import gradio as gr
import logging

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


def process_bow_analysis(analysis_results, prompt, analyses):
    """
    Process Bag of Words 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
    bow_results = analyses["bag_of_words"]
    models = bow_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"### Comparing responses from {models[0]} and {models[1]}"

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

    # Format important words for each model
    important_words = bow_results.get("important_words", {})

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

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

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

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

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

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

    similarity_title_visible = False
    similarity_metrics_visible = False
    similarity_metrics_value = ""

    # Format similarity metrics
    comparisons = bow_results.get("comparisons", {})
    comparison_key = f"{model1_name} vs {model2_name}"

    if comparison_key in comparisons:
        metrics = comparisons[comparison_key]
        cosine = metrics.get("cosine_similarity", 0)
        jaccard = metrics.get("jaccard_similarity", 0)
        semantic = metrics.get("semantic_similarity", 0)
        common_words = metrics.get("common_word_count", 0)

        similarity_title_visible = True
        similarity_metrics_visible = True
        similarity_metrics_value = f"""
        - **Cosine Similarity**: {cosine:.2f} (higher means more similar word frequency patterns)
        - **Jaccard Similarity**: {jaccard:.2f} (higher means more word overlap)
        - **Semantic Similarity**: {semantic:.2f} (higher means more similar meaning)
        - **Common Words**: {common_words} words 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 BoW analysis
    )