import gradio as gr
import logging

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


def process_ngram_analysis(analysis_results, prompt, analyses):
    """
    Process N-gram 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
    ngram_results = analyses["ngram_analysis"]
    models = ngram_results.get("models", [])
    ngram_size = ngram_results.get("ngram_size", 2)
    size_name = "Unigrams" if ngram_size == 1 else f"{ngram_size}-grams"

    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"### {size_name} Analysis: Comparing responses from {models[0]} and {models[1]}"

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

    # Format important n-grams for each model
    important_ngrams = ngram_results.get("important_ngrams", {})

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

    if model1_name in important_ngrams:
        model1_title_visible = True
        model1_title_value = f"#### Top {size_name} Used by {model1_name}"

        ngram_list = [f"**{item['ngram']}** ({item['count']})" for item in important_ngrams[model1_name][:10]]
        model1_words_visible = True
        model1_words_value = ", ".join(ngram_list)

    model2_title_visible = False
    model2_title_value = ""
    model2_words_visible = False
    model2_words_value = ""

    if model2_name in important_ngrams:
        model2_title_visible = True
        model2_title_value = f"#### Top {size_name} Used by {model2_name}"

        ngram_list = [f"**{item['ngram']}** ({item['count']})" for item in important_ngrams[model2_name][:10]]
        model2_words_visible = True
        model2_words_value = ", ".join(ngram_list)

    similarity_title_visible = False
    similarity_metrics_visible = False
    similarity_metrics_value = ""

    # Format similarity metrics if available
    if "comparisons" in ngram_results:
        comparison_key = f"{model1_name} vs {model2_name}"

        if comparison_key in ngram_results["comparisons"]:
            metrics = ngram_results["comparisons"][comparison_key]
            common_count = metrics.get("common_ngram_count", 0)

            similarity_title_visible = True
            similarity_metrics_visible = True
            similarity_metrics_value = f"""
            - **Common {size_name}**: {common_count} {size_name.lower()} appear in both responses
            """

    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=similarity_title_visible),  # similarity_metrics_title
        gr.update(visible=similarity_metrics_visible, value=similarity_metrics_value),  # similarity_metrics
        False,  # status_message_visible
        gr.update(visible=False),  # status_message
        gr.update(visible=False)  # bias_visualizations - Not visible for N-gram analysis
    )