by davi
AI Web Chatbot
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
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

```
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:
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
- Enhanced Customer Service Automation
- Provides a professional-looking chat interface for customer support
- Reduces response times and operational costs through automated interactions
-
Maintains conversation history for context-aware responses
-
Seamless Multi-Modal Communication
- Supports both text and image-based interactions
- Enables users to share visual content for better problem description
-
Professional UI with modern glass-effect design enhances user experience
-
Enterprise-Ready Architecture
- Built with scalable Flask backend architecture
- Includes session management for personalized conversations
-
Features database integration for data persistence and analytics
-
Cross-Platform Accessibility
- Responsive design works across desktop and mobile devices
- Modern UI with tailwind CSS ensures consistent styling
-
Optimized performance with efficient message handling
-
Easy Integration & Customization
- Modular structure allows for easy integration with existing systems
- Customizable LLM responses for different business needs
- Extensible architecture for adding new features and capabilities
Technologies





