Upload app.py

app.py

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+"""
+Gradio App for Multilingual Sentiment Analysis
+Deploy this to Hugging Face Spaces
+"""
+
+import gradio as gr
+from sentiment_analyzer import MultilingualSentimentAnalyzer
+
+def analyze_sentiment(text, language, method):
+    """Analyze sentiment and return formatted results"""
+    if not text or not text.strip():
+        return "Please enter some text to analyze."
+    
+    try:
+        analyzer = MultilingualSentimentAnalyzer(language=language, method=method)
+        result = analyzer.analyze(text)
+        
+        # Format the output nicely
+        output = f"""
+## Sentiment Analysis Results
+
+**Polarity:** {result['polarity'].upper()}
+**Confidence:** {result['confidence']*100:.1f}%
+
+**Scores:**
+- Positive: {result['positive_score']:.2f}
+- Negative: {result['negative_score']:.2f}
+
+**Details:**
+- Method: {result['method']}
+- Language: {result['language']}
+- Words analyzed: {result.get('word_count', 0)}
+"""
+        
+        return output
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def batch_analyze(texts, language, method):
+    """Analyze multiple texts"""
+    if not texts:
+        return "Please enter texts to analyze (one per line)."
+    
+    text_list = [t.strip() for t in texts.split('\n') if t.strip()]
+    if not text_list:
+        return "No valid texts found."
+    
+    try:
+        analyzer = MultilingualSentimentAnalyzer(language=language, method=method)
+        results = analyzer.analyze_batch(text_list)
+        stats = analyzer.get_statistics(text_list)
+        
+        output = f"""
+## Batch Analysis Results
+
+**Statistics:**
+- Total texts: {stats['total_texts']}
+- Average confidence: {stats['average_confidence']*100:.1f}%
+
+**Polarity Distribution:**
+"""
+        for polarity, percentage in stats['polarity_percentages'].items():
+            output += f"- {polarity.capitalize()}: {percentage}%\n"
+        
+        output += "\n**Individual Results:**\n"
+        for i, (text, result) in enumerate(zip(text_list, results), 1):
+            output += f"\n{i}. \"{text[:50]}...\" → {result['polarity']} ({result['confidence']*100:.1f}%)\n"
+        
+        return output
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="Multilingual Sentiment Analysis", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🌍 Multilingual Sentiment Analysis Tool
+    
+    Analyze sentiment in **English**, **Turkish**, and **Persian** text using non-deep-learning approaches.
+    
+    This tool uses lexicon-based, rule-based, and hybrid methods for interpretable sentiment analysis.
+    """)
+    
+    with gr.Tabs():
+        with gr.TabItem("Single Text Analysis"):
+            with gr.Row():
+                with gr.Column():
+                    text_input = gr.Textbox(
+                        label="Enter Text",
+                        placeholder="Type your text here...",
+                        lines=5
+                    )
+                    language = gr.Dropdown(
+                        choices=["english", "turkish", "persian"],
+                        value="english",
+                        label="Language"
+                    )
+                    method = gr.Dropdown(
+                        choices=["lexicon", "rule", "hybrid"],
+                        value="hybrid",
+                        label="Analysis Method"
+                    )
+                    analyze_btn = gr.Button("Analyze Sentiment", variant="primary")
+                
+                with gr.Column():
+                    output = gr.Markdown(label="Results")
+            
+            analyze_btn.click(
+                fn=analyze_sentiment,
+                inputs=[text_input, language, method],
+                outputs=output
+            )
+        
+        with gr.TabItem("Batch Analysis"):
+            with gr.Row():
+                with gr.Column():
+                    batch_texts = gr.Textbox(
+                        label="Enter Texts (one per line)",
+                        placeholder="Enter multiple texts, one per line...",
+                        lines=10
+                    )
+                    batch_language = gr.Dropdown(
+                        choices=["english", "turkish", "persian"],
+                        value="english",
+                        label="Language"
+                    )
+                    batch_method = gr.Dropdown(
+                        choices=["lexicon", "rule", "hybrid"],
+                        value="hybrid",
+                        label="Analysis Method"
+                    )
+                    batch_btn = gr.Button("Analyze Batch", variant="primary")
+                
+                with gr.Column():
+                    batch_output = gr.Markdown(label="Batch Results")
+            
+            batch_btn.click(
+                fn=batch_analyze,
+                inputs=[batch_texts, batch_language, batch_method],
+                outputs=batch_output
+            )
+        
+        with gr.TabItem("Examples"):
+            gr.Markdown("""
+            ### Example Texts to Try:
+            
+            **English:**
+            - "I love this product! It's absolutely amazing!!! 😊"
+            - "This is terrible. I hate it."
+            - "Not bad, actually it's quite good!"
+            
+            **Turkish:**
+            - "Bu ürünü çok seviyorum! Harika!"
+            - "Berbat bir deneyim. Hiç beğenmedim."
+            
+            **Persian:**
+            - "این محصول عالی است!"
+            - "خیلی بد بود"
+            """)
+    
+    gr.Markdown("""
+    ---
+    **About:** This tool uses lexicon-based, rule-based, and hybrid approaches (without deep learning) 
+    for interpretable sentiment analysis. Supports English, Turkish, and Persian languages.
+    """)
+
+if __name__ == "__main__":
+    demo.launch()