Web Based Chatbot with LLM

 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.

How to Build a Web-Based Chatbot with LLM Using a Lazy Template

This guide will walk you through the process of creating a web-based chatbot using the Lazy platform. The template provided is a great starting point for builders who want to create a chatbot service with a user-friendly interface and backend integration with an LLM (Large Language Model).

Starting with the Template

  • Click on "Start with this Template" on the Lazy platform to initialize your project with the pre-populated code.

Testing the Application

  • Press the "Test" button on the Lazy platform to begin the deployment of your chatbot application.

Entering User Input

  • If the template requires user input, the Lazy CLI will prompt you for it after you press the "Test" button. Follow the instructions in the CLI to provide the necessary input.

Using the Chatbot Interface

  • Once the application is deployed, you will be provided with a dedicated server link to access the chatbot interface.
  • Interact with the chatbot by typing your message and clicking the "Send" button or pressing "Enter" on your keyboard.

Integrating the Chatbot into Your Service

  • If you need to integrate the chatbot with other services or frontends, use the server link provided by Lazy to connect to the chatbot's API.
  • For example, to integrate the chatbot into your website, you can embed the chat interface or make API calls to the chatbot service from your site's backend.

Below is a sample request you might send to the chatbot API:


{
  "message": "Hello, chatbot!"
}

And here is an example of a response you might receive:


{
  "message": "Hello! How can I assist you today?"
}

Remember, the Lazy platform handles all the deployment details, so you don't need to worry about setting up your environment or installing libraries. Just follow the steps above to get your chatbot up and running!

If you need further assistance or have any questions, refer to the documentation provided in the template or reach out to the Lazy support team.

Category
Last published
July 20, 2024

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Web Based Chatbot with LLM