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Update app.py
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app.py
CHANGED
@@ -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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bleu_score_output = gr.Textbox(label="BLEU Score", placeholder="BLEU 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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@@ -210,7 +209,7 @@ 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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@@ -226,7 +225,6 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
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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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"bleu_score": bleu_score,
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"rouge_scores": rouge_scores
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})
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@@ -247,7 +245,6 @@ 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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bleu_score_display = f"{bleu_score} (Scale: 0 to 1, higher is better)" if bleu_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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@@ -261,7 +258,7 @@ 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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@@ -282,7 +279,7 @@ 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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@@ -292,7 +289,7 @@ 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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@@ -317,7 +314,7 @@ 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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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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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, rouge_scores
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) = classify_toxic_comment(comment)
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history.append({
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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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)
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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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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, rouge_scores_display
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)
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def handle_feedback(feedback, comment):
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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, rouge_scores_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, rouge_scores_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, rouge_scores_output
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]
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)
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