Update app_spaces.py

app_spaces.py

@@ -1,171 +1,242 @@
-import gradio as gr
-import pandas as pd
-from transformers import pipeline
-import warnings
-import os
-warnings.filterwarnings("ignore")
-
-# Initialize the models
-print("Loading ABSA models for Hugging Face Spaces...")
-token_classifier = pipeline(
-    model="sdf299/abte-restaurants-distilbert-base-uncased",
-    aggregation_strategy="simple"
-)
-
-classifier = pipeline(
-    model="sdf299/absa-restaurants-distilbert-base-uncased"
-)
-print("Models loaded successfully!")
-
-def analyze_sentiment(sentence):
-    """
-    Perform aspect-based sentiment analysis on the input sentence.
-    
-    Args:
-        sentence (str): Input sentence to analyze
-        
-    Returns:
-        tuple: (formatted_results, aspects_summary, detailed_dataframe)
-    """
-    if not sentence.strip():
-        return "Please enter a sentence to analyze.", "", pd.DataFrame()
-    
-    try:
-        # Extract aspects using token classifier
-        results = token_classifier(sentence)
-        
-        if not results:
-            return "No aspects found in the sentence.", "", pd.DataFrame()
-        
-        # Get unique aspects
-        aspects = list(set([result['word'] for result in results]))
-        
-        # Analyze sentiment for each aspect
-        detailed_results = []
-        formatted_output = f"**Input Sentence:** {sentence}\n\n**Analysis Results:**\n\n"
-        
-        for aspect in aspects:
-            # Classify sentiment for this aspect
-            sentiment_result = classifier(f'{sentence} [SEP] {aspect}')
-            
-            # Extract sentiment label and confidence
-            sentiment_label = sentiment_result[0]['label']
-            confidence = sentiment_result[0]['score']
-            
-            # Format the result
-            formatted_output += f"🎯 **Aspect:** {aspect}\n"
-            formatted_output += f"   **Sentiment:** {sentiment_label} (Confidence: {confidence:.3f})\n\n"
-            
-            # Store for dataframe
-            detailed_results.append({
-                'Aspect': aspect,
-                'Sentiment': sentiment_label,
-                'Confidence': f"{confidence:.3f}"
-            })
-        
-        # Create summary
-        aspects_summary = f"**Identified Aspects:** {', '.join(aspects)}"
-        
-        # Create dataframe for tabular view
-        df = pd.DataFrame(detailed_results)
-        
-        return formatted_output, aspects_summary, df
-        
-    except Exception as e:
-        error_msg = f"Error during analysis: {str(e)}"
-        return error_msg, "", pd.DataFrame()
-
-# Create the Gradio interface
-with gr.Blocks(
-    title="🍽️ Restaurant Review Analyzer - ABSA",
-    theme=gr.themes.Soft(),
-    css="""
-    .gradio-container {
-        font-family: 'Arial', sans-serif;
-        max-width: 1200px;
-    }
-    .main-header {
-        text-align: center;
-        margin-bottom: 30px;
-    }
-    """
-) as demo:
-    
-    gr.HTML("""
-    <div class="main-header">
-        <h1>🍽️ Restaurant Review Analyzer</h1>
-        <h3>Aspect-Based Sentiment Analysis</h3>
-        <p>Analyze restaurant reviews to identify specific aspects (food, service, atmosphere, etc.) and their associated sentiments.</p>
-        <p><em>Powered by DistilBERT models fine-tuned on restaurant reviews</em></p>
-    </div>
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=2):
-            # Input section
-            sentence_input = gr.Textbox(
-                label="Enter Restaurant Review",
-                placeholder="e.g., The services here is wonderful, but I hate the food. However, I still love the atmosphere here.",
-                lines=3,
-                max_lines=5
-            )
-            
-            analyze_btn = gr.Button("🔍 Analyze Sentiment", variant="primary", size="lg")
-            
-            # Example sentences
-            gr.Examples(
-                examples=[
-                    ["The services here is wonderful, but I hate the food. However, I still love the atmosphere here."],
-                    ["The food was amazing and the staff was very friendly, but the restaurant was too noisy."],
-                    ["Great location and delicious pizza, but the service was slow and the prices are too high."],
-                    ["The ambiance is perfect for a romantic dinner, excellent wine selection, but the dessert was disappointing."],
-                    ["Fast service and good value for money, but the food quality could be better."],
-                    ["Excellent sushi and attentive waiters, though the wait time was quite long."],
-                    ["Beautiful decor and reasonable prices, but the pasta was overcooked."]
