import gradio as gr
import logging

# Set up logging
logger = logging.getLogger('gradio_app.processors.classifier')


def process_classifier_analysis(analysis_results, prompt, analyses):
    """
    Process Classifier analysis and return UI updates

    Args:
        analysis_results (dict): Complete analysis results
        prompt (str): The prompt being analyzed
        analyses (dict): Analysis data for the prompt

    Returns:
        tuple: UI component updates
    """
    visualization_area_visible = True
    classifier_results = analyses["classifier"]
    models = classifier_results.get("models", [])

    if len(models) < 2:
        from analysis_runner import default_no_visualization
        return default_no_visualization(analysis_results)

    prompt_title_visible = True
    prompt_title_value = f"## Analysis of Prompt: \"{prompt[:100]}...\""

    models_compared_visible = True
    models_compared_value = f"### Classifier Analysis for {models[0]} and {models[1]}"

    # Extract and format classifier information
    model1_name = models[0]
    model2_name = models[1]

    # Display classifications for each model
    classifications = classifier_results.get("classifications", {})

    model1_title_visible = False
    model1_title_value = ""
    model1_words_visible = False
    model1_words_value = ""

    if classifications:
        model1_title_visible = True
        model1_title_value = f"#### Classification Results"
        model1_words_visible = True

        model1_results = classifications.get(model1_name, {})
        model2_results = classifications.get(model2_name, {})

        model1_words_value = f"""
        **{model1_name}**:
        - Formality: {model1_results.get('formality', 'N/A')}
        - Sentiment: {model1_results.get('sentiment', 'N/A')}
        - Complexity: {model1_results.get('complexity', 'N/A')}

        **{model2_name}**:
        - Formality: {model2_results.get('formality', 'N/A')}
        - Sentiment: {model2_results.get('sentiment', 'N/A')}
        - Complexity: {model2_results.get('complexity', 'N/A')}
        """

    # Show comparison
    model2_title_visible = False
    model2_title_value = ""
    model2_words_visible = False
    model2_words_value = ""

    differences = classifier_results.get("differences", {})
    if differences:
        model2_title_visible = True
        model2_title_value = f"#### Classification Comparison"
        model2_words_visible = True

        model2_words_value = "\n".join([
            f"- **{category}**: {diff}"
            for category, diff in differences.items()
        ])

    return (
        analysis_results,  # analysis_results_state
        False,  # analysis_output visibility
        True,  # visualization_area_visible
        gr.update(visible=True),  # analysis_title
        gr.update(visible=prompt_title_visible, value=prompt_title_value),  # prompt_title
        gr.update(visible=models_compared_visible, value=models_compared_value),  # models_compared
        gr.update(visible=model1_title_visible, value=model1_title_value),  # model1_title
        gr.update(visible=model1_words_visible, value=model1_words_value),  # model1_words
        gr.update(visible=model2_title_visible, value=model2_title_value),  # model2_title
        gr.update(visible=model2_words_visible, value=model2_words_value),  # model2_words
        gr.update(visible=False),  # similarity_metrics_title
        gr.update(visible=False),  # similarity_metrics
        False,  # status_message_visible
        gr.update(visible=False),  # status_message
        gr.update(visible=False)  # bias_visualizations - Not visible for Classifier analysis
    )