Update app.py

app.py

@@ -1,34 +1,25 @@
 import gradio as gr
 from transformers import pipeline
 
-# 1. Load a text-generation pipeline from Hugging Face
-# (Choose any model from https://huggingface.co/models)
+# Create text-generation pipeline with explicit truncation.
 generator = pipeline(
     "text-generation",
-    model="EleutherAI/gpt-neo-1.3B",  # Example smaller Bloom model
-    max_length=100
+    model="gpt2",
+    max_length=100,
+    truncation=True  # important
 )
 
 def chatbot(prompt):
-    """
-    Generate a text completion from the hugging face model pipeline.
-    """
-    # 2. Pass the prompt to the pipeline
-    responses = generator(prompt, max_length=150, num_return_sequences=1)
-    # The pipeline returns a list of dicts; we only need the generated_text
-    generated_text = responses[0]["generated_text"]
-    
-    # Optionally, if you want to remove the original prompt portion from the output, you can slice it out.
-    # For simplicity, we'll just return the entire generated text.
-    return generated_text
+    # Generate text with the pipeline, also specifying truncation at call time (optional).
+    responses = generator(prompt, max_length=100, truncation=True)
+    return responses[0]["generated_text"]
 
-# 3. Create and launch the Gradio interface
 demo = gr.Interface(
     fn=chatbot,
     inputs="text",
     outputs="text",
-    title="Hugging Face Chatbot",
-    description="Ask anything to this Hugging Face model!"
+    title="Hugging Face GPT-2 Chatbot",
+    description="Ask anything to the GPT-2-based model!"
 )
 
 if __name__ == "__main__":