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import gradio as gr
import nltk
import plotly.graph_objects as go
from nltk.sentiment.vader import SentimentIntensityAnalyzer
nltk.download("vader_lexicon")
sid = SentimentIntensityAnalyzer()
def sentiment_analysis(text):
scores = sid.polarity_scores(text)
del scores["compound"]
labels = list(scores.keys())
values = list(scores.values())
colors = ['red' if label == 'neg' else 'green' if label == 'pos' else 'white' for label in labels]
fig = go.Figure(go.Bar(
x=values,
y=labels,
orientation='h',
marker=dict(color=colors)
))
fig.update_layout(
title="Sentiment Analysis",
xaxis_title="Score",
yaxis_title="Sentiment",
xaxis=dict(range=[0, 1]),
yaxis=dict(showgrid=True),
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)'
)
fig.update_xaxes(showgrid=True)
fig.update_yaxes(showgrid=True)
fig.write_image('sentiment_chart.png')
return gr.Image('sentiment_chart.png')
demo = gr.Interface(
fn=sentiment_analysis,
inputs=gr.Textbox(lines=5, placeholder="Enter a positive or negative sentence here..."),
outputs="image",
title="Sentiment Analysis",
description="Enter a sentence to analyze its sentiment. The model will return the sentiment scores for positive, negative, and neutral tones as a bar chart.",
examples=[["This is wonderful!"], ["I hate this!"], ["It's okay, not bad."]]
)
demo.launch()
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