# app.py
import gradio as gr
import csv
import os
from datetime import datetime
from classifier import classify_toxic_comment

def clear_inputs():
    """
    Reset all UI input and output fields to their default values.
    Returns a tuple of empty or default values for all UI components.
    """
    return "", 0, "", [], "", "", "", "", 0, "", "", "", "", ""  # Updated to 14 values

custom_css = """
/* General Styling */
body {
    font-family: 'Roboto', sans-serif;
    background-color: #F5F7FA;
    color: #333333;
}

/* Header Styling */
h1 {
    color: #FFFFFF !important;
    background-color: #1E88E5;
    padding: 20px;
    border-radius: 10px;
    text-align: center;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
    margin-bottom: 20px;
}

/* Section Headers */
h3 {
    color: #1E88E5;
    font-weight: 600;
    margin-bottom: 15px;
    border-bottom: 2px solid #1E88E5;
    padding-bottom: 5px;
}

/* Input Textbox */
.gr-textbox textarea {
    border: 2px solid #1E88E5 !important;
    border-radius: 10px !important;
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
    transition: border-color 0.3s, box-shadow 0.3s;
}
.gr-textbox textarea:focus {
    border-color: #1565C0 !important;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15) !important;
}

/* Buttons */
.gr-button-primary {
    background-color: #1E88E5 !important;
    color: white !important;
    border-radius: 10px !important;
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
    transition: background-color 0.3s, transform 0.1s;
    font-weight: 500;
}
.gr-button-primary:hover {
    background-color: #1565C0 !important;
    transform: translateY(-2px);
}
.gr-button-secondary {
    background-color: #D32F2F !important;
    color: white !important;
    border-radius: 10px !important;
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
    transition: background-color 0.3s, transform 0.1s;
    font-weight: 500;
}
.gr-button-secondary:hover {
    background-color: #B71C1C !important;
    transform: translateY(-2px);
}

/* Sliders */
.gr-slider {
    background-color: #E0E0E0 !important;
    border-radius: 10px !important;
    box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.1);
}

/* Output Boxes */
.gr-textbox {
    border: 1px solid #E0E0E0 !important;
    border-radius: 10px !important;
    background-color: #FFFFFF !important;
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
    padding: 10px;
    margin-bottom: 10px;
}

/* Accordion */
.gr-accordion {
    border: 1px solid #E0E0E0 !important;
    border-radius: 10px !important;
    background-color: #FFFFFF !important;
    margin-bottom: 15px;
}

/* Custom Classes for Visual Indicators */
.toxic-indicator::before {
    content: "⚠️ ";
    color: #D32F2F;
    font-size: 20px;
}
.nontoxic-indicator::before {
    content: "✅ ";
    color: #388E3C;
    font-size: 20px;
}

/* Loading State Animation */
@keyframes pulse {
    0% { opacity: 1; }
    50% { opacity: 0.5; }
    100% { opacity: 1; }
}
.loading {
    animation: pulse 1.5s infinite;
}
"""

with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
    gr.Markdown(
        """
        # Toxic Comment Classifier
        Enter a comment below to check if it's toxic or non-toxic. This app uses a fine-tuned XLM-RoBERTa model to classify comments, paraphrases toxic comments, and evaluates the output with advanced metrics.
        """
    )

    with gr.Row():
        with gr.Column(scale=4, min_width=600):
            comment_input = gr.Textbox(
                label="Your Comment",
                placeholder="Type your comment here...",
                lines=3,
                max_lines=5
            )
        with gr.Column(scale=1, min_width=200):
            submit_btn = gr.Button("Classify Comment", variant="primary")
            clear_btn = gr.Button("Clear", variant="secondary")

    gr.Examples(
        examples=[
            "I love this community, it's so supportive!",
            "You are an idiot and should leave this platform.",
            "This app is amazing, great work!"
        ],
        inputs=comment_input,
        label="Try these examples:"
    )

    with gr.Row():
        with gr.Column(scale=1, min_width=400):
            gr.Markdown("### Original Comment Analysis")
            prediction_output = gr.Textbox(label="Prediction", placeholder="Prediction will appear here...")
            label_display = gr.HTML()
            confidence_output = gr.Slider(
                label="Confidence",
                minimum=0,
                maximum=1,
                value=0,
                interactive=False
            )
            toxicity_output = gr.Textbox(label="Toxicity Score", placeholder="Toxicity score will appear here...")
            bias_output = gr.Textbox(label="Bias Score", placeholder="Bias score will appear here...")
            threshold_display = gr.HTML()

