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-base") # 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()