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import gradio as gr |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline |
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model = AutoModelForSequenceClassification.from_pretrained( |
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"savasy/bert-base-turkish-sentiment-cased" |
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) |
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tokenizer = AutoTokenizer.from_pretrained( |
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"savasy/bert-base-turkish-sentiment-cased" |
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) |
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sentiment_pipe = pipeline( |
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"sentiment-analysis", |
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tokenizer=tokenizer, |
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model=model |
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) |
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def analyze(text): |
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result = sentiment_pipe(text)[0]["label"] |
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if result == "positive": |
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return "🔵 POZİTİF" |
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elif result == "negative": |
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return "🔴 NEGATİF" |
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return "🟡 NÖTR" |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" |
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# 🇹🇷 Türkçe Duygu Analizi |
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Bir cümle yaz → Model sonucu göstersin. |
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""" |
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) |
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text_input = gr.Textbox( |
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label="Cümle", |
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placeholder="Bir cümle yazınız...", |
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lines=4 |
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) |
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analyze_button = gr.Button("Analiz Et") |
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output = gr.Textbox( |
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label="Duygu Sonucu", |
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interactive=False |
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) |
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analyze_button.click(analyze, inputs=text_input, outputs=output) |
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demo.launch() |
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