Update app.py

app.py

@@ -1,28 +1,50 @@
 import gradio as gr
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
 
 # Load the GPT-2 model and tokenizer
 model_name = "gpt2"
-model = GPT2LMHeadModel.from_pretrained(model_name)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load model and tokenizer, move model to the correct device
+model = GPT2LMHeadModel.from_pretrained(model_name).to(device)
 tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 
 # Define the sentence completion function
 def complete_sentence(sentence):
-	input_ids = tokenizer.encode(sentence, return_tensors="pt")
-	output = model.generate(input_ids, max_length=50, num_return_sequences=1)
-	completed_sentence = tokenizer.decode(output[0], skip_special_tokens=True)
-	return completed_sentence
+    if not sentence.strip():
+        return "Please enter a valid input."
+    try:
+        # Encode the input sentence
+        input_ids = tokenizer.encode(sentence, return_tensors="pt").to(device)
+        
+        # Generate completion
+        output = model.generate(
+            input_ids,
+            max_length=50,
+            num_return_sequences=1,
+            no_repeat_ngram_size=2,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True,
+        )
+        
+        # Decode the generated sentence
+        completed_sentence = tokenizer.decode(output[0], skip_special_tokens=True)
+        return completed_sentence
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
 # Create the Gradio interface
 iface = gr.Interface(
-	fn=complete_sentence,
-	inputs="text",
-	outputs="text",
-	title="Sentence Completion",
-	description="Enter a sentence to complete",
-	example="I love to"
+    fn=complete_sentence,
+    inputs="text",
+    outputs="text",
+    title="Sentence Completion",
+    description="Enter a sentence to complete.",
+    examples=["I love to", "The future of AI is", "Once upon a time"],
 )
 
 # Launch the Gradio interface
 if __name__ == "__main__":
-	iface.launch()
+    iface.launch()