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This video demonstrates how to use the AI Chatbot Website template.
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():Frequently Asked Questions
What kind of businesses can benefit from using this Web Based Chatbot with LLM template?
This Web Based Chatbot with LLM template is versatile and can benefit a wide range of businesses. It's particularly useful for: - Customer service operations looking to automate initial customer interactions - E-commerce platforms wanting to provide 24/7 product information and support - Educational institutions offering an AI tutor for students - Healthcare providers giving preliminary health information or appointment scheduling - Financial services offering basic account information and transaction support
The template's flexibility allows it to be customized for various industries, making it a valuable tool for businesses looking to enhance their customer engagement and support capabilities.
How can this chatbot template improve customer experience?
The Web Based Chatbot with LLM template can significantly enhance customer experience in several ways: - Instant responses: Customers get immediate answers to their queries, reducing wait times. - 24/7 availability: The chatbot can provide support outside of regular business hours. - Consistent information: It ensures that all customers receive uniform and accurate information. - Personalization: With its conversation history feature, the chatbot can provide more contextual and personalized responses over time. - Scalability: It can handle multiple customer interactions simultaneously, reducing bottlenecks during peak times.
By implementing this template, businesses can offer a more responsive and efficient customer service experience, potentially increasing customer satisfaction and loyalty.
What are the potential cost savings for a business implementing this chatbot solution?
Implementing the Web Based Chatbot with LLM template can lead to significant cost savings for businesses: - Reduced staff requirements: The chatbot can handle a large volume of basic inquiries, reducing the need for human customer service representatives. - Lower training costs: Once set up, the chatbot doesn't require ongoing training like human staff would. - Increased efficiency: By handling routine queries, the chatbot frees up human agents to focus on more complex issues, improving overall productivity. - Scalability without proportional cost increase: Unlike hiring more staff, scaling up the chatbot to handle more interactions doesn't significantly increase costs. - Decreased error rates: The chatbot can provide consistent, accurate information, potentially reducing costly mistakes.
While there are initial setup and maintenance costs, many businesses find that the long-term savings and efficiency gains far outweigh these expenses.
How can I customize the appearance of the chatbot interface in this template?
The Web Based Chatbot with LLM template uses Tailwind CSS for styling, making it easy to customize the appearance. You can modify the template.html file to change colors, layouts, and other visual elements. For example, to change the background color of the chat messages, you can modify the class names in the JavaScript file:
```javascript // In script.js userMessage.className = 'p-2 my-2 text-right bg-blue-900 rounded'; // Change to: userMessage.className = 'p-2 my-2 text-right bg-purple-700 rounded';
botMessage.className = 'p-2 my-2 text-left bg-green-900 rounded'; // Change to: botMessage.className = 'p-2 my-2 text-left bg-indigo-600 rounded'; ```
You can also add custom CSS classes or modify the existing Tailwind classes in the HTML file to further customize the look and feel of your chatbot interface.
How can I extend the functionality of the chatbot to include more complex features?
The Web Based Chatbot with LLM template provides a solid foundation that can be extended to include more complex features. Here are a few ways to enhance its functionality:
Created: | Last Updated:
Here's a step-by-step guide on how to use the Web Based Chatbot with LLM template:
Introduction
This template provides a foundation for creating a web-based chatbot using a Large Language Model (LLM). The chatbot features a clean, responsive user interface built with Tailwind CSS and Flask for the backend. It's designed to maintain conversation history and provide contextual responses.
Getting Started
- Click "Start with this Template" to begin using this template in the Lazy Builder interface.
Test the Application
- Press the "Test" button to deploy the application. This will launch the Lazy CLI and start the deployment process.
Using the Chatbot
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Once the deployment is complete, you'll receive a dedicated server link to access the chatbot interface.
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Open the provided link in your web browser. You'll see a simple chat interface with an input field at the bottom.
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Type your message in the input field and press "Send" or hit Enter to submit your query to the chatbot.
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The chatbot will process your input and provide a response based on the conversation context.
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Continue the conversation by sending more messages. The chatbot will maintain the context of up to 10 previous messages (5 from the user and 5 from the bot) to provide more relevant responses.
Customizing the Chatbot
To further customize your chatbot, you can modify the following aspects:
- Update the chatbot's name and branding in the
template.htmlfile. - Adjust the styling by modifying the Tailwind CSS classes in the HTML.
- Fine-tune the LLM's behavior by adjusting parameters in the
send_messageroute inmain.py:python response = llm(prompt=conversation_history, response_schema={"type": "object", "properties": {"response": {"type": "string"}}}, model="gpt-4o", temperature=0.7)["response"]You can experiment with different models or adjust the temperature to control the randomness of responses.
Integrating the Chatbot
This chatbot application runs as a standalone web service. If you want to integrate it into an existing website or application:
- Use the provided server link as the endpoint for your chatbot interactions.
- Send POST requests to the
/send_messageroute with JSON payload containing the user's message: ```python import requests
url = "https://your-chatbot-server-link/send_message" payload = {"message": "User's message here"} response = requests.post(url, json=payload) bot_response = response.json()["message"] ``` 3. Display the bot's response in your application's interface.
By following these steps, you'll have a functional web-based chatbot that you can further customize and integrate into your projects as needed.
Here are 5 key business benefits for this web-based chatbot template:
Template Benefits
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Rapid Deployment of AI-Powered Customer Support: This template allows businesses to quickly implement an AI chatbot for customer service, reducing response times and operational costs while providing 24/7 support.
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Customizable User Interface: With a clean, responsive design using Tailwind CSS, companies can easily customize the look and feel to match their brand, enhancing user experience and brand consistency.
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Scalable Architecture: Built with Flask and Gunicorn, this template provides a solid foundation for scaling the chatbot service to handle increasing user loads, ensuring business continuity as demand grows.
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Intelligent Conversation Management: The implementation of conversation history and context retention allows for more natural, coherent interactions, improving customer satisfaction and engagement.
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Flexible Integration Capabilities: The modular structure of the template makes it easy to integrate with existing systems or expand functionality, such as adding analytics, CRM connections, or multi-language support to meet diverse business needs.
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