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