42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
import gradio as gr
|
|
from transformers import pipeline, AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
|
|
|
|
from gradio.themes.utils import sizes
|
|
theme = gr.themes.Default(radius_size=sizes.radius_none).set(
|
|
block_label_text_color = '#4D63FF',
|
|
block_title_text_color = '#4D63FF',
|
|
button_primary_text_color = '#4D63FF',
|
|
button_primary_background_fill='#FFFFFF',
|
|
button_primary_border_color='#4D63FF',
|
|
button_primary_background_fill_hover='#EDEFFF',
|
|
)
|
|
|
|
modelName="Seethal/sentiment_analysis_generic_dataset"
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(modelName)
|
|
model = AutoModelForSequenceClassification.from_pretrained(modelName)
|
|
sentimentPipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
|
|
Label2Des = {
|
|
"LABEL_0": "NEGATIVE",
|
|
"LABEL_1": "NEUTRAL",
|
|
"LABEL_2": "POSITIVE"
|
|
}
|
|
|
|
def sentiment_analysis(text):
|
|
results = sentimentPipeline(text)
|
|
|
|
return f"Sentiment: {Label2Des.get(results[0]['label'])}, Score: {results[0]['score']:.2f}"
|
|
|
|
demo = gr.Interface(fn=sentiment_analysis,
|
|
inputs='text',
|
|
outputs='text',
|
|
theme = theme,
|
|
css = "footer {visibility: hidden}",
|
|
allow_flagging = "never"
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
demo.queue(concurrency_count=10)
|
|
demo.launch(server_name = "0.0.0.0")
|