Update app.py

app.py

@@ -9,52 +9,47 @@ print("🔄 Loading Model...")
 try:
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
-    print("✅ Model Loaded Successfully!")
+    print("✅ Model Loaded!")
 except Exception as e:
     print(f"❌ Error loading model: {e}")
 
-
 def generate_text(prompt):
     print(f"📩 Input received: {prompt}")
-    
-    
+
     if len(prompt.split()) < 3:
         return "⚠️ text is too short, please write a full sentence."
-
+        
     try:
         inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
         
-        print("🧠 Generating...")
-        
         summary_ids = model.generate(
             inputs["input_ids"],
-            max_length=100,
+            max_length=120,
             min_length=10,
-            num_beams=1,           
-            do_sample=False,        
-            no_repeat_ngram_size=2  
-           
+            num_beams=1,
+            early_stopping=True
         )
         
         decoded = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
         cleaned_text = decoded.replace("<n>", " ").replace(" .", ".").strip()
         
-        print(f"✅ Output: {cleaned_text}")
         return cleaned_text
         
     except Exception as e:
-        print(f"❌ Error: {e}")
         return f"Error: {str(e)}"
 
-
 interface = gr.Interface(
     fn=generate_text,
-    inputs=gr.Textbox(lines=5, label="Input Text", placeholder="Deep learning allows computers to..."),
-    outputs=gr.Textbox(lines=10, label="Generated Content"),
+    
+    inputs=gr.Textbox(lines=5, label="Input Text", placeholder="Write your topic here..."),
+    
+    
+    outputs=gr.Textbox(lines=10, label="Generated Content"), 
+    
     title="Generative AI Project",
-    description="Fine-tuned Pegasus Model on ArXiv Papers.",
+    description="Fine-tuned Pegasus Model.",
     cache_examples=False 
 )
 
 if __name__ == "__main__":
-    interface.queue().launch()
+    interface.launch()