Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import pandas as pd
 import plotly.express as px
-import plotly.graph_objects as go
 import shutil
 import os
 import torch
@@ -26,10 +25,10 @@ _, _ = predict_emotions("dummy text")
 emotion_labels = inference_module.EMOTION_LABELS
 default_thresholds = inference_module.THRESHOLDS
 
-# Prediction function with export capability
-def predict_emotions_with_details(text, confidence_threshold=0.0, chart_type="bar"):
+# Prediction function (simplified, no export)
+def predict_emotions_with_details(text, confidence_threshold=0.0):
     if not text.strip():
-        return "Please enter some text.", "", "", None, None, False
+        return "Please enter some text.", "", None
     
     predictions_str, processed_text = predict_emotions(text)
     
@@ -40,7 +39,7 @@ def predict_emotions_with_details(text, confidence_threshold=0.0, chart_type="ba
             emotion, confidence = line.split(": ")
             predictions.append((emotion, float(confidence)))
     
-    # Get raw logits for all emotions
+    # Get raw logits for top 5 (though not displayed in this simplified version)
     encodings = inference_module.TOKENIZER(
         processed_text,
         padding='max_length',
@@ -55,12 +54,6 @@ def predict_emotions_with_details(text, confidence_threshold=0.0, chart_type="ba
         outputs = inference_module.MODEL(input_ids, attention_mask=attention_mask)
         logits = torch.sigmoid(outputs.logits).cpu().numpy()[0]
     
-    # All emotions for top 5
-    all_emotions = [(emotion_labels[i], round(logit, 4)) for i, logit in enumerate(logits)]
-    all_emotions.sort(key=lambda x: x[1], reverse=True)
-    top_5_emotions = all_emotions[:5]
-    top_5_output = "\n".join([f"{emotion}: {confidence:.4f}" for emotion, confidence in top_5_emotions])
-    
     # Filter predictions based on threshold
     filtered_predictions = []
     for emotion, confidence in predictions:
@@ -74,143 +67,73 @@ def predict_emotions_with_details(text, confidence_threshold=0.0, chart_type="ba
     else:
         thresholded_output = "\n".join([f"{emotion}: {confidence:.4f}" for emotion, confidence in filtered_predictions])
     
-    # Create visualization
+    # Create bar chart
     fig = None
-    df_export = None
     if filtered_predictions:
         df = pd.DataFrame(filtered_predictions, columns=["Emotion", "Confidence"])
-        df_export = df.copy()
-        
-        if chart_type == "bar":
-            fig = px.bar(
-                df,
-                x="Emotion",
-                y="Confidence",
-                color="Emotion",
-                text="Confidence",
-                title="Emotion Confidence Levels (Above Threshold)",
-                height=400,
-                color_discrete_sequence=px.colors.qualitative.Plotly
-            )
-            fig.update_traces(texttemplate='%{text:.2f}', textposition='auto')
-            fig.update_layout(showlegend=False, margin=dict(t=40, b=40), xaxis_title="", yaxis_title="Confidence")
-        else:  # pie chart
-            fig = px.pie(
-                df,
-                names="Emotion",
-                values="Confidence",
-                title="Emotion Confidence Distribution (Above Threshold)",
-                height=400,
-                color_discrete_sequence=px.colors.qualitative.Plotly
-            )
-            fig.update_traces(textinfo='percent+label', pull=[0.1] + [0] * (len(df) - 1))
-            fig.update_layout(margin=dict(t=40, b=40))
+        fig = px.bar(
+            df,
+            x="Emotion",
+            y="Confidence",
+            color="Emotion",
+            text="Confidence",
+            title="Emotion Confidence Levels",
+            height=300,
+            color_discrete_sequence=px.colors.qualitative.Pastel
+        )
+        fig.update_traces(texttemplate='%{text:.2f}', textposition='auto')
+        fig.update_layout(showlegend=False, margin=dict(t=40, b=40), xaxis_title="", yaxis_title="Confidence")
     
-    return processed_text, thresholded_output, top_5_output, fig, df_export, True
+    return processed_text, thresholded_output, fig
 
