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Tolga
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Update app.py
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app.py
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import gradio as gr
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import gradio as gr
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import torch
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from transformers import pipeline
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model = pipeline(task="sentiment-analysis", model="tkurtulus/Turkish-AI-rlines-SentimentAnalysis")
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def sentiment_analysis(text):
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res = model(text)[0]
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res_label = {}
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if res["label"] == "positive":
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res_label["positive"] = res["score"]
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res_label["negative"] = 1 - res["score"]
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res_label["neutral"] = 1 - res["score"]
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if res["label"] == "negative":
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res_label["negative"] = res["score"]
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res_label["positive"] = 1 - res["score"]
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res_label["neutral"] = 1 - res["score"]
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if res["label"] == "neutral":
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res_label["neutral"] = res["score"]
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res_label["positive"] = 1 - res["score"]
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res_label["negative"] = 1 - res["score"]
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return res_label
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custom_css = """
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#component-0 {
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max-width: 600px;
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margin: 0 auto;
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}
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h1,h2 {
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text-align: center;
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}
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a {
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color: #77b3ee !important;
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text-decoration: none !important;
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}
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a:hover {
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text-decoration: underline !important;
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}
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"""
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browser_tab_title = "Sentiment Analysis"
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intro_markdown = """## Sentiment Analysis
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Using the [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) model, trained on movie reviews."""
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with gr.Blocks(title=browser_tab_title, css=custom_css) as demo:
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with gr.Row():
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with gr.Column():
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title = gr.Markdown(intro_markdown)
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text_input = gr.Textbox(placeholder="Enter a positive or negative sentence here...", label="Text")
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label_output = gr.Label(label="Sentiment outcome")
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button_run = gr.Button("Compute sentiment")
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button_run.click(sentiment_analysis, inputs=text_input, outputs=label_output)
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gr.Examples(["That's great!", "The movie was bad.", "How are you"], text_input)
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if __name__ == "__main__":
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demo.launch()
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