app.py

from transformers import pipeline

# Load the pipeline for text generation
generator = pipeline("text-generation", model="microsoft/DialoGPT-small")

# Function to generate a response
def dialoGPT_response(user_input, history):
    # Since the pipeline handles everything, we just need to format our input
    conversation = [{"role": "user", "content": user_input}] if history is None else history + [{"role": "user", "content": user_input}]
    
    # Generate response using the pipeline, which manages all pre/post-processing
    response = generator(conversation, return_full_text=False, max_length=1000)

    # Extract the last assistant response
    assistant_response = response[0]['generated_text']
    
    # Append this response to history
    new_history = conversation + [{"role": "assistant", "content": assistant_response}]
    
    return assistant_response, new_history

# Gradio interface
iface = gr.Interface(
    fn=dialoGPT_response,
    inputs=[
        gr.Textbox(placeholder="Enter your message..."),
        "state"
    ],
    outputs=[
        "text",  # The response
        "state"  # Updated history
    ],
    title="DialoGPT Chat",
    description="Chat with DialoGPT-small model. Your conversation history is maintained.",
    allow_flagging="never"
)

iface.launch()