Simple AI Text Analyzer
import logging
from gunicorn.app.base import BaseApplication
from app_init import create_initialized_flask_app
# Setup logging
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()
class StandaloneApplication(BaseApplication):
def __init__(self, app, options=None):
self.application = app
self.options = options or {}
super().__init__()
def load_config(self):
# Apply configuration to Gunicorn
for key, value in self.options.items():
if key in self.cfg.settings and value is not None:
self.cfg.set(key.lower(), value)
def load(self):
Frequently Asked Questions
How can businesses benefit from using the Simple AI Text Analyzer?
The Simple AI Text Analyzer offers several benefits for businesses: - Sentiment analysis of customer feedback, reviews, and social media mentions - Quick insights into large volumes of text data - Improved customer service by understanding customer emotions - Data-driven decision making for marketing and product development - Real-time monitoring of brand perception
By leveraging this tool, businesses can gain valuable insights into customer sentiment and make informed decisions to improve their products and services.
What industries would find the Simple AI Text Analyzer most useful?
The Simple AI Text Analyzer can be valuable across various industries, including: - E-commerce: Analyzing product reviews and customer feedback - Hospitality: Evaluating guest reviews and comments - Finance: Assessing customer satisfaction with banking services - Healthcare: Analyzing patient feedback and experiences - Media and Entertainment: Gauging audience reactions to content
Any industry that deals with large amounts of text-based customer feedback can benefit from the insights provided by the Simple AI Text Analyzer.
How can I customize the Simple AI Text Analyzer for my specific business needs?
The Simple AI Text Analyzer can be customized in several ways: - Tailoring the sentiment categories to match your industry-specific terminology - Integrating with your existing CRM or data analysis tools - Adding custom visualizations for sentiment trends over time - Implementing industry-specific text classification models - Creating custom reports that align with your business KPIs
By adapting the Simple AI Text Analyzer to your specific needs, you can extract more relevant and actionable insights for your business.
How can I modify the chat functionality in the Simple AI Text Analyzer to handle more complex queries?
To enhance the chat functionality for more complex queries, you can modify the /chat
route in the routes.py
file. Here's an example of how you can extend the existing code:
```python @app.route("/chat", methods=["POST"]) def chat(): data = request.get_json() message = data.get("message", "")
response_schema = {
"type": "object",
"properties": {
"response": {
"type": "string",
"description": "The chatbot's response"
},
"sentiment": {
"type": "string",
"description": "The sentiment of the user's message"
},
"keywords": {
"type": "array",
"items": {"type": "string"},
"description": "Key topics extracted from the message"
}
},
"required": ["response", "sentiment", "keywords"]
}
try:
result = llm(
prompt=f"Analyze this message and provide a response, sentiment, and keywords: {message}",
response_schema=response_schema,
model="gpt-4o",
temperature=0.7
)
return jsonify(result)
except Exception as e:
logger.error(f"Error in chat: {str(e)}")
return jsonify({"error": "Failed to process message"}), 500
```
This modification expands the chat functionality to include sentiment analysis and keyword extraction, providing more comprehensive insights for each user message.
How can I implement a file upload feature in the Simple AI Text Analyzer for batch processing of text documents?
To add a file upload feature for batch processing, you can create a new route in the routes.py
file and add a corresponding HTML template. Here's an example implementation:
In routes.py
:
```python from werkzeug.utils import secure_filename import os
UPLOAD_FOLDER = 'uploads' ALLOWED_EXTENSIONS = {'txt', 'pdf', 'doc', 'docx'}
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
def allowed_file(filename): return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
@app.route('/upload', methods=['GET', 'POST']) def upload_file(): if request.method == 'POST': if 'file' not in request.files: return jsonify({"error": "No file part"}), 400 file = request.files['file'] if file.filename == '': return jsonify({"error": "No selected file"}), 400 if file and allowed_file(file.filename): filename = secure_filename(file.filename) file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) file.save(file_path) # Process the file here (e.g., read content and perform analysis) return jsonify({"message": "File uploaded and processed successfully"}), 200 return render_template('upload.html') ```
Create a new upload.html
template in the templates folder:
```html
{% extends "base.html" %}
{% block content %}
Upload Text File for Analysis
{% endblock %} ```
This implementation allows users to upload text files for batch processing in the Simple AI Text Analyzer, expanding its capabilities for handling larger volumes of text data.
Created: | Last Updated:
Here's a step-by-step guide for using the Simple AI Text Analyzer template:
Introduction
The Simple AI Text Analyzer is a web-based tool that uses AI to provide sentiment insights on text input. This template sets up a Flask application with a chat interface for interacting with an AI model.
Getting Started
To begin using this template:
- Click the "Start with this Template" button in the Lazy Builder interface.
Test the Application
After starting with the template:
- Click the "Test" button in the Lazy Builder interface.
- Wait for the application to deploy and start.
Using the Application
Once the application is running:
- Lazy will provide you with a dedicated server link to access the web interface.
- Open the provided link in your web browser.
- You'll see a chat interface with a welcome message from the INOVECLOUD AI BOT.
- Type your message or text for analysis in the input field at the bottom of the chat.
- Click the "Send" button or press Enter to submit your text.
- The AI will process your input and provide a response with sentiment insights.
Features
The Simple AI Text Analyzer offers:
- A user-friendly chat interface
- AI-powered text analysis
- Sentiment insights on user input
Additional Functionality
The template also includes a file upload feature:
- Click on the "Upload Files" link in the navigation menu.
- You can upload files to the INOVECLOUD storage system.
- Select files by clicking the "Choose files" button or drag and drop files into the designated area.
- Click the "Upload Files" button to start the upload process.
Customization
You can customize this template by modifying the following files:
routes.py
: Adjust the AI model's behavior or add new routes.templates/home.html
andtemplates/upload.html
: Modify the HTML structure of the pages.static/css/styles.css
: Change the appearance of the application.static/js/home.js
andstatic/js/upload.js
: Alter the client-side functionality.
Remember that all changes and deployments are handled through the Lazy platform, so you don't need to worry about server setup or environment configuration.
Template Benefits
-
Enhanced User Experience: The responsive design with both mobile and desktop layouts ensures a seamless experience across devices, potentially increasing user engagement and retention.
-
AI-Powered Customer Support: The chatbot interface allows businesses to provide 24/7 automated customer support, reducing response times and operational costs while maintaining consistent service quality.
-
Scalable File Management: The file upload functionality enables businesses to easily manage and store user-submitted documents, supporting various use cases such as document processing or data analysis.
-
Customizable AI Integration: The template's integration with AI models (like GPT-4) allows businesses to tailor the chatbot's responses to their specific industry or use case, providing more accurate and relevant information to users.
-
Secure and Efficient Backend: The use of Flask, SQLAlchemy, and Gunicorn provides a robust and secure backend structure, ensuring data integrity and efficient handling of user requests, which is crucial for business applications dealing with sensitive information.