Spaces:
Sleeping
Sleeping
File size: 1,825 Bytes
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import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the Inference Client for the GPT-2 model (or "gpttrash")
client = InferenceClient("gpt2")
def respond(message, history, max_tokens, temperature, top_p):
messages = []
# Add the conversation history (user and assistant exchanges)
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Add the current user message to continue the conversation
messages.append({"role": "user", "content": message})
response = ""
# Get the model's response using chat completion
for response_chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = response_chunk.choices[0].delta.content
response += token
yield response
# Create Gradio Blocks layout for Hugging Face Spaces
with gr.Blocks() as demo:
with gr.Row():
user_input = gr.Textbox(label="User Input")
history = gr.State() # Keeps conversation history
with gr.Row():
max_tokens_slider = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
temperature_slider = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
with gr.Row():
output = gr.Textbox(label="Model Output")
# Set up the chatbot functionality
user_input.submit(respond, [user_input, history, max_tokens_slider, temperature_slider, top_p_slider], output)
if __name__ == "__main__":
demo.launch()
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