        with gr.Column(scale=1, min_width=400):
            with gr.Accordion("Paraphrased Output (if Toxic)", open=False):
                paraphrased_comment_output = gr.Textbox(label="Paraphrased Comment", placeholder="Paraphrased comment will appear here if the input is toxic...")
                paraphrased_prediction_output = gr.Textbox(label="Paraphrased Prediction", placeholder="Prediction will appear here...")
                paraphrased_label_display = gr.HTML()
                paraphrased_confidence_output = gr.Slider(
                    label="Paraphrased Confidence",
                    minimum=0,
                    maximum=1,
                    value=0,
                    interactive=False
                )
                paraphrased_toxicity_output = gr.Textbox(label="Paraphrased Toxicity Score", placeholder="Toxicity score will appear here...")
                paraphrased_bias_output = gr.Textbox(label="Paraphrased Bias Score", placeholder="Bias score will appear here...")
                semantic_similarity_output = gr.Textbox(label="Semantic Similarity", placeholder="Semantic similarity score will appear here...")
                empathy_score_output = gr.Textbox(label="Empathy Score", placeholder="Empathy score will appear here...")

    with gr.Row():
        with gr.Column(scale=1):
            with gr.Accordion("Prediction History", open=False):
                history_output = gr.JSON(label="Previous Predictions")

        with gr.Column(scale=1):
            with gr.Accordion("Provide Feedback", open=False):
                feedback_input = gr.Radio(
                    choices=["Yes, the prediction was correct", "No, the prediction was incorrect"],
                    label="Was this prediction correct?"
                )
                feedback_comment = gr.Textbox(label="Additional Comments (optional)", placeholder="Let us know your thoughts...")
                feedback_submit = gr.Button("Submit Feedback")
                feedback_output = gr.Textbox(label="Feedback Status")

    with gr.Row():
        with gr.Column():
            refine_btn = gr.Button("Run Iterative Refinement (Stage 4)", variant="primary")
            refine_status = gr.Textbox(label="Refinement Status", placeholder="Status will appear here...")

    def handle_classification(comment, history):
        if history is None:
            history = []
        (
            prediction, confidence, color, toxicity_score, bias_score,
            paraphrased_comment, paraphrased_prediction, paraphrased_confidence,
            paraphrased_color, paraphrased_toxicity_score, paraphrased_bias_score,
            semantic_similarity, empathy_score
        ) = classify_toxic_comment(comment)
        
        history.append({
            "comment": comment,
            "prediction": prediction,
            "confidence": confidence,
            "toxicity_score": toxicity_score,
            "bias_score": bias_score,
            "paraphrased_comment": paraphrased_comment,
            "paraphrased_prediction": paraphrased_prediction,
            "paraphrased_confidence": paraphrased_confidence,
            "paraphrased_toxicity_score": paraphrased_toxicity_score,
            "paraphrased_bias_score": paraphrased_bias_score,
            "semantic_similarity": semantic_similarity,
            "empathy_score": empathy_score
        })
        
        threshold_message = "High Confidence" if confidence >= 0.7 else "Low Confidence"
        threshold_color = "green" if confidence >= 0.7 else "orange"
        toxicity_display = f"{toxicity_score} (Scale: 0 to 1, lower is less toxic)" if toxicity_score is not None else "N/A"
        bias_display = f"{bias_score} (Scale: 0 to 1, lower indicates less bias)" if bias_score is not None else "N/A"
        
        paraphrased_comment_display = paraphrased_comment if paraphrased_comment else "N/A (Comment was non-toxic)"
        paraphrased_prediction_display = paraphrased_prediction if paraphrased_prediction else "N/A"
        paraphrased_confidence_display = paraphrased_confidence if paraphrased_confidence else 0
        paraphrased_toxicity_display = f"{paraphrased_toxicity_score} (Scale: 0 to 1, lower is less toxic)" if paraphrased_toxicity_score is not None else "N/A"
        paraphrased_bias_display = f"{paraphrased_bias_score} (Scale: 0 to 1, lower indicates less bias)" if paraphrased_bias_score is not None else "N/A"
        paraphrased_label_html = (
            f"<span class='{'toxic-indicator' if 'Toxic' in paraphrased_prediction else 'nontoxic-indicator'}' "
            f"style='color: {paraphrased_color}; font-size: 20px; font-weight: bold;'>{paraphrased_prediction}</span>"
            if paraphrased_prediction else ""
        )
        semantic_similarity_display = f"{semantic_similarity} (Scale: 0 to 1, higher is better)" if semantic_similarity is not None else "N/A"
        empathy_score_display = f"{empathy_score} (Scale: 0 to 1, higher indicates more empathy)" if empathy_score is not None else "N/A"

