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import gradio as gr
from transformers import pipeline
sentiment_analyzer = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
def analyze_sentiment(text):
result = sentiment_analyzer(text)[0]
sentiment_score = result['label']
if sentiment_score == '1 star':
return 1
elif sentiment_score == '2 stars':
return 2
elif sentiment_score == '3 stars':
return 3
elif sentiment_score == '4 stars':
return 4
else:
return 5
examples = [
"I love this product! It's amazing!",
"This was the worst experience I've ever had.",
"The movie was okay, not great but not bad either.",
"Absolutely fantastic! I would recommend it to everyone."
]
iface = gr.Interface(
fn=analyze_sentiment, # Function to call for sentiment analysis
inputs=[
gr.Textbox(label="Enter Text", placeholder="Type or paste a sentence or paragraph here...", lines=5),
gr.Button("Analyze Sentiment") # Button to trigger analysis
],
outputs=gr.Textbox(label="Sentiment Rating (1 to 5 stars)"), # Display sentiment rating
live=False, # Disable live preview while typing
examples=examples, # Predefined examples
description="Sentiment analysis using BERT-based model for multilingual sentiment prediction."
)
iface.launch()
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