-# Custom CSS for enhanced styling
+# Simplified CSS
 custom_css = """
 body {
-    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
-    background-color: #f5f7fa;
+    font-family: 'Arial', sans-serif;
+    background-color: #f9f9f9;
 }
 .gr-panel {
-    border-radius: 16px;
-    box-shadow: 0 6px 20px rgba(0,0,0,0.08);
+    border-radius: 8px;
+    box-shadow: 0 2px 10px rgba(0,0,0,0.05);
     background: white;
-    padding: 20px;
-    margin-bottom: 20px;
+    padding: 15px;
+    margin-bottom: 15px;
 }
 .gr-button {
-    border-radius: 8px;
-    padding: 12px 24px;
-    font-weight: 600;
-    transition: all 0.3s ease;
-}
-.gr-button-primary {
+    border-radius: 6px;
+    padding: 10px 20px;
+    font-weight: 500;
     background: #4a90e2;
     color: white;
+    transition: background 0.3s ease;
 }
-.gr-button-primary:hover {
+.gr-button:hover {
     background: #357abd;
 }
-.gr-button-secondary {
-    background: #e4e7eb;
-    color: #333;
-}
-.gr-button-secondary:hover {
-    background: #d1d5db;
-}
 #title {
-    font-size: 2.8em;
-    font-weight: 700;
-    color: #1a3c6e;
+    font-size: 2.2em;
+    font-weight: 600;
+    color: #333;
     text-align: center;
     margin-bottom: 10px;
 }
 #description {
-    font-size: 1.2em;
-    color: #555;
-    text-align: center;
-    max-width: 800px;
-    margin: 0 auto 30px auto;
-}
-#theme-toggle {
-    position: fixed;
-    top: 20px;
-    right: 20px;
-    background: none;
-    border: none;
-    font-size: 1.5em;
-    cursor: pointer;
-    transition: transform 0.3s;
-}
-#theme-toggle:hover {
-    transform: scale(1.2);
-}
-.dark-mode {
-    background: #1e2a44;
-    color: #e0e0e0;
-}
-.dark-mode .gr-panel {
-    background: #2a3a5a;
-    box-shadow: 0 6px 20px rgba(0,0,0,0.2);
-}
-.dark-mode #title {
-    color: #66b3ff;
-}
-.dark-mode #description {
-    color: #b0b0b0;
-}
-.dark-mode .gr-button-secondary {
-    background: #3a4a6a;
-    color: #e0e0e0;
-}
-.dark-mode .gr-button-secondary:hover {
-    background: #4a5a7a;
-}
-#loading {
-    font-style: italic;
-    color: #888;
+    font-size: 1.1em;
+    color: #666;
     text-align: center;
-    display: none;
-}
-#loading.visible {
-    display: block;
+    max-width: 600px;
+    margin: 0 auto 20px auto;
 }
 #examples-title {
-    font-size: 1.5em;
-    font-weight: 600;
-    color: #1a3c6e;
+    font-size: 1.3em;
+    font-weight: 500;
+    color: #333;
     margin-bottom: 10px;
 }
-.dark-mode #examples-title {
-    color: #66b3ff;
-}
 footer {
     text-align: center;
-    margin-top: 40px;
-    padding: 20px;
+    margin-top: 30px;
+    padding: 15px;
     font-size: 0.9em;
     color: #666;
 }
@@ -221,148 +144,77 @@ footer a {
 footer a:hover {
     text-decoration: underline;
 }
-.dark-mode footer {
-    color: #b0b0b0;
-}
-"""
-
-# JavaScript for theme toggle
-theme_js = """
-function toggleTheme() {
-    document.body.classList.toggle('dark-mode');
-    const toggleBtn = document.getElementById('theme-toggle');
-    toggleBtn.innerHTML = document.body.classList.contains('dark-mode') ? '☀️' : '🌙';
-}
-function showLoading() {
-    document.getElementById('loading').classList.add('visible');
-}
-function hideLoading() {
-    document.getElementById('loading').classList.remove('visible');
-}
 """
 
-# Gradio Blocks UI
+# Gradio Blocks UI (Simplified)
 with gr.Blocks(css=custom_css) as demo:
-    # Theme toggle button
-    gr.HTML(
-        """
-        <button id='theme-toggle' onclick='toggleTheme()'>🌙</button>
-        <script>{}</script>
-        """.format(theme_js)
-    )
-    
     # Header
     gr.Markdown("<div id='title'>GoEmotions BERT Classifier</div>", elem_id="title")
     gr.Markdown(
         """
         <div id='description'>
-        Predict emotions from text using a fine-tuned BERT-base model on the GoEmotions dataset. 
-        Detect 28 emotions with optimized thresholds (Micro F1: 0.6006). 
-        View preprocessed text, top 5 emotions, and thresholded predictions with interactive visualizations!
+        Predict emotions from text using a fine-tuned BERT model. 
+        Enter your text below to see the detected emotions and their confidence scores.
         </div>
         """,
         elem_id="description"
     )
     
