Update app.py

app.py

@@ -1,26 +1,26 @@
-import gradio as gr
-from transformers import pipeline
+ import gradio as gr
+ from transformers import pipeline
 
-# Initialize the summarization pipeline
-summarizer = pipeline("summarization", model="google/mt5-small") # Changed the model here
+ # Initialize the summarization pipeline
+ summarizer = pipeline("summarization", model="google/mt5-base") # Changed the model here
 
-def summarize_text(text):
-    """Summarizes the given text using the pre-trained model."""
-    try:
-        summary = summarizer(text, max_length=150, min_length=30, do_sample=False)[0]['summary_text'] # Adjust max and min length as needed
-        return summary
-    except Exception as e:
-        return f"Error during summarization: {str(e)}"
+ def summarize_text(text):
+     """Summarizes the given text using the pre-trained model."""
+     try:
+         summary = summarizer(text, max_length=150, min_length=30, do_sample=False)[0]['summary_text'] # Adjust max and min length as needed
+         return summary
+     except Exception as e:
+         return f"Error during summarization: {str(e)}"
 
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=summarize_text,
-    inputs=gr.Textbox(lines=5, label="Nepali Text to Summarize"),
-    outputs=gr.Textbox(lines=5, label="Summary"),
-    title="Nepali Text Summarizer",
-    description="Enter Nepali text and get a concise summary using a pre-trained NLP model.",
-    flagging_mode="never"
-)
+ # Create the Gradio interface
+ iface = gr.Interface(
+     fn=summarize_text,
+     inputs=gr.Textbox(lines=5, label="Nepali Text to Summarize"),
+     outputs=gr.Textbox(lines=5, label="Summary"),
+     title="Nepali Text Summarizer",
+     description="Enter Nepali text and get a concise summary using a pre-trained NLP model.",
+     flagging_mode="never"
+ )
 
-if __name__ == "__main__":
-    iface.launch()
+ if __name__ == "__main__":
+     iface.launch()