Delete app_default.py

app_default.py

@@ -1,213 +0,0 @@
-# app.py
-import gradio as gr
-from classifier import classify_toxic_comment
-
-# Clear function for resetting the UI
-def clear_inputs():
-    return "", 0, "", [], "", "", "", "", 0, "", "", "", "", ""
-
-# Custom CSS for styling
-custom_css = """
-.gr-button-primary {
-    background-color: #4CAF50 !important;
-    color: white !important;
-}
-.gr-button-secondary {
-    background-color: #f44336 !important;
-    color: white !important;
-}
-.gr-textbox textarea {
-    border: 2px solid #2196F3 !important;
-    border-radius: 8px !important;
-}
-.gr-slider {
-    background-color: #e0e0e0 !important;
-    border-radius: 10px !important;
-}
-"""
-
-# Main UI function
-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 as part of a four-stage pipeline for automated toxic comment moderation.
-        """
-    )
-
-    with gr.Row():
-        with gr.Column(scale=3):
-            comment_input = gr.Textbox(
-                label="Your Comment",
-                placeholder="Type your comment here...",
-                lines=3,
-                max_lines=5
-            )
-        with gr.Column(scale=1):
-            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=2):
-            prediction_output = gr.Textbox(label="Prediction", placeholder="Prediction will appear here...")
-            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...")
-        with gr.Column(scale=1):
-            confidence_output = gr.Slider(
-                label="Confidence",
-                minimum=0,
-                maximum=1,
-                value=0,
-                interactive=False
-            )
-
-    with gr.Row():
-        label_display = gr.HTML()
-        threshold_display = gr.HTML()
-
-    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_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...")
-        paraphrased_confidence_output = gr.Slider(
-            label="Paraphrased Confidence",
-            minimum=0,
-            maximum=1,
-            value=0,
-            interactive=False
-        )
-        paraphrased_label_display = gr.HTML()
-        semantic_similarity_output = gr.Textbox(label="Semantic Similarity", placeholder="Semantic similarity score will appear here...")
-        emotion_shift_output = gr.Textbox(label="Emotion Shift", placeholder="Emotion shift will appear here...")
-        empathy_score_output = gr.Textbox(label="Empathy Score", placeholder="Empathy score will appear here...")
-
-    with gr.Accordion("Prediction History", open=False):
-        history_output = gr.JSON(label="Previous Predictions")
-
-    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")
-
-    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, emotion_shift, 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,
-            "emotion_shift": emotion_shift,
-            "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 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"
-        emotion_shift_display = emotion_shift if emotion_shift 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"
-
-        return (
-            prediction, confidence, color, 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, emotion_shift_display, empathy_score_display
-        )
-
-    def handle_feedback(feedback, comment):
-        return f"Thank you for your feedback: {feedback}\nAdditional comment: {comment}"
-
-    submit_btn.click(
-        fn=lambda: ("Classifying...", 0, "", None, "", "", "Calculating...", "Calculating...", "Paraphrasing...", "Calculating...", 0, "Calculating...", "Calculating...", "", "Calculating...", "Calculating...", "Calculating..."),  # Show loading state
-        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, emotion_shift_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, emotion_shift_output, empathy_score_output
-        ]
-    ).then(
-        fn=lambda prediction, confidence, color: f"<span style='color: {color}; font-size: 20px; font-weight: bold;'>{prediction}</span>",
-        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],
-        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, emotion_shift_output, empathy_score_output
-        ]
-    )
-
-    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()