app.py

import gradio as gr
from transformers import pipeline

# Sentiment analysis model'ini yükle
sentiment_pipeline = pipeline(
    "sentiment-analysis",
    model="cardiffnlp/twitter-roberta-base-sentiment-latest",
    top_k=None
)

def analyze_sentiment(text):
    """
    Verilen metni analiz eder ve duygu sonucunu döner.
    
    Args:
        text (str): Analiz edilecek metin
        
    Returns:
        dict: Sentiment etiketi ve skorları
    """
    if not text or text.strip() == "":
        return {
            "error": "Please provide a text to analyze",
            "sentiment": "neutral",
            "score": 0.5
        }
    
    try:
        # Model'den tahmin al
        results = sentiment_pipeline(text)[0]
        
        # En yüksek skora sahip sentiment'i bul
        top_result = max(results, key=lambda x: x['score'])
        
        # Label'ı normalize et (positive, negative, neutral)
        label = top_result['label'].lower()
        score = top_result['score']
        
        # Label mapping (model'e göre değişebilir)
        sentiment_map = {
            'positive': 'positive',
            'pos': 'positive',
            'negative': 'negative',
            'neg': 'negative',
            'neutral': 'neutral',
            'neu': 'neutral'
        }
        
        sentiment = sentiment_map.get(label, 'neutral')
        
        # Negative için score'u normalize et
        if sentiment == 'negative':
            score = 1.0 - score
        
        return {
            "sentiment": sentiment,
            "score": round(score, 4),
            "all_scores": results
        }
        
    except Exception as e:
        return {
            "error": str(e),
            "sentiment": "neutral",
            "score": 0.5
        }

# Gradio arayüzü oluştur
def gradio_interface(text):
    result = analyze_sentiment(text)
    
    if "error" in result:
        return f"⚠️ Error: {result['error']}\n\nDefault: {result['sentiment']} ({result['score']})"
    
    sentiment = result['sentiment']
    score = result['score']
    
    # Emoji ekle
    emoji_map = {
        'positive': '😊',
        'negative': '😞',
        'neutral': '😐'
    }
    
    emoji = emoji_map.get(sentiment, '😐')
    
    output = f"{emoji} **Sentiment:** {sentiment.upper()}\n"
    output += f"📊 **Score:** {score}\n\n"
    output += "**All Scores:**\n"
    
    for item in result['all_scores']:
        output += f"- {item['label']}: {item['score']:.4f}\n"
    
    return output

# Gradio UI
demo = gr.Interface(
    fn=gradio_interface,
    inputs=gr.Textbox(
        lines=3,
        placeholder="Enter text to analyze sentiment...",
        label="Input Text"
    ),
    outputs=gr.Markdown(label="Sentiment Analysis Result"),
    title="🤖 Sentiment Analysis API",
    description="Real-time sentiment analysis using Hugging Face Transformers. Analyzes text and returns positive, negative, or neutral sentiment with confidence scores.",
    examples=[
        ["This is amazing! I love it!"],
        ["I'm so sad and disappointed."],
        ["The weather is okay today."],
        ["Bu harika bir gün! Çok mutluyum."],
        ["Berbat bir deneyimdi, hiç beğenmedim."]
    ],
    theme=gr.themes.Soft(),
    api_name="analyze"
)

if __name__ == "__main__":
    demo.launch()