-                ],
-                inputs=sentence_input,
-                label="Try these examples:"
-            )
-        
-        with gr.Column(scale=3):
-            # Output section
-            with gr.Tab("📊 Detailed Results"):
-                results_output = gr.Markdown(label="Analysis Results")
-            
-            with gr.Tab("📋 Quick Summary"):
-                aspects_output = gr.Markdown(label="Aspects Summary")
-            
-            with gr.Tab("📈 Data Table"):
-                table_output = gr.Dataframe(
-                    label="Results Table",
-                    headers=["Aspect", "Sentiment", "Confidence"]
-                )
-    
-    # Event handlers
-    analyze_btn.click(
-        fn=analyze_sentiment,
-        inputs=[sentence_input],
-        outputs=[results_output, aspects_output, table_output]
-    )
-    
-    sentence_input.submit(
-        fn=analyze_sentiment,
-        inputs=[sentence_input],
-        outputs=[results_output, aspects_output, table_output]
-    )
-    
-    # Footer with model information
-    gr.HTML("""
-    <div style="text-align: center; margin-top: 30px; padding: 20px; border-top: 1px solid #eee;">
-        <p><strong>Models Used:</strong></p>
-        <p>🔤 Aspect Extraction: <a href="https://huggingface.co/sdf299/abte-restaurants-distilbert-base-uncased" target="_blank">sdf299/abte-restaurants-distilbert-base-uncased</a></p>
-        <p>😊 Sentiment Classification: <a href="https://huggingface.co/sdf299/absa-restaurants-distilbert-base-uncased" target="_blank">sdf299/absa-restaurants-distilbert-base-uncased</a></p>
-        <p style="margin-top: 15px; font-size: 0.9em; color: #666;">
-            This app demonstrates aspect-based sentiment analysis for restaurant reviews using fine-tuned DistilBERT models.
-        </p>
-    </div>
-    """)
-
-# Launch the app
-if __name__ == "__main__":
+import gradio as gr
+import pandas as pd
+from transformers import pipeline
+import warnings
+import os
+warnings.filterwarnings("ignore")
+
+# Initialize the models
+print("Loading ABSA models for Hugging Face Spaces...")
+token_classifier = pipeline(
+    model="sdf299/abte-restaurants-distilbert-base-uncased",
+    aggregation_strategy="simple"
+)
+
+classifier = pipeline(
+    model="sdf299/absa-restaurants-distilbert-base-uncased"
+)
+print("Models loaded successfully!")
+
+def get_sentiment_color(sentiment_label):
+    """Return color based on sentiment label."""
+    sentiment_lower = sentiment_label.lower()
+    if 'positive' in sentiment_lower:
+        return "#28a745", "🟢"  # Green
+    elif 'negative' in sentiment_lower:
+        return "#dc3545", "🔴"  # Red
+    else:
+        return "#6c757d", "⚪"  # Gray for neutral
+
+def analyze_sentiment(sentence):
+    """
+    Perform aspect-based sentiment analysis on the input sentence.
