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Update app.py
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app.py
CHANGED
@@ -7,7 +7,7 @@ def clear_inputs():
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Reset all UI input and output fields to their default values.
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Returns a tuple of empty or default values for all UI components.
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"""
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return "", 0, "", [], "", "", "", "", 0, "", "", "", ""
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custom_css = """
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/* General Styling */
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@@ -185,7 +185,6 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
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paraphrased_bias_output = gr.Textbox(label="Paraphrased Bias Score", placeholder="Bias score will appear here...")
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semantic_similarity_output = gr.Textbox(label="Semantic Similarity", placeholder="Semantic similarity score will appear here...")
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empathy_score_output = gr.Textbox(label="Empathy Score", placeholder="Empathy score will appear here...")
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rouge_scores_output = gr.Textbox(label="ROUGE Scores", placeholder="ROUGE scores will appear here...")
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with gr.Row():
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with gr.Column(scale=1):
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@@ -209,7 +208,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
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prediction, confidence, color, toxicity_score, bias_score,
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paraphrased_comment, paraphrased_prediction, paraphrased_confidence,
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paraphrased_color, paraphrased_toxicity_score, paraphrased_bias_score,
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semantic_similarity, empathy_score
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) = classify_toxic_comment(comment)
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history.append({
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@@ -224,8 +223,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
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"paraphrased_toxicity_score": paraphrased_toxicity_score,
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"paraphrased_bias_score": paraphrased_bias_score,
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"semantic_similarity": semantic_similarity,
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"empathy_score": empathy_score
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"rouge_scores": rouge_scores
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})
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threshold_message = "High Confidence" if confidence >= 0.7 else "Low Confidence"
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@@ -245,10 +243,6 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
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)
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semantic_similarity_display = f"{semantic_similarity} (Scale: 0 to 1, higher is better)" if semantic_similarity is not None else "N/A"
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empathy_score_display = f"{empathy_score} (Scale: 0 to 1, higher indicates more empathy)" if empathy_score is not None else "N/A"
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rouge_scores_display = (
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f"ROUGE-1: {rouge_scores['rouge1']}, ROUGE-2: {rouge_scores['rouge2']}, ROUGE-L: {rouge_scores['rougeL']}"
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if rouge_scores else "N/A"
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)
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prediction_class = "toxic-indicator" if "Toxic" in prediction else "nontoxic-indicator"
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prediction_html = f"<span class='{prediction_class}' style='color: {color}; font-size: 20px; font-weight: bold;'>{prediction}</span>"
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@@ -258,7 +252,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
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toxicity_display, bias_display,
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paraphrased_comment_display, paraphrased_prediction_display, paraphrased_confidence_display,
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paraphrased_toxicity_display, paraphrased_bias_display, paraphrased_label_html,
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semantic_similarity_display, empathy_score_display
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)
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def handle_feedback(feedback, comment):
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@@ -270,7 +264,6 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
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"Calculating... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>",
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"Paraphrasing... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>", 0,
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"Calculating... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>", "",
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"Calculating... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>",
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"Calculating... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>"
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),
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inputs=[],
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@@ -279,7 +272,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
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toxicity_output, bias_output,
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paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
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paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
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semantic_similarity_output, empathy_score_output
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]
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).then(
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fn=handle_classification,
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@@ -289,7 +282,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
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toxicity_output, bias_output,
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paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
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paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
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semantic_similarity_output, empathy_score_output
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]
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).then(
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fn=lambda prediction, confidence, html: html,
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@@ -314,7 +307,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
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comment_input, confidence_output, label_display, history_output, toxicity_output, bias_output,
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paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
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paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
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semantic_similarity_output, empathy_score_output
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]
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)
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Reset all UI input and output fields to their default values.
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Returns a tuple of empty or default values for all UI components.
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"""
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return "", 0, "", [], "", "", "", "", 0, "", "", "", ""
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custom_css = """
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/* General Styling */
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paraphrased_bias_output = gr.Textbox(label="Paraphrased Bias Score", placeholder="Bias score will appear here...")
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semantic_similarity_output = gr.Textbox(label="Semantic Similarity", placeholder="Semantic similarity score will appear here...")
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empathy_score_output = gr.Textbox(label="Empathy Score", placeholder="Empathy score will appear here...")
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with gr.Row():
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with gr.Column(scale=1):
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prediction, confidence, color, toxicity_score, bias_score,
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paraphrased_comment, paraphrased_prediction, paraphrased_confidence,
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paraphrased_color, paraphrased_toxicity_score, paraphrased_bias_score,
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semantic_similarity, empathy_score
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) = classify_toxic_comment(comment)
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history.append({
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"paraphrased_toxicity_score": paraphrased_toxicity_score,
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"paraphrased_bias_score": paraphrased_bias_score,
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"semantic_similarity": semantic_similarity,
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"empathy_score": empathy_score
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})
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threshold_message = "High Confidence" if confidence >= 0.7 else "Low Confidence"
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)
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semantic_similarity_display = f"{semantic_similarity} (Scale: 0 to 1, higher is better)" if semantic_similarity is not None else "N/A"
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empathy_score_display = f"{empathy_score} (Scale: 0 to 1, higher indicates more empathy)" if empathy_score is not None else "N/A"
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prediction_class = "toxic-indicator" if "Toxic" in prediction else "nontoxic-indicator"
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prediction_html = f"<span class='{prediction_class}' style='color: {color}; font-size: 20px; font-weight: bold;'>{prediction}</span>"
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toxicity_display, bias_display,
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paraphrased_comment_display, paraphrased_prediction_display, paraphrased_confidence_display,
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paraphrased_toxicity_display, paraphrased_bias_display, paraphrased_label_html,
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semantic_similarity_display, empathy_score_display
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)
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def handle_feedback(feedback, comment):
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"Calculating... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>",
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"Paraphrasing... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>", 0,
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"Calculating... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>", "",
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"Calculating... <span class='loading'>⏳</span>", "Calculating... <span class='loading'>⏳</span>"
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),
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inputs=[],
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toxicity_output, bias_output,
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paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
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paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
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semantic_similarity_output, empathy_score_output
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]
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).then(
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fn=handle_classification,
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toxicity_output, bias_output,
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paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
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paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
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semantic_similarity_output, empathy_score_output
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]
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).then(
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fn=lambda prediction, confidence, html: html,
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comment_input, confidence_output, label_display, history_output, toxicity_output, bias_output,
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paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
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paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
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semantic_similarity_output, empathy_score_output
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]
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)
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