Update app.py

app.py

@@ -1,13 +1,23 @@
 import gradio as gr
 from transformers import pipeline
 
-# Initialize the summarization pipeline
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")  # You can choose other models
+# Initialize multilingual summarization pipeline
+summarizer = pipeline("summarization", model="facebook/mbart-large-50")
 
 def summarize_text(text):
-    """Summarizes the given text using the pre-trained model."""
+    """Summarizes the given Nepali text using a multilingual model."""
     try:
-        summary = summarizer(text, max_length=150, min_length=30, do_sample=False)[0]['summary_text'] # Adjust max and min length as needed
+        if not text.strip():
+            return "Please enter some Nepali text to summarize"
+            
+        summary = summarizer(
+            text,
+            max_length=150,
+            min_length=30,
+            do_sample=False,
+            truncation=True,
+            src_lang="ne_NP"  # Nepali language code
+        )[0]['summary_text']
         return summary
     except Exception as e:
         return f"Error during summarization: {str(e)}"
@@ -18,8 +28,8 @@ iface = gr.Interface(
     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" # Changed this line
+    description="Enter Nepali text and get a concise summary using multilingual NLP models.",
+    flagging_mode="never"
 )
 
 if __name__ == "__main__":