import gradio as gr
import logging
import traceback
import matplotlib.pyplot as plt
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import io
import base64

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


def process_bias_detection(analysis_results, prompt, analyses):
    """
    Process Bias Detection 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
    """
    logger.info("Processing Bias Detection visualization")

    models = analyses["bias_detection"].get("models", ["Model 1", "Model 2"])
    logger.info(f"Bias models: {models}")

    try:
        # Get the bias detection results
        bias_results = analyses["bias_detection"]

        # Create markdown text for bias analysis results
        results_markdown = f"""
## Bias Analysis Results

### Sentiment Analysis
- {models[0]}: {bias_results[models[0]]['sentiment']['bias_direction']} (strength: {bias_results[models[0]]['sentiment']['bias_strength']:.2f})
- {models[1]}: {bias_results[models[1]]['sentiment']['bias_direction']} (strength: {bias_results[models[1]]['sentiment']['bias_strength']:.2f})
- Difference: {bias_results['comparative']['sentiment']['difference']:.2f}

### Partisan Leaning
- {models[0]}: {bias_results[models[0]]['partisan']['leaning']} (score: {bias_results[models[0]]['partisan']['lean_score']:.2f}) 
- {models[1]}: {bias_results[models[1]]['partisan']['leaning']} (score: {bias_results[models[1]]['partisan']['lean_score']:.2f})
- Difference: {bias_results['comparative']['partisan']['difference']:.2f}

### Framing Analysis
- {models[0]} dominant frame: {bias_results[models[0]]['framing']['dominant_frame']}
- {models[1]} dominant frame: {bias_results[models[1]]['framing']['dominant_frame']}
- Different frames: {'Yes' if bias_results['comparative']['framing']['different_frames'] else 'No'}

### Liberal Terms Found
- {models[0]}: {', '.join(bias_results[models[0]]['partisan']['liberal_terms'][:10])}
- {models[1]}: {', '.join(bias_results[models[1]]['partisan']['liberal_terms'][:10])}

### Conservative Terms Found
- {models[0]}: {', '.join(bias_results[models[0]]['partisan']['conservative_terms'][:10])}
- {models[1]}: {', '.join(bias_results[models[1]]['partisan']['conservative_terms'][:10])}

### Overall Comparison
The overall bias difference is {bias_results['comparative']['overall']['difference']:.2f}, which is 
{'significant' if bias_results['comparative']['overall']['significant_bias_difference'] else 'not significant'}.
"""

        # Return the expected components
        return (
            analysis_results,  # analysis_results_state
            False,  # analysis_output visibility
            True,  # visualization_area_visible
            gr.update(visible=True),  # analysis_title
            gr.update(visible=True, value=f"## Analysis of Prompt: \"{prompt[:100]}...\""),  # prompt_title
            gr.update(visible=True, value=f"### Comparing responses from {models[0]} and {models[1]}"),  # models_compared
            gr.update(visible=True, value="#### Bias detection visualization is available below"),  # model1_title
            gr.update(visible=True, value="The detailed bias analysis includes sentiment analysis, partisan term detection, and framing analysis."),  # model1_words
            gr.update(visible=False),  # model2_title
            gr.update(visible=False),  # 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=True, value=results_markdown)  # bias_visualizations - Pass markdown content
        )

    except Exception as e:
        logger.error(f"Error generating bias visualization: {str(e)}\n{traceback.format_exc()}")

        return (
            analysis_results,
            True,  # Show raw JSON for debugging
            False,
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            True,
            gr.update(visible=True, value=f"❌ **Error generating bias visualization:** {str(e)}"),
            gr.update(visible=False)  # bias_visualizations
        )