Pt 3 - Text Classification Magic: Transform Raw Text into Emotional Insights

Final part of series on leveraging AI for emotional intelligence In our previous posts, we explored how to train a custom AI model for recognizing emotions in text. Now, we're taking the next step: building a user-friendly web application that turns our powerful backend model into a tool anyone can use—no coding required. From Code to Clarity: Making AI Accessible While AI models are incredibly powerful, they often remain locked behind complex APIs and code. Our goal is to bridge this gap with a Streamlit application that provides an intuitive interface for our emotion classification model. This application allows users to: Input text directly or upload files containing multiple entries Process up to 50 text items simultaneously View immediate classification results and track historical analyses Export results for further processing or reporting How It Works: The User Journey Let's walk through the typical user experience: Setup: Users provide their Cohere API key and optionally specify a custom model ID (in our case, our fine-tuned emotion classifier) Input: Users can type directly into the chat interface or upload files (TXT, CSV, Excel) Classification: With a single click, the application sends the text to our model and retrieves emotional insights Results: The app displays both individual classification details and summary statistics History: All results are saved with timestamps for future reference The Technical Magic Behind the Scenes Our application is built on Streamlit, a Python framework that makes it easy to create data applications. The core functionality revolves around: Flexible Input Processing def process_file_content(file_input): """Extract text content from uploaded file.""" # Code determines file type and extracts text accordingly if file_name.endswith('.txt'): # Process text files elif file_name.endswith('.csv'): # Process CSV files # etc. This function handles various file formats intelligently, extracting text that can be fed to our emotion classifier. Seamless AI Integration The application connects to Cohere's API with minimal code: response = co.classify( inputs=string_inputs, model=model_id ) This single function call sends our text to the fine-tuned model and returns structured classifications. Intelligent Results Management Results are organized into both immediate displays and historical records: # Create results dataframe with full original text and timestamp batch_results = [ { 'text': item.input, 'prediction': item.prediction, 'confidence': round(item.confidence, 4), 'timestamp': current_time } for item in response.classifications ] From Raw Text to Emotional Intelligence What makes this application powerful is how it transforms simple text into emotional insights. For example, a customer service team might upload hundreds of support tickets and quickly identify: Tickets expressing frustration that need immediate attention Overall emotional trends in customer communications Specific product features that trigger negative emotions The emotional impact of recent company changes or announcements Building Your Own Text Classification Interface Want to create a similar application? Here's what you'll need: A Cohere API key - Sign up at cohere.com A fine-tuned model - Either train your own (see our previous post) or use Cohere's pre-trained options Basic Python knowledge - To understand and modify the Streamlit code Streamlit - Install with pip install streamlit The full code for this application is available in our GitHub repository. While we've focused on emotion detection, this same interface can be adapted for: Content moderation (identifying inappropriate text) Customer intent classification (purchase interest, support needs, etc.) Document categorization (automatically sorting documents by type) Language detection (identifying which language is being used) Conclusion: Democratizing AI Access The true power of AI isn't just in its capabilities but in its accessibility. By creating user-friendly interfaces like this Streamlit application, we make sophisticated emotional intelligence available to everyone in an organization—not just the data science team. Build something cool today!

def process_file_content(file_input):
    """Extract text content from uploaded file."""
    # Code determines file type and extracts text accordingly
    if file_name.endswith('.txt'):
        # Process text files
    elif file_name.endswith('.csv'):
        # Process CSV files
    # etc.

response = co.classify(
    inputs=string_inputs,
    model=model_id
)

# Create results dataframe with full original text and timestamp
batch_results = [
    {
        'text': item.input,
        'prediction': item.prediction,
        'confidence': round(item.confidence, 4),
        'timestamp': current_time
    }
    for item in response.classifications
]

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