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import gradio as gr
import spaces
from transformers import pipeline
# Create the text generation pipeline.
# If you're running on GPU, you can specify device=0 (or use device_map="auto" if supported).
pipe = pipeline("text-generation", model="TheBloke/Chronoboros-33B-GPTQ", device=0)
@spaces.GPU
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
# Build the prompt from system message and conversation history.
prompt = f"{system_message}\n"
for user_text, assistant_text in history:
if user_text:
prompt += f"User: {user_text}\n"
if assistant_text:
prompt += f"Assistant: {assistant_text}\n"
prompt += f"User: {message}\nAssistant: "
# Generate a response using the pipeline.
# The pipeline returns a list of dictionaries; we take the generated text from the first output.
output = pipe(prompt, max_new_tokens=max_tokens, temperature=temperature, top_p=top_p)
full_text = output[0]["generated_text"]
# Remove the prompt from the generated text to isolate the response.
response_text = full_text[len(prompt):]
# Simulate streaming output in chunks (e.g., 5 characters at a time).
chunk_size = 5
for i in range(0, len(response_text), chunk_size):
yield response_text[: i + chunk_size]
# Configure the ChatInterface with additional inputs.
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)"),
],
)
if __name__ == "__main__":
demo.launch()
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