Update app.py

app.py

@@ -1,36 +1,30 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 
-# Initialize multilingual summarization pipeline
-summarizer = pipeline("summarization", model="facebook/mbart-large-50")
+# Load model and tokenizer explicitly
+model_name = "facebook/mbart-large-50"
+tokenizer = AutoTokenizer.from_pretrained(model_name, src_lang="ne_NP")
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Create pipeline with explicit config
+summarizer = pipeline(
+    "summarization",
+    model=model,
+    tokenizer=tokenizer
+)
 
 def summarize_text(text):
-    """Summarizes the given Nepali text using a multilingual model."""
     try:
-        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
+            # No src_lang here anymore
+            generate_kwargs={"forced_bos_token_id": tokenizer.lang_code_to_id["ne_NP"]}
         )[0]['summary_text']
         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 multilingual NLP models.",
-    flagging_mode="never"
-)
-
-if __name__ == "__main__":
-    iface.launch()
+# Rest of Gradio interface remains the same