Update app.py

app.py

@@ -1,103 +1,206 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+import torch
 
-# Hafif ve hızlı sentiment analysis model
+# 🌍 MULTILINGUAL MODEL - Supports 58 languages including Turkish and English
+# Model: cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual
+# Dataset: Twitter data in multiple languages
+# Classes: Negative (0), Neutral (1), Positive (2)
+
+MODEL_NAME = "cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+# Create pipeline for sentiment analysis
 sentiment_pipeline = pipeline(
     "sentiment-analysis",
-    model="distilbert-base-uncased-finetuned-sst-2-english"
+    model=model,
+    tokenizer=tokenizer,
+    return_all_scores=True
 )
 
 def analyze_sentiment(text):
     """
-    Verilen metni analiz eder ve duygu sonucunu döner.
+    Analyzes sentiment of text in 58+ languages including Turkish and English.
+    
+    Args:
+        text (str): Input text to analyze (any supported language)
+        
+    Returns:
+        dict: {
+            "sentiment": "positive" | "neutral" | "negative",
+            "score": float (0.0 to 1.0),
+            "scores": {
+                "positive": float,
+                "neutral": float,
+                "negative": float
+            },
+            "language_detected": bool (future feature),
+            "error": str (optional)
+        }
     """
     if not text or text.strip() == "":
         return {
             "sentiment": "neutral",
-            "score": 0.5,
+            "score": 0.33,
+            "scores": {
+                "positive": 0.33,
+                "neutral": 0.34,
+                "negative": 0.33
+            },
             "error": "Empty text"
         }
     
     try:
-        # Model'den tahmin al
-        result = sentiment_pipeline(text)[0]
+        # Get all scores from the model
+        results = sentiment_pipeline(text)[0]
         
-        label = result['label'].lower()
-        score = result['score']
+        # Parse results - model returns list of dicts with label and score
+        scores_dict = {}
+        for item in results:
+            label = item['label'].lower()
+            score = item['score']
+            
+            # Map model labels to our format
+            if 'negative' in label or label == 'label_0':
+                scores_dict['negative'] = round(score, 4)
+            elif 'neutral' in label or label == 'label_1':
+                scores_dict['neutral'] = round(score, 4)
+            elif 'positive' in label or label == 'label_2':
+                scores_dict['positive'] = round(score, 4)
         
-        # Label mapping
-        if 'pos' in label:
-            sentiment = 'positive'
-        elif 'neg' in label:
-            sentiment = 'negative'
-            score = 1.0 - score  # Negative için score'u ters çevir
-        else:
-            sentiment = 'neutral'
+        # Determine the dominant sentiment
+        max_sentiment = max(scores_dict.items(), key=lambda x: x[1])
         
         return {
-            "sentiment": sentiment,
-            "score": round(score, 4)
+            "sentiment": max_sentiment[0],
+            "score": max_sentiment[1],
+            "scores": scores_dict
         }
         
     except Exception as e:
-        print(f"Error: {str(e)}")
+        print(f"❌ Error in sentiment analysis: {str(e)}")
         return {
             "sentiment": "neutral",
-            "score": 0.5,
+            "score": 0.33,
+            "scores": {
+                "positive": 0.33,
+                "neutral": 0.34,
+                "negative": 0.33
+            },
             "error": str(e)
         }
 
 def gradio_interface(text):
-    """Web arayüzü için formatted output"""
+    """
+    Formats sentiment analysis for beautiful web display.
+    
+    Args:
+        text (str): Input text
+        
+    Returns:
+        str: Formatted markdown with emojis and detailed scores
+    """
     result = analyze_sentiment(text)
     
     sentiment = result['sentiment']
     score = result['score']
+    scores = result.get('scores', {})
     
-    # Emoji ekle
+    # Emoji mapping for visual appeal
     emoji_map = {
         'positive': '😊',
-        'negative': '😞',
+        'negative': '😢',
         'neutral': '😐'
     }
     
     emoji = emoji_map.get(sentiment, '😐')
     
