|
import gradio as gr |
|
from transformers import pipeline |
|
|
|
|
|
|
|
|
|
def sentiment_analysis_generate_text(text): |
|
|
|
model_name = "yiyanghkust/finbert-tone" |
|
|
|
|
|
|
|
|
|
|
|
nlp = pipeline("sentiment-analysis", model=model_name) |
|
|
|
sentences = text.split('|') |
|
|
|
results = nlp(sentences) |
|
output = [] |
|
for sentence, result in zip(sentences, results): |
|
output.append(f"Text: {sentence.strip()}\nSentiment: {result['label']}, Score: {result['score']:.4f}\n") |
|
|
|
|
|
return "\n".join(output) |
|
|
|
|
|
def sentiment_analysis_generate_table(text): |
|
|
|
model_name = "yiyanghkust/finbert-tone" |
|
|
|
nlp = pipeline("sentiment-analysis", model=model_name) |
|
|
|
sentences = text.split('|') |
|
|
|
|
|
html = """ |
|
<html> |
|
<head> |
|
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.0/css/bootstrap.min.css"> |
|
<style> |
|
.label { |
|
transition: .15s; |
|
border-radius: 8px; |
|
padding: 5px 10px; |
|
font-size: 14px; |
|
text-transform: uppercase; |
|
} |
|
.positive { |
|
background-color: rgb(54, 176, 75); |
|
color: white; |
|
} |
|
.negative { |
|
background-color: rgb(237, 83, 80); |
|
color: white; |
|
} |
|
.neutral { |
|
background-color: rgb(52, 152, 219); |
|
color: white; |
|
} |
|
th { |
|
font-weight: bold; |
|
color: rgb(106, 38, 198); |
|
} |
|
</style> |
|
</head> |
|
<body> |
|
<table class="table table-striped"> |
|
<thead> |
|
<tr> |
|
<th scope="col">Text</th> |
|
<th scope="col">Score</th> |
|
<th scope="col">Sentiment</th> |
|
</tr> |
|
</thead> |
|
<tbody> |
|
""" |
|
for sentence in sentences: |
|
result = nlp(sentence.strip())[0] |
|
text = sentence.strip() |
|
score = f"{result['score']:.4f}" |
|
sentiment = result['label'] |
|
|
|
|
|
if sentiment == "Positive": |
|
sentiment_class = "positive" |
|
elif sentiment == "Negative": |
|
sentiment_class = "negative" |
|
else: |
|
sentiment_class = "neutral" |
|
|
|
|
|
html += f'<tr><td>{text}</td><td>{score}</td><td><span class="label {sentiment_class}">{sentiment}</span></td></tr>' |
|
|
|
html += """ |
|
</tbody> |
|
</table> |
|
</body> |
|
</html> |
|
""" |
|
|
|
return html |
|
|
|
|
|
if __name__ == "__main__": |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
iface = gr.Interface( |
|
sentiment_analysis_generate_table, |
|
gr.Textbox(placeholder="Enter sentence here..."), |
|
["html"], |
|
title="Financial Sentiment Analysis", |
|
description="<p>A sentiment analysis model fine-tuned on financial news.</p>" |
|
"<p>Enter some financial text to see whether the sentiment is positive, neutral or negative.</p>" |
|
"<p><strong>Note:</strong> Separate multiple sentences with a '|'.", |
|
examples=[ |
|
['growth is strong and we have plenty of liquidity.'], |
|
['there is a shortage of capital, and we need extra financing.'], |
|
['formulation patents might protect Vasotec to a limited extent.'], |
|
["growth is strong and we have plenty of liquidity.|there is a shortage of capital"] |
|
], |
|
allow_flagging=False, |
|
examples_per_page=2, |
|
) |
|
|
|
iface.launch() |
