Update app.py

app.py

@@ -1,12 +1,16 @@
 import gradio as gr
+import torch
 from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 
 # 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)
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    low_cpu_mem_usage=True
+)
 
-# Create pipeline with explicit config
 summarizer = pipeline(
     "summarization",
     model=model,
@@ -15,16 +19,30 @@ summarizer = pipeline(
 
 def summarize_text(text):
     try:
+        if not text.strip():
+            return "Please enter some Nepali text to summarize"
+            
         summary = summarizer(
             text,
             max_length=150,
             min_length=30,
             truncation=True,
-            # 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)}"
+    finally:
+        torch.cuda.empty_cache()
+
+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.",
+    allow_flagging="never"
+)
 
-# Rest of Gradio interface remains the same
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)