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