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This video demonstrates how to use the Web Based Chatbot on Flask with LLM 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 are some potential business applications for this Web Based Chatbot with LLM template?
The Web Based Chatbot with LLM template offers versatile applications across various industries. It can be customized for customer support, providing instant responses to common queries. In e-commerce, it can assist with product recommendations and order tracking. For educational institutions, it can serve as a virtual tutor or campus information guide. Healthcare providers could use it for initial patient screening or appointment scheduling. Financial services could implement it for basic account inquiries or financial advice. The template's flexibility allows businesses to tailor the chatbot to their specific needs and enhance customer engagement.
How can this chatbot template improve customer experience and business efficiency?
The Web Based Chatbot with LLM template can significantly enhance customer experience by providing instant, 24/7 support. It reduces wait times and allows customers to get immediate answers to their questions. From a business perspective, this improves efficiency by handling routine inquiries automatically, freeing up human agents to focus on more complex issues. The chatbot can also collect valuable customer data and insights, helping businesses understand their customers better and improve their services. Additionally, the template's use of advanced language models ensures more natural and context-aware conversations, leading to higher customer satisfaction.
What are the cost implications of implementing this chatbot template for a small business?
Implementing the Web Based Chatbot with LLM template can be cost-effective for small businesses. The initial setup costs are minimal as the template provides a ready-to-use structure. The main ongoing cost would be associated with the Language Model API usage, which varies based on the chosen provider and usage volume. However, this is often offset by the reduction in customer service staff hours and improved efficiency. Small businesses can start with a basic implementation and scale as needed. It's important to note that while there might be some upfront costs for customization and integration, the long-term benefits in terms of improved customer service and operational efficiency often outweigh these initial investments.
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 color of the send button, you can modify this line in the HTML:
html
<button id="sendButton" class="w-full mt-2 bg-blue-500 hover:bg-blue-700 text-white font-bold py-2 px-4 rounded">
Send
</button>
You could change bg-blue-500 to bg-green-500 for a green button. Similarly, you can adjust padding, margins, and other properties using Tailwind's utility classes. For more extensive customization, you can add your own CSS file and link it in the <head> section of the HTML.
How can I extend the chatbot's functionality to handle more complex tasks?
To extend the Web Based Chatbot with LLM template for more complex tasks, you can modify the send_message route in main.py. For instance, you could add custom logic to handle specific intents or integrate with external APIs. Here's an example of how you might modify the route to handle a weather inquiry:
```python import requests
@app.route("/send_message", methods=['POST']) def send_message(): user_message = request.json['message'] if 'weather' in user_message.lower(): # Call a weather API weather_data = requests.get('https://api.weatherapi.com/v1/current.json?key=YOUR_API_KEY&q=London').json() response = f"The current weather in London is {weather_data['current']['condition']['text']} with a temperature of {weather_data['current']['temp_c']}°C." else: # Use the LLM for other queries response = llm_prompt(user_message, model="gpt-4-1106-preview", temperature=0.7) return jsonify({"message": response}) ```
This example demonstrates how you can add conditional logic to handle specific types of queries differently, integrating external APIs when necessary. Remember to add any new dependencies to your requirements.txt file.
Created: | Last Updated:
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.
Here are 5 key business benefits for this web-based chatbot template:
Template Benefits
-
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.
-
Customizable User Interface: With a clean, responsive design using Tailwind CSS, companies can easily tailor the chatbot's appearance to match their brand identity, enhancing user experience and brand consistency.
-
Scalable Architecture: Built on Flask with Gunicorn, this template provides a solid foundation for handling high traffic volumes, making it suitable for businesses of all sizes, from startups to large enterprises.
-
Conversation History Management: The implementation of session-based conversation history allows for more contextual and personalized interactions, improving the quality of responses and customer satisfaction.
-
Flexible Integration with Advanced Language Models: The template's design allows for easy integration with various language models, enabling businesses to leverage state-of-the-art AI capabilities for tasks such as lead generation, product recommendations, or technical support.
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