-    # Main content
-    with gr.Row():
-        with gr.Column(scale=1):
-            # Input Section
-            with gr.Group():
-                gr.Markdown("### Input Text")
-                text_input = gr.Textbox(
-                    label="Enter Your Text",
-                    placeholder="Type something like 'I’m just chilling today'...",
-                    lines=3,
-                    show_label=False
-                )
-                confidence_slider = gr.Slider(
-                    minimum=0.0,
-                    maximum=0.9,
-                    value=0.0,
-                    step=0.05,
-                    label="Minimum Confidence Threshold",
-                    info="Filter predictions below this confidence level (default thresholds still apply)"
-                )
-                chart_type = gr.Radio(
-                    choices=["bar", "pie"],
-                    value="bar",
-                    label="Chart Type",
-                    info="Choose how to visualize the emotion confidences"
-                )
-                with gr.Row():
-                    submit_btn = gr.Button("Predict Emotions", variant="primary")
-                    reset_btn = gr.Button("Reset", variant="secondary")
-    
-    # Loading indicator
-    loading_indicator = gr.HTML("<div id='loading'>Predicting emotions, please wait...</div>")
+    # Input Section
+    with gr.Group():
+        text_input = gr.Textbox(
+            label="Enter Your Text",
+            placeholder="Type something like 'I’m just chilling today'...",
+            lines=2,
+            show_label=False
+        )
+        confidence_slider = gr.Slider(
+            minimum=0.0,
+            maximum=0.9,
+            value=0.0,
+            step=0.05,
+            label="Minimum Confidence Threshold",
+            info="Filter predictions below this confidence level (default thresholds still apply)"
+        )
+        submit_btn = gr.Button("Predict Emotions")
     
     # Output Section
-    with gr.Row():
-        with gr.Column(scale=1):
-            with gr.Group():
-                gr.Markdown("### Results")
-                processed_text_output = gr.Textbox(label="Preprocessed Text", lines=2, interactive=False)
-                thresholded_output = gr.Textbox(label="Predicted Emotions (Above Threshold)", lines=5, interactive=False)
-                top_5_output = gr.Textbox(label="Top 5 Emotions (Regardless of Threshold)", lines=5, interactive=False)
-                output_plot = gr.Plot(label="Emotion Confidence Visualization (Above Threshold)")
-    
-    # Export predictions
-    export_btn = gr.File(label="Download Predictions as CSV", visible=False)
+    with gr.Group():
+        processed_text_output = gr.Textbox(label="Preprocessed Text", lines=1, interactive=False)
+        thresholded_output = gr.Textbox(label="Predicted Emotions", lines=3, interactive=False)
+        output_plot = gr.Plot(label="Emotion Confidence Chart")
     
     # Example carousel
     with gr.Group():
-        gr.Markdown("<div id='examples-title'>Example Texts</div>", elem_id="examples-title")
+        gr.Markdown("<div id='examples-title'>Try These Examples</div>", elem_id="examples-title")
         examples = gr.Examples(
             examples=[
-                ["I’m just chilling today.", "Neutral Example"],
-                ["Thank you for saving my life!", "Gratitude Example"],
-                ["I’m nervous about my exam tomorrow.", "Nervousness Example"],
-                ["I love my new puppy so much!", "Love Example"],
-                ["I’m so relieved the storm passed.", "Relief Example"]
+                ["I’m thrilled to win this award! 😄", "Joy Example"],
+                ["This is so frustrating, nothing works. 😣", "Annoyance Example"],
+                ["I feel so sorry for what happened. 😢", "Sadness Example"],
+                ["What a beautiful day to be alive! 🌞", "Admiration Example"],
+                ["Feeling nervous about the exam tomorrow 😓 u/student r/study", "Nervousness Example"]
             ],
             inputs=[text_input],
-            label="",
-            examples_per_page=3
+            label=""
         )
     
     # Footer
     gr.HTML(
         """
         <footer>
-            Built with ❤️ by logasanjeev | 
+            Built by logasanjeev | 
             <a href="https://huggingface.co/logasanjeev/goemotions-bert">Model Card</a> | 
-            <a href="https://www.kaggle.com/code/ravindranlogasanjeev/evaluation-logasanjeev-goemotions-bert/notebook">Kaggle Notebook</a> | 
-            <a href="https://github.com/logasanjeev">GitHub</a>
+            <a href="https://www.kaggle.com/code/ravindranlogasanjeev/evaluation-logasanjeev-goemotions-bert/notebook">Kaggle Notebook</a>
         </footer>
         """
     )
     
-    # State to manage loading visibility
-    loading_state = gr.State(value=False)
-    
-    # Bind predictions with loading spinner
-    def start_loading():
-        return True
-    
-    def stop_loading(processed_text, thresholded_output, top_5_output, fig, df_export, loading_state):
-        return processed_text, thresholded_output, top_5_output, fig, df_export, False
-    
+    # Bind predictions
     submit_btn.click(
-        fn=start_loading,
-        inputs=[],
-        outputs=[loading_state]
-    ).then(
         fn=predict_emotions_with_details,
-        inputs=[text_input, confidence_slider, chart_type],
-        outputs=[processed_text_output, thresholded_output, top_5_output, output_plot, export_btn, loading_state]
-    )
-    
-    # Reset functionality
-    reset_btn.click(
-        fn=lambda: ("", "", "", None, None, False),
-        inputs=[],
-        outputs=[text_input, processed_text_output, thresholded_output, top_5_output, output_plot, export_btn, loading_state]
+        inputs=[text_input, confidence_slider],
+        outputs=[processed_text_output, thresholded_output, output_plot]
     )
 
 # Launch