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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import pipeline
# Sentiment pipeline
sentiment = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis")
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def get_sentiment(text):
output = sentiment(text)
return f'The sentence was classified as "{output[0]["label"]}" with {output[0]["score"]*100}% confidence'
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
title = "Get a sentiment on you text"
description = """
The bot was takes your text and classify it as either 'Positive' or 'Negative'
"""
demo = gr.Interface(
fn=get_sentiment,
inputs="text",
outputs="text",
title=title,
description=description,
examples=[["I really enjoyed my stay !"], ["Worst rental I ever got"]],
)
if __name__ == "__main__":
demo.launch()
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