        prediction_class = "toxic-indicator" if "Toxic" in prediction else "nontoxic-indicator"
        prediction_html = f"<span class='{prediction_class}' style='color: {color}; font-size: 20px; font-weight: bold;'>{prediction}</span>"

        return (
            prediction, confidence, prediction_html, history, threshold_message, threshold_color,
            toxicity_display, bias_display,
            paraphrased_comment_display, paraphrased_prediction_display, paraphrased_confidence_display,
            paraphrased_toxicity_display, paraphrased_bias_display, paraphrased_label_html,
            semantic_similarity_display, empathy_score_display
        )

    def handle_feedback(feedback, additional_comment, comment, prediction, confidence):
        """
        Handle user feedback and store it in a CSV file.
        """
        if not feedback:
            return "Please select a feedback option before submitting."

        # Define the CSV file path
        csv_file_path = "/home/user/app/feedback.csv"

        # Check if the CSV file exists; if not, create it with headers
        file_exists = os.path.isfile(csv_file_path)
        with open(csv_file_path, mode='a', newline='', encoding='utf-8') as csv_file:
            fieldnames = ['timestamp', 'comment', 'prediction', 'confidence', 'feedback', 'additional_comment']
            writer = csv.DictWriter(csv_file, fieldnames=fieldnames)

            if not file_exists:
                writer.writeheader()  # Write headers if the file is newly created

            # Write the feedback to the CSV
            writer.writerow({
                'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
                'comment': comment,
                'prediction': prediction,
                'confidence': confidence,
                'feedback': feedback,
                'additional_comment': additional_comment if additional_comment else "N/A"
            })

        return f"Thank you for your feedback: {feedback}\nAdditional comment: {additional_comment if additional_comment else 'None'}\nFeedback has been saved."

    def run_refinement():
        try:
            from refine_paraphrases import main
            main()
            return "Refinement complete. Results saved to iterated_paraphrases.csv and pushed to JanviMl/toxi_iterated_paraphrases."
        except Exception as e:
            return f"Error running refinement: {str(e)}"

    submit_btn.click(
        fn=lambda: (
            "Classifying... <span class='loading'>⏳</span>", 0, "", None, "", "", 
            "Calculating... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>", 
            "Paraphrasing... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>", 0, 
            "Calculating... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>", "", 
            "Calculating... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>"
        ),
        inputs=[],
        outputs=[
            prediction_output, confidence_output, label_display, history_output, threshold_display, threshold_display,
            toxicity_output, bias_output,
            paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
            paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
            semantic_similarity_output, empathy_score_output
        ]
    ).then(
        fn=handle_classification,
        inputs=[comment_input, history_output],
        outputs=[
            prediction_output, confidence_output, label_display, history_output, threshold_display, threshold_display,
            toxicity_output, bias_output,
            paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
            paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
            semantic_similarity_output, empathy_score_output
        ]
    ).then(
        fn=lambda prediction, confidence, html: html,
        inputs=[prediction_output, confidence_output, label_display],
        outputs=label_display
    ).then(
        fn=lambda threshold_message, threshold_color: f"<span style='color: {threshold_color}; font-size: 16px;'>{threshold_message}</span>",
        inputs=[threshold_display, threshold_display],
        outputs=threshold_display
    )

    feedback_submit.click(
        fn=handle_feedback,
        inputs=[feedback_input, feedback_comment, comment_input, prediction_output, confidence_output],
        outputs=feedback_output
    )

    clear_btn.click(
        fn=clear_inputs,
        inputs=[],
        outputs=[
            comment_input, confidence_output, label_display, history_output, toxicity_output, bias_output,
            paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
            paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
            semantic_similarity_output, empathy_score_output
        ]
    )

    refine_btn.click(
        fn=run_refinement,
        inputs=[],
        outputs=[refine_status]
    )

    gr.Markdown(
        """
        ---
        **About**: This app is part of a four-stage pipeline for automated toxic comment moderation with emotional intelligence via RLHF. Built with ❤️ using Hugging Face and Gradio.
        """
    )

demo.launch()