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Update app.py
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app.py
CHANGED
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@@ -6,19 +6,49 @@ model_path = "cardiffnlp/twitter-roberta-base-sentiment-latest"
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sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
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def analyze_sentiment(text):
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# Gradio arayüzünü oluştur
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iface = gr.Interface(
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fn=analyze_sentiment,
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inputs=gr.Textbox(lines=2, placeholder="Metin giriniz..."),
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outputs=
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title="Twitter RoBERTa Duygu Analizi",
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description="cardiffnlp/twitter-roberta-base-sentiment-latest modelini kullanarak
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examples=[
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)
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# Arayüzü başlat
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sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
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def analyze_sentiment(text):
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"""
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Metni analiz eder ve en yüksek skorlu duyguyu döndürür
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"""
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if not text or len(text.strip()) == 0:
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return {
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"sentiment": "neutral",
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"score": 0.0
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}
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try:
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results = sentiment_task(text[:512]) # Max 512 karakter
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# En yüksek skorlu sonucu al
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top_result = results[0]
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label = top_result['label'].lower()
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score = top_result['score']
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return {
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"sentiment": label,
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"score": round(score, 3),
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"text": text[:100] + "..." if len(text) > 100 else text
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}
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except Exception as e:
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return {
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"sentiment": "neutral",
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"score": 0.0,
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"error": str(e)
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}
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# Gradio arayüzünü oluştur
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iface = gr.Interface(
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fn=analyze_sentiment,
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inputs=gr.Textbox(lines=2, placeholder="Metin giriniz..."),
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outputs=gr.JSON(label="Sonuç"),
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title="💬 Twitter RoBERTa Duygu Analizi",
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description="cardiffnlp/twitter-roberta-base-sentiment-latest modelini kullanarak metnin duygu analizini yapın (positive/neutral/negative).",
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examples=[
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["I love the new design of your website!"],
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["This is terrible and disappointing."],
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["It's okay, nothing special."],
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["I drank coffee, then went to work"]
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]
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)
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# Arayüzü başlat
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