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from transformers import pipeline
import gradio as gr
# Load the pipeline for text generation
try:
# generator = pipeline("text-generation", model="microsoft/DialoGPT-small")
generator = pipeline("text-generation", model="microsoft/DialoGPT-medium")
except Exception as e:
print(f"Error loading the model: {e}")
raise
# Function to generate a response
def dialoGPT_response(user_input, history):
try:
conversation = [{"role": "user", "content": user_input}] if history is None else history + [{"role": "user", "content": user_input}]
response = generator(conversation, return_full_text=False, max_length=1000)
assistant_response = response[0]['generated_text']
new_history = conversation + [{"role": "assistant", "content": assistant_response}]
return assistant_response, new_history
except Exception as e:
print(f"Error generating response: {e}")
return "An error occurred while generating a response.", history
# Gradio interface
iface = gr.Interface(
fn=dialoGPT_response,
inputs=[
gr.Textbox(placeholder="Enter your message..."),
"state"
],
outputs=[
"text",
"state"
],
title="DialoGPT Chat",
description="Chat with DialoGPT-small model. Your conversation history is maintained.",
allow_flagging="never"
)
if __name__ == "__main__":
iface.launch(debug=True) |