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

Frequently Asked Questions

Minimizing error rates in information dissemination Q3: What sets this template apart from other chatbot solutions in terms of business value?

The template offers several unique advantages: - Modern, professional UI with Tailwind CSS - Built-in image processing capabilities - Real-time typing indicators for better user experience - Session management for conversation history - Scalable architecture suitable for enterprise deployment - Easy integration with existing systems through Flask backend

Q4: How can I modify the chat interface to add custom branding?

A: You can customize the template's branding by modifying the HTML and CSS. Here's an example:

```html

Your Brand YOUR·BRAND·CHAT

```

Q5: How can I extend the template to support multiple language models?

A: You can modify the send_message route in main.py to support different models. Here's an example:

```python @app.route("/send_message", methods=['POST']) def send_message(): user_message = request.form.get('message', '') model_type = request.form.get('model', 'gpt-4') # Default model

# Model configuration
model_configs = {
    'gpt-4': {'temperature': 0.7, 'max_tokens': 150},
    'gpt-3.5-turbo': {'temperature': 0.9, 'max_tokens': 100},
    'custom-model': {'temperature': 0.5, 'max_tokens': 200}
}

response = llm(
    prompt=conversation_history,
    response_schema={
        "type": "object",
        "properties": {
            "response": {"type": "string"}
        }
    },
    model=model_type,
    model_configs[model_type]
)

return jsonify({"message": response['response']})

```

Created: | Last Updated:

A flexible chatbot template with Tailwind styling and AI integration.

Web Based Chatbot with LLM Template Guide

This template provides a modern, responsive chat interface powered by LLM technology. The chat interface features a sleek design with message animations, image upload capabilities, and real-time typing indicators.

Getting Started

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

Testing 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 features:

  • A clean, modern design with a dark theme
  • Real-time message animations and typing indicators
  • Image upload capability through the paperclip icon
  • Send messages using either the send button or Enter key
  • 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 the image using the send button

The chatbot will respond to your messages using the configured LLM model, maintaining a conversation history of up to 10 messages for context.

The interface automatically adjusts to different screen sizes and includes features like:

  • Message animations for smooth transitions
  • Typing indicators while waiting for responses
  • Image preview before sending
  • Ability to remove selected images before sending

This template provides a complete chat interface that can be used immediately after deployment, with no additional integration steps required.



Template Benefits

  1. Enhanced Customer Service Automation
  2. Provides a professional-looking chat interface for customer support
  3. Reduces response times and operational costs through automated interactions
  4. Maintains conversation history for context-aware responses

  5. Seamless Multi-Modal Communication

  6. Supports both text and image-based interactions
  7. Enables users to share visual content for better problem description
  8. Professional UI with modern glass-effect design enhances user experience

  9. Enterprise-Ready Architecture

  10. Built with scalable Flask backend architecture
  11. Includes session management for personalized conversations
  12. Features database integration for data persistence and analytics

  13. Cross-Platform Accessibility

  14. Responsive design works across desktop and mobile devices
  15. Modern UI with tailwind CSS ensures consistent styling
  16. Optimized performance with efficient message handling

  17. Easy Integration & Customization

  18. Modular structure allows for easy integration with existing systems
  19. Customizable LLM responses for different business needs
  20. Extensible architecture for adding new features and capabilities

Technologies

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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.

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