File size: 4,332 Bytes
5a18f55 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
import gradio as gr
from transformers import pipeline
def sentiment_analysis_generate_text(text):
# Define the model
model_name = "yiyanghkust/finbert-tone"
# for faster, less model size use this model
# model_name = "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
# Create the pipeline
nlp = pipeline("sentiment-analysis", model=model_name)
# Split the input text into individual sentences
sentences = text.split('|')
# Run the pipeline on each sentence and collect the results
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")
# Join the results into a single string to return
return "\n".join(output)
def sentiment_analysis_generate_table(text):
# Define the model
model_name = "yiyanghkust/finbert-tone"
# Create the pipeline
nlp = pipeline("sentiment-analysis", model=model_name)
# Split the input text into individual sentences
sentences = text.split('|')
# Generate the HTML table with enhanced colors and bold headers
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']
# Determine the sentiment class
if sentiment == "Positive":
sentiment_class = "positive"
elif sentiment == "Negative":
sentiment_class = "negative"
else:
sentiment_class = "neutral"
# Generate table rows
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__":
# uncomment below code for using the code in text results
# iface = gr.Interface(
# fn=sentiment_analysis_generate_text,
# inputs="text",
# outputs="text",
# 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 '|'.",
# )
# generate the result in html format
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() |