Upload folder using huggingface_hub

.gradio/certificate.pem

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README.md

@@ -1,12 +1,51 @@
----
-title: ABSA
-emoji: 🐢
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 5.35.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: ABSA
+app_file: app_spaces.py
+sdk: gradio
+sdk_version: 5.9.1
+---
+
+# 🍽️ Restaurant Review Analyzer
+
+A Gradio-powered web interface for analyzing restaurant reviews using **Aspect-Based Sentiment Analysis (ABSA)**. This application identifies specific aspects (like food, service, atmosphere) mentioned in reviews and determines the sentiment associated with each aspect.
+
+## 🎯 How It Works
+
+The application uses two fine-tuned DistilBERT models:
+
+1. **Aspect Extraction**: Identifies aspects mentioned in reviews (e.g., "food", "service", "atmosphere")
+2. **Sentiment Classification**: Determines sentiment (positive/negative) for each aspect
+
+## 🚀 Try It Out!
+
+Simply enter a restaurant review in the text box and click "Analyze Sentiment" to see:
+- **Identified Aspects**: What specific elements are mentioned
+- **Sentiment Analysis**: Whether each aspect is viewed positively or negatively
+- **Confidence Scores**: How certain the model is about each prediction
+
+## 📊 Example
+
+**Input**: "The services here is wonderful, but I hate the food. However, I still love the atmosphere here."
+
+**Output**:
+- **service** → POSITIVE (0.952)
+- **food** → NEGATIVE (0.887) 
+- **atmosphere** → POSITIVE (0.934)
+
+## 🔧 Models
+
+- **Aspect Extraction**: [sdf299/abte-restaurants-distilbert-base-uncased](https://huggingface.co/sdf299/abte-restaurants-distilbert-base-uncased)
+- **Sentiment Classification**: [sdf299/absa-restaurants-distilbert-base-uncased](https://huggingface.co/sdf299/absa-restaurants-distilbert-base-uncased)
+
+## 💡 Use Cases
+
+Perfect for:
+- Restaurant owners analyzing customer feedback
+- Review aggregation platforms
+- Market research on dining experiences
+- Academic research in sentiment analysis
+- Understanding customer opinions at scale
+
+---
+
+*Built with 🤗 Transformers and Gradio* 

app.py

@@ -0,0 +1,172 @@
+import gradio as gr
+import pandas as pd
+from transformers import pipeline
+import warnings
+warnings.filterwarnings("ignore")
+
+# Initialize the models
+print("Loading models...")
+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()
+
+def create_interface():
+    """Create and configure the Gradio interface."""
+    
+    with gr.Blocks(
+        title="Aspect-Based Sentiment Analysis",
+        theme=gr.themes.Soft(),
+        css="""
+        .gradio-container {
+            font-family: 'Arial', sans-serif;
+        }
+        .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>
+        </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."]
+                    ],
+                    inputs=sentence_input
+                )
+            
+            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
+        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: <code>sdf299/abte-restaurants-distilbert-base-uncased</code></p>
+            <p>😊 Sentiment Classification: <code>sdf299/absa-restaurants-distilbert-base-uncased</code></p>
+        </div>
+        """)
+    
+    return demo
+
+if __name__ == "__main__":
+    # Create and launch the interface
+    demo = create_interface()
+    demo.launch(
+        share=True,  # Creates a public link
+        server_name="0.0.0.0",  # Makes it accessible from other devices on the network
+        server_port=7860,
+        show_error=True
+    ) 

app_spaces.py

@@ -0,0 +1,171 @@
+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__":
+    demo.launch() 

requirements.txt

@@ -0,0 +1,6 @@
+gradio==4.8.0
+transformers==4.35.0
+torch==2.1.0
+pandas==2.1.0
+numpy==1.24.0
+tokenizers==0.14.1 

requirements_flexible.txt

@@ -0,0 +1,5 @@
+gradio>=4.0.0
+transformers>=4.30.0
+torch>=1.12.0
+pandas>=1.5.0
+numpy>=1.21.0 

