Update app.py

app.py

@@ -1,106 +1,161 @@
-import gradio as gr
-from transformers import pipeline
-import pandas as pd
-import plotly.graph_objects as go
-
-# Load the emotion classification model
-model_id = "S-4-G-4-R/distilbert-base-uncased-finetuned-emotion"
-classifier = pipeline("text-classification", model=model_id)
-
-def classify_emotion(text):
-    """
-    Classify the emotion in the given text and return results with visualization
-    """
-    if not text.strip():
-        return None, "Please enter some text to analyze."
-    
-    # Get predictions
-    preds = classifier(text, return_all_scores=True)[0]
-    
-    # Create DataFrame
-    df = pd.DataFrame(preds)
-    df['score'] = df['score'] * 100  # Convert to percentage
-    df = df.sort_values('score', ascending=True)
-    
-    # Create horizontal bar chart using Plotly
-    fig = go.Figure(go.Bar(
-        x=df['score'],
-        y=df['label'],
-        orientation='h',
-        marker=dict(color='steelblue')
-    ))
-    
-    fig.update_layout(
-        title=f'Emotion Classification Results',
-        xaxis_title='Probability (%)',
-        yaxis_title='Emotion',
-        height=400,
-        margin=dict(l=100, r=20, t=60, b=40)
-    )
-    
-    # Format results as text
-    results_text = "**Prediction Results:**\n\n"
-    sorted_df = df.sort_values('score', ascending=False)
-    for _, row in sorted_df.iterrows():
-        results_text += f"- **{row['label']}**: {row['score']:.2f}%\n"
-    
-    return fig, results_text
-
-# Example texts
-examples = [
-    "I was feeling very alone today walking down on road",
-    "I am so happy and excited about this new opportunity!",
-    "This makes me really angry and frustrated.",
-    "I'm scared about what might happen next.",
-    "What a beautiful day, I love this!",
-    "I feel so sad and disappointed about the news."
-]
-
-# Create Gradio interface
-with gr.Blocks(title="Emotion Classifier") as demo:
-    gr.Markdown(
-        """
-        # 🎭 Emotion Classification
-        Enter any text to analyze its emotional content. The model will classify it into different emotion categories.
-        
-        **Model:** S-4-G-4-R/distilbert-base-uncased-finetuned-emotion
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column():
-            text_input = gr.Textbox(
-                label="Enter text to analyze",
-                placeholder="Type your text here...",
-                lines=3
-            )
-            classify_btn = gr.Button("Classify Emotion", variant="primary")
-        
-        with gr.Column():
-            results_text = gr.Markdown(label="Results")
-    
-    plot_output = gr.Plot(label="Emotion Probabilities")
-    
-    # Examples section
-    gr.Examples(
-        examples=examples,
-        inputs=text_input,
-        label="Try these examples:"
-    )
-    
-    # Connect the button
-    classify_btn.click(
-        fn=classify_emotion,
-        inputs=text_input,
-        outputs=[plot_output, results_text]
-    )
-    
-    # Also trigger on Enter key
-    text_input.submit(
-        fn=classify_emotion,
-        inputs=text_input,
-        outputs=[plot_output, results_text]
-    )
-
-# Launch the app
+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+import plotly.graph_objects as go
+
+# Load the emotion classification model
+model_id = "S-4-G-4-R/distilbert-base-uncased-finetuned-emotion"
+classifier = pipeline("text-classification", model=model_id)
+
+# Define emotion labels
+EMOTION_LABELS = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']
+
+# Emoji mapping for emotions
+EMOTION_EMOJIS = {
+    'sadness': '😢',
+    'joy': '😊',
+    'love': '❤️',
+    'anger': '😠',
+    'fear': '😨',
+    'surprise': '😲'
+}
+
+def classify_emotion(text):
+    """
+    Classify the emotion in the given text and return results with visualization
+    """
+    if not text.strip():
+        return None, "Please enter some text to analyze."