-    output = f"{emoji} **Sentiment:** {sentiment.upper()}\n\n"
-    output += f"📊 **Confidence Score:** {score}"
+    # Build beautiful markdown output
+    output = f"## {emoji} Sentiment: **{sentiment.upper()}**\n\n"
+    output += f"### 📊 Confidence: {score * 100:.1f}%\n\n"
+    output += "---\n\n"
+    output += "### 🎯 Detailed Scores:\n\n"
+    
+    # Add progress bars for each sentiment
+    if scores:
+        for sent_type, sent_score in sorted(scores.items(), key=lambda x: x[1], reverse=True):
+            emoji_small = emoji_map.get(sent_type, '•')
+            percentage = sent_score * 100
+            bar_length = int(percentage / 5)  # 20 chars max
+            bar = "█" * bar_length + "░" * (20 - bar_length)
+            output += f"{emoji_small} **{sent_type.capitalize()}**: {bar} {percentage:.1f}%\n\n"
     
     if "error" in result and result["error"] != "Empty text":
-        output += f"\n\n⚠️ Note: {result['error']}"
+        output += f"\n\n⚠️ **Note:** {result['error']}"
+    
+    output += "\n\n---\n\n"
+    output += "✨ **Multilingual AI** • 🌍 Supports 58+ languages including Turkish and English"
     
     return output
 
 def api_analyze(text):
-    """API endpoint için JSON response"""
+    """
+    Direct API endpoint for programmatic access.
+    Returns raw JSON response.
+    
+    Args:
+        text (str): Input text in any supported language
+        
+    Returns:
+        dict: Sentiment analysis result
+    """
     return analyze_sentiment(text)
 
-# Gradio arayüzü
+# 🎨 Beautiful Gradio Interface
 demo = gr.Interface(
     fn=gradio_interface,
     inputs=gr.Textbox(
-        lines=3,
-        placeholder="Enter text to analyze sentiment...",
-        label="Input Text"
+        lines=5,
+        placeholder="Bir metin girin... / Enter text to analyze...",
+        label="📝 Input Text (Turkish, English, or 56+ other languages)"
     ),
-    outputs=gr.Markdown(label="Sentiment Analysis Result"),
-    title="🤖 Sentiment Analysis API",
-    description="Real-time sentiment analysis using DistilBERT. Analyzes text and returns positive, negative, or neutral sentiment.",
+    outputs=gr.Markdown(label="🤖 AI Sentiment Analysis Result"),
+    title="🌍 Multilingual Chat Sentiment Analysis",
+    description="""
+    **Advanced AI-powered sentiment analysis** supporting **58+ languages** including Turkish and English.
+    
+    🚀 **Powered by:** XLM-RoBERTa (Twitter-trained multilingual model)
+    
+    ✨ **Features:**
+    - 🇹🇷 Turkish language support
+    - 🇬🇧 English language support  
+    - 🌍 56+ additional languages
+    - 😊😐😢 Positive, Neutral, Negative classification
+    - 📊 Detailed confidence scores
+    - ⚡ Real-time analysis
+    
+    **Perfect for:** Chat applications, social media monitoring, customer feedback analysis
+    """,
     examples=[
-        ["This is amazing! I love it!"],
-        ["I'm so sad and disappointed."],
-        ["The weather is okay today."],
-        ["This product is terrible, worst purchase ever!"],
-        ["Great service, highly recommend!"],
-        ["Bu harika!"],
-        ["Çok kötü bir deneyim."]
+        ["This is amazing! I absolutely love it! 🎉"],
+        ["I'm so disappointed and sad about this situation... 😞"],
+        ["The weather is okay today, nothing special."],
+        ["Bu harika! Çok mutluyum! 🎊"],
+        ["Gerçekten çok kötü bir deneyim yaşadım. 😠"],
+        ["İyi, fena değil. Normal bir gün."],
+        ["¡Esto es increíble! Me encanta! 🌟"],
+        ["Je suis très content de ce service! ❤️"],
+        ["Это ужасно! Я очень разочарован. 😤"]
     ],
-    theme=gr.themes.Soft(),
-    api_name="analyze"
+    theme=gr.themes.Soft(
+        primary_hue="blue",
+        secondary_hue="cyan"
+    ),
+    api_name="analyze",
+    allow_flagging="never"
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    print("🚀 Starting Multilingual Sentiment Analysis Service...")
+    print(f"📦 Model: {MODEL_NAME}")
+    print("🌍 Languages: Turkish, English, Spanish, French, Arabic, Russian, and 52+ more")
+    print("✅ Server starting on http://127.0.0.1:7860")
+    demo.launch(server_name="0.0.0.0", server_port=7860)