by davi

AI Web Chatbot

Test this app for free
10
import logging

from flask import Flask, render_template, session
from flask_session import Session
from gunicorn.app.base import BaseApplication
from abilities import apply_sqlite_migrations

from app_init import create_initialized_flask_app
from models import db

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Flask app creation should be done by create_initialized_flask_app to avoid circular dependency problems.
app = create_initialized_flask_app()

# Configuring server-side session
app.config["SESSION_PERMANENT"] = False
app.config["SESSION_TYPE"] = "filesystem"
Session(app)

from abilities import llm
from flask import request, jsonify
Get full code

Created: | Last Updated:

A flexible chatbot template with Tailwind styling and AI integration.

Web Based Chatbot with LLM

This template provides a modern, responsive chat interface powered by GPT-4o. It features a sleek design with image upload capabilities and real-time message animations.

Getting Started

  • Click "Start with this Template" to begin using this template in the Lazy Builder interface.

Test the Application

  • Click the "Test" button in the Lazy Builder interface.
  • Once deployed, you'll receive a dedicated server link to access your chat interface.

Using the Chat Interface

The chat interface includes several key features:

  • A modern, glass-effect design with smooth animations
  • Real-time message updates
  • Image upload capability through the paperclip icon
  • Responsive design that works on both desktop and mobile devices

To use the chat interface:

  • Type your message in the input field at the bottom
  • Click the paper plane icon or press Enter to send
  • To send an image:
  • Click the paperclip icon
  • Select an image from your device
  • The image will appear in a preview
  • Send your message with or without accompanying text

The chatbot will respond to your messages and can also process and discuss any images you share.

The interface includes: * A status indicator showing the bot is active * A clean, minimalist design * Support for both text and image-based conversations * Smooth message animations * Custom scrollbar for better navigation

This template is ready to use as a standalone chat application and doesn't require any additional integration steps.



Template Benefits

  1. Enhanced Customer Service Automation
  2. Provides 24/7 customer support capability
  3. Reduces support staff workload
  4. Maintains consistent service quality with AI-powered responses

  5. Streamlined Business Communication

  6. Professional chat interface for both internal and external communications
  7. Image sharing capabilities for better context and explanation
  8. Real-time response system with typing indicators

  9. Cost-Effective Implementation

  10. Ready-to-use template reduces development time and costs
  11. Built with modern, free-to-use technologies (Tailwind CSS, Flask)
  12. Scalable architecture suitable for growing businesses

  13. Brand-Aligned User Experience

  14. Customizable interface that can match company branding
  15. Modern, professional design with glass-effect styling
  16. Responsive layout that works across all devices

  17. Secure and Reliable Infrastructure

  18. Built-in session management for user privacy
  19. Database integration for conversation history tracking
  20. File handling system for secure image uploads and storage

Technologies

Enhance HTML Development with Lazy AI: Automate Templates, Optimize Workflows and More Enhance HTML Development with Lazy AI: Automate Templates, Optimize Workflows and More
Streamline JavaScript Workflows with Lazy AI: Automate Development, Debugging, API Integration and More  Streamline JavaScript Workflows with Lazy AI: Automate Development, Debugging, API Integration and More
Maximize OpenAI Potential with Lazy AI: Automate Integrations, Enhance Customer Support and More  Maximize OpenAI Potential with Lazy AI: Automate Integrations, Enhance Customer Support and More
Optimize PDF Workflows with Lazy AI: Automate Document Creation, Editing, Extraction and More Optimize PDF Workflows with Lazy AI: Automate Document Creation, Editing, Extraction and More
Python App Templates for Scraping, Machine Learning, Data Science and More Python App Templates for Scraping, Machine Learning, Data Science and More
Streamline Adobe XD Design with Lazy AI: Websites, Apps, Dashboards and More Streamline Adobe XD Design with Lazy AI: Websites, Apps, Dashboards and More
Flask Templates from Lazy AI – Boost Web App Development with Bootstrap, HTML, and Free Python Flask Flask Templates from Lazy AI – Boost Web App Development with Bootstrap, HTML, and Free Python Flask
Optimize SQL Workflows with Lazy AI: Automate Queries, Reports, Database Management and More Optimize SQL Workflows with Lazy AI: Automate Queries, Reports, Database Management and More

Similar templates

FastAPI endpoint for Text Classification using OpenAI GPT 4

This API will classify incoming text items into categories using the Open AI's GPT 4 model. If the model is unsure about the category of a text item, it will respond with an empty string. The categories are parameters that the API endpoint accepts. The GPT 4 model will classify the items on its own with a prompt like this: "Classify the following item {item} into one of these categories {categories}". There is no maximum number of categories a text item can belong to in the multiple categories classification. The API will use the llm_prompt ability to ask the LLM to classify the item and respond with the category. The API will take the LLM's response as is and will not handle situations where the model identifies multiple categories for a text item in the single category classification. If the model is unsure about the category of a text item in the multiple categories classification, it will respond with an empty string for that item. The API will use Python's concurrent.futures module to parallelize the classification of text items. The API will handle timeouts and exceptions by leaving the items unclassified. The API will parse the LLM's response for the multiple categories classification and match it to the list of categories provided in the API parameters. The API will convert the LLM's response and the categories to lowercase before matching them. The API will split the LLM's response on both ':' and ',' to remove the "Category" word from the response. The temperature of the GPT model is set to a minimal value to make the output more deterministic. The API will return all matching categories for a text item in the multiple categories classification. The API will strip any leading or trailing whitespace from the categories in the LLM's response before matching them to the list of categories provided in the API parameters. The API will accept lists as answers from the LLM. If the LLM responds with a string that's formatted like a list, the API will parse it and match it to the list of categories provided in the API parameters.

Icon 1 Icon 1
130

We found some blogs you might like...