Simple AI Text Analyzer

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.