requirements_minimal.txt

@@ -0,0 +1,4 @@
+gradio
+transformers
+torch
+pandas 

run.bat

@@ -0,0 +1,38 @@
+@echo off
+echo.
+echo ==========================================
+echo   Restaurant Review Analyzer (ABSA)
+echo ==========================================
+echo.
+
+REM Check if Python is installed
+python --version >nul 2>&1
+if %errorlevel% neq 0 (
+    echo Error: Python is not installed or not in PATH
+    echo Please install Python 3.8+ and try again
+    pause
+    exit /b 1
+)
+
+REM Check if requirements are installed
+echo Checking dependencies...
+python -c "import gradio, transformers, pandas" >nul 2>&1
+if %errorlevel% neq 0 (
+    echo Installing requirements...
+    pip install -r requirements.txt
+    if %errorlevel% neq 0 (
+        echo Error: Failed to install requirements
+        pause
+        exit /b 1
+    )
+)
+
+echo.
+echo Starting the application...
+echo This may take a few minutes on first run (downloading models)
+echo.
+
+REM Launch the application
+python app.py
+
+pause 

run.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+"""
+Simple launcher script for the ABSA Gradio application.
+Provides different launch options for various use cases.
+"""
+
+import argparse
+import sys
+import os
+
+def main():
+    parser = argparse.ArgumentParser(description="Launch the ABSA Gradio Application")
+    parser.add_argument(
+        '--mode', 
+        choices=['dev', 'prod', 'share'], 
+        default='dev',
+        help='Launch mode: dev (development), prod (production), share (public link)'
+    )
+    parser.add_argument('--port', type=int, default=7860, help='Port to run the server on')
+    parser.add_argument('--host', default='127.0.0.1', help='Host to bind to')
+    
+    args = parser.parse_args()
+    
+    # Import here to avoid loading models during argument parsing
+    try:
+        from app import create_interface
+        print("Loading ABSA models... This may take a few minutes on first run.")
+        demo = create_interface()
+        
+        # Configure launch parameters based on mode
+        launch_kwargs = {
+            'server_port': args.port,
+            'show_error': True
+        }
+        
+        if args.mode == 'dev':
+            launch_kwargs.update({
+                'server_name': '127.0.0.1',
+                'share': False,
+                'debug': True
+            })
+            print(f"🚀 Starting in DEVELOPMENT mode on http://127.0.0.1:{args.port}")
+            
+        elif args.mode == 'prod':
+            launch_kwargs.update({
+                'server_name': '0.0.0.0',
+                'share': False,
+                'debug': False
+            })
+            print(f"🚀 Starting in PRODUCTION mode on http://0.0.0.0:{args.port}")
+            
+        elif args.mode == 'share':
+            launch_kwargs.update({
+                'server_name': '0.0.0.0',
+                'share': True,
+                'debug': False
+            })
+            print("🚀 Starting with PUBLIC LINK (share=True)")
+            print("⚠️  The public link will be accessible from anywhere on the internet!")
+        
+        # Launch the application
+        demo.launch(**launch_kwargs)
+        
+    except ImportError as e:
+        print(f"❌ Error importing required modules: {e}")
+        print("💡 Make sure you've installed the requirements: pip install -r requirements.txt")
+        sys.exit(1)
+    except Exception as e:
+        print(f"❌ Error starting the application: {e}")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main() 

run.sh

@@ -0,0 +1,45 @@
+#!/bin/bash
+
+echo ""
+echo "=========================================="
+echo "  Restaurant Review Analyzer (ABSA)"
+echo "=========================================="
+echo ""
+
+# Check if Python is installed
+if ! command -v python3 &> /dev/null && ! command -v python &> /dev/null; then
+    echo "Error: Python is not installed"
+    echo "Please install Python 3.8+ and try again"
+    exit 1
+fi
+
+# Use python3 if available, otherwise python
+PYTHON_CMD="python3"
+if ! command -v python3 &> /dev/null; then
+    PYTHON_CMD="python"
+fi
+
+# Check Python version
+PYTHON_VERSION=$($PYTHON_CMD --version 2>&1 | awk '{print $2}')
+echo "Using Python $PYTHON_VERSION"
+
+# Check if requirements are installed
+echo "Checking dependencies..."
+$PYTHON_CMD -c "import gradio, transformers, pandas" 2>/dev/null
+if [ $? -ne 0 ]; then
+    echo "Installing requirements..."
+    pip install -r requirements.txt
+    if [ $? -ne 0 ]; then
+        echo "Error: Failed to install requirements"
+        echo "You may need to use pip3 instead of pip"
+        exit 1
+    fi
+fi
+
+echo ""
+echo "Starting the application..."
+echo "This may take a few minutes on first run (downloading models)"
+echo ""
+
+# Launch the application
+$PYTHON_CMD app.py