+    
+    Args:
+        sentence (str): Input sentence to analyze
+        
+    Returns:
+        tuple: (formatted_results, aspects_summary, detailed_dataframe)
+    """
+    if not sentence.strip():
+        return "Please enter a sentence to analyze.", "", pd.DataFrame()
+    
+    try:
+        # Extract aspects using token classifier
+        results = token_classifier(sentence)
+        
+        if not results:
+            return "No aspects found in the sentence.", "", pd.DataFrame()
+        
+        # Get unique aspects
+        aspects = list(set([result['word'] for result in results]))
+        
+        # Analyze sentiment for each aspect
+        detailed_results = []
+        formatted_output = f"**📝 Input Sentence:** {sentence}\n\n"
+        formatted_output += "## 🎯 Analysis Results:\n\n"
+        
+        # Count sentiments for summary
+        sentiment_counts = {'positive': 0, 'negative': 0, 'neutral': 0}
+        
+        for aspect in aspects:
+            # Classify sentiment for this aspect
+            sentiment_result = classifier(f'{sentence} [SEP] {aspect}')
+            
+            # Extract sentiment label and confidence
+            sentiment_label = sentiment_result[0]['label']
+            confidence = sentiment_result[0]['score']
+            
+            # Get color and emoji for this sentiment
+            color, emoji = get_sentiment_color(sentiment_label)
+            
+            # Count sentiments
+            if 'positive' in sentiment_label.lower():
+                sentiment_counts['positive'] += 1
+            elif 'negative' in sentiment_label.lower():
+                sentiment_counts['negative'] += 1
+            else:
+                sentiment_counts['neutral'] += 1
+            
+            # Format the result with colors
+            formatted_output += f'<div style="margin: 15px 0; padding: 15px; border-left: 4px solid {color}; background-color: {color}15; border-radius: 5px;">'
+            formatted_output += f'<strong style="color: {color};">{emoji} Aspect: {aspect}</strong><br>'
+            formatted_output += f'<span style="color: {color}; font-weight: bold;">Sentiment: {sentiment_label}</span> '
+            formatted_output += f'<span style="color: #666; font-size: 0.9em;">(Confidence: {confidence:.3f})</span>'
+            formatted_output += '</div>\n\n'
+            
+            # Store for dataframe with colored styling
+            detailed_results.append({
+                'Aspect': aspect,
+                'Sentiment': sentiment_label,
+                'Confidence': f"{confidence:.3f}",
+                'Color': color,
+                'Emoji': emoji
+            })
+        
+        # Create colorful summary
+        aspects_summary = "## 📊 Summary:\n\n"
+        aspects_summary += f"**🔍 Total Aspects Found:** {len(aspects)}\n\n"
+        
+        # Add sentiment breakdown
+        if sentiment_counts['positive'] > 0:
+            aspects_summary += f"🟢 **Positive:** {sentiment_counts['positive']} aspects\n\n"
+        if sentiment_counts['negative'] > 0:
+            aspects_summary += f"🔴 **Negative:** {sentiment_counts['negative']} aspects\n\n"
+        if sentiment_counts['neutral'] > 0:
+            aspects_summary += f"⚪ **Neutral:** {sentiment_counts['neutral']} aspects\n\n"
+        
+        aspects_summary += f"**📝 Identified Aspects:** {', '.join(aspects)}"
+        
+        # Create dataframe for tabular view (simplified for table)
+        df_data = []
+        for result in detailed_results:
+            df_data.append({
+                'Aspect': result['Aspect'],
+                'Sentiment': f"{result['Emoji']} {result['Sentiment']}",
+                'Confidence': result['Confidence']
+            })
+        df = pd.DataFrame(df_data)
+        
+        return formatted_output, aspects_summary, df
+        
+    except Exception as e:
+        error_msg = f"❌ **Error during analysis:** {str(e)}\n\nPlease try again with a different sentence."
