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import gradio as gr
from transformers import pipeline
# Load the model
model = pipeline("text-generation", model="karthikqnq/qnqgpt2")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Construct the prompt from history and current message
prompt = system_message + "\n\n"
for user_msg, assistant_msg in history:
if user_msg:
prompt += f"User: {user_msg}\nAssistant: {assistant_msg}\n"
prompt += f"User: {message}\nAssistant: "
# Generate response
response = model(
prompt,
max_length=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
num_return_sequences=1
)[0]['generated_text']
# Extract only the assistant's response
try:
assistant_response = response.split("Assistant: ")[-1].strip()
except:
assistant_response = response
return assistant_response
# Create the Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
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)"
),
],
title="QnQ GPT-2 Chatbot",
description="A chatbot powered by the QnQ GPT-2 model"
)
if __name__ == "__main__":
demo.launch() |