 
from flask import Flask, render_template, request, jsonify
import os
from gunicorn.app.base import BaseApplication
from logging_config import configure_logging

app = Flask(__name__)

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

@app.route("/send_message", methods=['POST'])
def send_message():
    from abilities import llm_prompt
    user_message = request.json['message']
    context = request.json.get('context', '')
    # Ensure context is passed correctly and maintain chat history
    prompt = f"{context}\\nUser: {user_message}\\nAI:"
    response = llm_prompt(prompt, model="gpt-4-1106-preview", temperature=0.7)
    return jsonify({"message": response})

class StandaloneApplication(BaseApplication):
    def __init__(self, app, options=None):

About this template

A chat interface where users can chat with an AI using the llm ability package on Lazy. This Flask website is meant to simulate a store with dummy data and an AI assistant that a user can talk to about anything using the chat floating button on the bottom right of the page. The chatbox maintains chat history and generates replies with the context of the chat.

Introduction to the Add Chatbot to a Website Template

This template provides a fully functional chat interface that can be integrated into a website to allow users to interact with an AI chatbot. The chatbot is designed to simulate a store's AI assistant, providing users with information and answering inquiries in real-time. The template includes a Flask backend, HTML/CSS for the frontend, and JavaScript to handle chat interactions.

Getting Started

To begin using this template, click on "Start with this Template" on the Lazy platform. This will pre-populate the code in the Lazy Builder interface, so you won't need to copy or paste any code manually.

Initial Setup

There is no need to set up environment secrets for this template as it does not require any environment variables beyond what is built into the Lazy platform.

Test: Deploying the App

Once you have the template loaded, press the "Test" button to start the deployment process. The Lazy CLI will handle the deployment, and you won't need to install any libraries or set up your environment.

Entering Input

If the template requires user input, the Lazy App's CLI interface will prompt you to provide it after pressing the "Test" button. Follow the prompts to enter any necessary information.

Using the App

After deployment, Lazy will provide you with a dedicated server link to access your chatbot-enabled website. You can interact with the chatbot by clicking the "Chat" button on the bottom right of the page, which will open the chat interface. Here, you can type messages and receive responses from the AI.

Integrating the App

If you wish to integrate this chatbot into an existing website or service, you will need to include the provided HTML, CSS, and JavaScript code into your website's codebase. Ensure that the paths to the CSS and JavaScript files are correct and that your server is set up to serve these static files.

For the backend, you will need to host the Flask application on a server. If you're using Lazy, this is handled for you, and you will receive a server link after deployment. If you need to integrate the chatbot's API into an external tool, use the server link provided by Lazy to make POST requests to the /send_message endpoint.

Here is a sample POST request you might make to the chatbot API:


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));

And a sample response from the API might look like this:


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

Remember to replace 'YOUR_SERVER_LINK' with the actual server link provided by Lazy.

If you need to make any changes to the chatbot's behavior or appearance, you can modify the HTML, CSS, and JavaScript files accordingly. The Flask application can also be customized by editing main.py.

For further guidance, refer to any documentation links provided in the code comments.

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