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 = """
Text
Score
Sentiment
"""
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'
{text}
{score}
{sentiment}
'
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="
A sentiment analysis model fine-tuned on financial news.
"
# "
Enter some financial text to see whether the sentiment is positive, neutral or negative.
"
# "
Note: 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="
A sentiment analysis model fine-tuned on financial news.
"
"
Enter some financial text to see whether the sentiment is positive, neutral or negative.
"
"
Note: 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()