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Browse files- app.py +20 -13
- requirements.txt +2 -1
app.py
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import gradio as gr
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from transformers import pipeline, AutoTokenizer,
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# Model ve tokenizer'ı yükle
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model_name = "ozcangundes/mt5-small-turkish-summarization"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqGeneration.from_pretrained(model_name)
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summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
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def summarize_text(text):
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if len(text.split()) < 100:
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return "Metin çok kısa. En az 100 kelime olmalıdır."
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# Gradio arayüzünü oluştur
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iface = gr.Interface(
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import gradio as gr
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from transformers import pipeline, AutoTokenizer, T5ForConditionalGeneration
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# Model ve tokenizer'ı yükle
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model_name = "ozcangundes/mt5-small-turkish-summarization"
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def summarize_text(text):
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if len(text.split()) < 100:
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return "Metin çok kısa. En az 100 kelime olmalıdır."
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try:
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# Pipeline oluştur
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summarizer = pipeline(
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"summarization",
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model=model_name,
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tokenizer=model_name
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)
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# Metni özetle
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summary = summarizer(text,
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max_length=150,
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min_length=50,
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length_penalty=2.0,
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num_beams=4,
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early_stopping=True)
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return summary[0]['summary_text']
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except Exception as e:
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return f"Hata oluştu: {str(e)}"
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# Gradio arayüzünü oluştur
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iface = gr.Interface(
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requirements.txt
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gradio>=3.50.2
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transformers>=4.30.0
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torch>=2.0.0
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sentencepiece>=0.1.99
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gradio>=3.50.2
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transformers>=4.30.0
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torch>=2.0.0
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sentencepiece>=0.1.99
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protobuf>=3.20.0
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