+        return error_msg, "", pd.DataFrame()
+
+# Create the Gradio interface
+with gr.Blocks(
+    title="🍽️ Restaurant Review Analyzer - ABSA",
+    theme=gr.themes.Soft(),
+    css="""
+    .gradio-container {
+        font-family: 'Arial', sans-serif;
+        max-width: 1200px;
+    }
+    .main-header {
+        text-align: center;
+        margin-bottom: 30px;
+    }
+    .sentiment-positive {
+        color: #28a745 !important;
+        background-color: #d4edda;
+        border-color: #c3e6cb;
+    }
+    .sentiment-negative {
+        color: #dc3545 !important;
+        background-color: #f8d7da;
+        border-color: #f5c6cb;
+    }
+    .sentiment-neutral {
+        color: #6c757d !important;
+        background-color: #f8f9fa;
+        border-color: #dee2e6;
+    }
+    """
+) as demo:
+    
+    gr.HTML("""
+    <div class="main-header">
+        <h1>🍽️ Restaurant Review Analyzer</h1>
+        <h3>🎨 Colorful Aspect-Based Sentiment Analysis</h3>
+        <p>Analyze restaurant reviews to identify specific aspects and their sentiments with beautiful color coding!</p>
+        <div style="margin: 15px 0; padding: 10px; background-color: #f8f9fa; border-radius: 8px; border: 1px solid #dee2e6;">
+            <p style="margin: 5px 0;"><strong>🎨 Color Guide:</strong></p>
+            <span style="color: #28a745; font-weight: bold;">🟢 Positive</span> | 
+            <span style="color: #dc3545; font-weight: bold;">🔴 Negative</span> | 
+            <span style="color: #6c757d; font-weight: bold;">⚪ Neutral</span>
+        </div>
+        <p><em>Powered by DistilBERT models fine-tuned on restaurant reviews</em></p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            # Input section
+            sentence_input = gr.Textbox(
+                label="🍽️ Enter Restaurant Review",
+                placeholder="e.g., The services here is wonderful, but I hate the food. However, I still love the atmosphere here.",
+                lines=3,
+                max_lines=5
+            )
+            
+            analyze_btn = gr.Button("🔍 Analyze Sentiment", variant="primary", size="lg")
+            
+            # Example sentences
+            gr.Examples(
+                examples=[
+                    ["The services here is wonderful, but I hate the food. However, I still love the atmosphere here."],
+                    ["The food was amazing and the staff was very friendly, but the restaurant was too noisy."],
+                    ["Great location and delicious pizza, but the service was slow and the prices are too high."],
+                    ["The ambiance is perfect for a romantic dinner, excellent wine selection, but the dessert was disappointing."],
+                    ["Fast service and good value for money, but the food quality could be better."],
+                    ["Excellent sushi and attentive waiters, though the wait time was quite long."],
+                    ["Beautiful decor and reasonable prices, but the pasta was overcooked."],
+                    ["Outstanding customer service and fresh ingredients, highly recommend this place!"],
+                    ["Terrible experience - rude staff, cold food, and dirty tables. Never coming back."]
+                ],
+                inputs=sentence_input,
+                label="💡 Try these examples:"
+            )
+        
+        with gr.Column(scale=3):
+            # Output section
+            with gr.Tab("🎨 Colorful Results"):
+                results_output = gr.HTML(label="Visual Analysis Results")
+            
+            with gr.Tab("📊 Summary Dashboard"):
+                aspects_output = gr.Markdown(label="Quick Summary")
+            
+            with gr.Tab("📈 Data Table"):
+                table_output = gr.Dataframe(
+                    label="Results Table",
+                    headers=["Aspect", "Sentiment", "Confidence"]
+                )
+    
+    # Event handlers
+    analyze_btn.click(
+        fn=analyze_sentiment,
+        inputs=[sentence_input],
+        outputs=[results_output, aspects_output, table_output]
+    )
+    
+    sentence_input.submit(
+        fn=analyze_sentiment,
+        inputs=[sentence_input],
+        outputs=[results_output, aspects_output, table_output]
+    )
+    
+    # Footer with model information
+    gr.HTML("""
+    <div style="text-align: center; margin-top: 30px; padding: 20px; border-top: 1px solid #eee;">
+        <p><strong>🤖 Models Used:</strong></p>
+        <p>🔤 Aspect Extraction: <a href="https://huggingface.co/sdf299/abte-restaurants-distilbert-base-uncased" target="_blank">sdf299/abte-restaurants-distilbert-base-uncased</a></p>
+        <p>😊 Sentiment Classification: <a href="https://huggingface.co/sdf299/absa-restaurants-distilbert-base-uncased" target="_blank">sdf299/absa-restaurants-distilbert-base-uncased</a></p>
+        <p style="margin-top: 15px; font-size: 0.9em; color: #666;">
+            ✨ This app demonstrates colorful aspect-based sentiment analysis for restaurant reviews using fine-tuned DistilBERT models.
+        </p>
+    </div>
+    """)
+
+# Launch the app
+if __name__ == "__main__":
     demo.launch()