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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load DialoGPT model and tokenizer
model_name = "microsoft/DialoGPT-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Respond function for Gradio interface
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Format the chat history for the DialoGPT model using the 'messages' format
conversation = [{"role": "system", "content": system_message}]
for user_msg, bot_msg in history:
if user_msg:
conversation.append({"role": "user", "content": user_msg})
if bot_msg:
conversation.append({"role": "assistant", "content": bot_msg})
conversation.append({"role": "user", "content": message})
# Tokenize input and generate response
inputs = tokenizer.encode(" ".join([msg["content"] for msg in conversation]), return_tensors="pt")
outputs = model.generate(
inputs,
max_length=max_tokens,
temperature=temperature,
top_p=top_p,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(outputs[:, inputs.shape[-1] :][0], skip_special_tokens=True)
return response
# Gradio Chat Interface
demo = gr.Interface(
fn=respond,
inputs=[
gr.Textbox(label="Message"),
gr.State(), # For history
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
outputs="text",
)
if __name__ == "__main__":
demo.launch()
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