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import gradio as gr | |
from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
import torch | |
# Load model and tokenizer | |
model_name = "cardiffnlp/twitter-roberta-base-sentiment-latest" | |
model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
def predict_sentiment(text): | |
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) | |
with torch.no_grad(): | |
outputs = model(**inputs) | |
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) | |
sentiments = ['Negative', 'Neutral', 'Positive'] | |
result = {sentiments[i]: float(predictions[0][i]) for i in range(len(sentiments))} | |
return result | |
def custom_theme(): | |
"""Define a custom theme for the Gradio app.""" | |
return gr.Theme( | |
# Define your color scheme | |
primary='#FF6347', | |
text_on_primary='#FFFFFF', | |
background='#F0F8FF', | |
card_background='#FAEBD7', | |
text='#2F4F4F', | |
icon='light', | |
) | |
# Create Gradio interface | |
iface = gr.Interface(fn=predict_sentiment, | |
inputs=gr.inputs.Textbox(lines=2, placeholder="Type your sentence here..."), | |
outputs=gr.outputs.Label(num_top_classes=3), | |
theme=custom_theme(), | |
title="Sentiment Analysis", | |
description="Analyze the sentiment of your text.", | |
article="<p style='text-align: center'>Enter a sentence to get its sentiment. The model categorizes sentiments into Negative, Neutral, and Positive.</p>") | |
if __name__ == "__main__": | |
iface.launch() | |