot into Your Service

If you wish to integrate the chatbot into an external service or frontend, you can use the server link provided by Lazy. This link can be added to your service to enable communication between the chatbot and users. If you need to make API calls to the chatbot, you can use the provided endpoint '/send_message' to send user messages and receive chatbot responses.

Here is an example of how you might use the chatbot's API endpoint:


fetch('YOUR_SERVER_LINK/send_message', {
    method: 'POST',
    headers: {
        'Content-Type': 'application/json',
    },
    body: JSON.stringify({ message: 'Hello, chatbot!' }),
})
.then(response => response.json())
.then(data => console.log(data.message))
.catch(error => console.error('Error:', error));

Replace 'YOUR_SERVER_LINK' with the actual server link provided after deploying your app.

If you need to integrate this chatbot with other tools or services, ensure you follow the specific steps required by those tools to add the chatbot's server link or API endpoints.

Remember, no additional setup for the 'abilities' module is required, as it is built into the Lazy platform.

By following these steps, you should have a fully functional chatbot application ready to interact with users and be integrated into your service.

Category
Last published
April 7, 2024

More templates like this

WebChatify

WebChatify: A web-based chat application that allows users to discuss and analyze website content using the llm system and a user-friendly interface powered by tailwind.

React JS

Customisable Server Status Dashboard

This template allows you to create a dashboard to report about incidents regarding your server to your users.

Streamlit
Python

A bot that answers questions about data

Ask questions about your database via a chat bot. This chatbot connects to a SQLITE database, generates queries for it based on the schema and then runs the queries printing the response all in a nicely styled chat UI. This template is pre-loaded with sample data (car parts) to play around with - here are the sample data columns that you can query: part_number, part_name, price, units_left_in_stock, manufacturer .

Python
Flask
Home
/
Web Based Chatbot with LLM