app.py

import gradio as gr
from transformers import pipeline

# Load the pretrained model pipeline
pipe = pipeline("text-classification", model="ahmedrachid/FinancialBERT-Sentiment-Analysis")

# Footer content
footer = """
---
### Sasiraj Shanmugasundaram
#### Machine Learning Deployment Project
"""
# Function for prediction
def predict_sentiment(news_text):
    result = pipe(news_text)[0]
    return result['label']

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_sentiment, 
    inputs=gr.Textbox(lines=4, placeholder="Type your financial news here..."), 
    outputs="text",
    title="Financial Sentiment Analysis",
    description="Enter financial news and get sentiment analysis based on FinancialBERT."
)
# Add footer using Markdown
# Add footer using Markdown
gr.Markdown(footer)


# Launch the app
iface.launch()