+    
+    # Get predictions
+    preds = classifier(text, return_all_scores=True)[0]
+    
+    # Create DataFrame
+    df = pd.DataFrame(preds)
+    df['score'] = df['score'] * 100  # Convert to percentage
+    
+    # Add emojis to labels
+    df['display_label'] = df['label'].map(lambda x: f"{EMOTION_EMOJIS.get(x, '')} {x.capitalize()}")
+    
+    # Sort by score for better visualization
+    df = df.sort_values('score', ascending=True)
+    
+    # Create horizontal bar chart using Plotly
+    colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#FFA07A', '#98D8C8', '#F7DC6F']
+    
+    fig = go.Figure(go.Bar(
+        x=df['score'],
+        y=df['display_label'],
+        orientation='h',
+        marker=dict(
+            color=df['score'],
+            colorscale='Viridis',
+            showscale=False
+        ),
+        text=df['score'].round(2),
+        texttemplate='%{text}%',
+        textposition='outside'
+    ))
+    
+    fig.update_layout(
+        title={
+            'text': 'Emotion Classification Results',
+            'x': 0.5,
+            'xanchor': 'center'
+        },
+        xaxis_title='Confidence (%)',
+        yaxis_title='',
+        height=450,
+        margin=dict(l=20, r=80, t=60, b=40),
+        plot_bgcolor='rgba(0,0,0,0)',
+        paper_bgcolor='rgba(0,0,0,0)',
+        font=dict(size=12)
+    )
+    
+    fig.update_xaxis(range=[0, 105], gridcolor='lightgray')
+    
+    # Format results as text with emojis
+    results_text = "### 🎯 Prediction Results\n\n"
+    sorted_df = df.sort_values('score', ascending=False)
+    
+    top_emotion = sorted_df.iloc[0]
+    results_text += f"**Top Emotion:** {EMOTION_EMOJIS.get(top_emotion['label'], '')} **{top_emotion['label'].capitalize()}** ({top_emotion['score']:.2f}%)\n\n"
+    results_text += "---\n\n**All Emotions:**\n\n"
+    
+    for _, row in sorted_df.iterrows():
+        emoji = EMOTION_EMOJIS.get(row['label'], '')
+        bar_length = int(row['score'] / 5)
+        bar = '█' * bar_length
+        results_text += f"{emoji} **{row['label'].capitalize()}**: {row['score']:.2f}% {bar}\n\n"
+    
+    return fig, results_text
+
+# Example texts
+examples = [
+    ["I was feeling very alone today walking down on road"],
+    ["I am so happy and excited about this new opportunity!"],
+    ["This makes me really angry and frustrated!"],
+    ["I'm scared about what might happen next..."],
+    ["What a beautiful day, I love this!"],
+    ["Wow! I can't believe this just happened!"],
+    ["I feel so sad and disappointed about the news."]
+]
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(), title="Emotion Classifier") as demo:
+    gr.Markdown(
+        """
+        # 🎭 Emotion Classification
+        Analyze the emotional tone of any text using AI. This model can detect **6 emotions**: 
+        Sadness 😢, Joy 😊, Love ❤️, Anger 😠, Fear 😨, and Surprise 😲
+        
+        **Model:** S-4-G-4-R/distilbert-base-uncased-finetuned-emotion
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            text_input = gr.Textbox(
+                label="📝 Enter text to analyze",
+                placeholder="Type or paste your text here...",
+                lines=5
+            )
+            classify_btn = gr.Button("🔍 Classify Emotion", variant="primary", size="lg")
+            
+            gr.Markdown("### 💡 Try these examples:")
+            gr.Examples(
+                examples=examples,
+                inputs=text_input,
+                label=None
+            )
+        
+        with gr.Column(scale=1):
+            results_text = gr.Markdown(label="Results")
+    
+    with gr.Row():
+        plot_output = gr.Plot(label="📊 Emotion Probabilities")
+    
+    gr.Markdown(
+        """
+        ---
+        **How it works:** The model analyzes your text and assigns confidence scores to each of the 6 emotions. 
+        Higher percentages indicate stronger presence of that emotion in the text.
+        """
+    )
+    
+    # Connect the button
+    classify_btn.click(
+        fn=classify_emotion,
+        inputs=text_input,
+        outputs=[plot_output, results_text]
+    )
+    
+    # Also trigger on Enter key
+    text_input.submit(
+        fn=classify_emotion,
+        inputs=text_input,
+        outputs=[plot_output, results_text]
+    )
+
+# Launch the app
 demo.launch()