Create app.py

app.py

@@ -0,0 +1,32 @@
+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()