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import gradio as gr
from huggingface_hub import InferenceClient
client = InferenceClient("Grandediw/lora_model")
def respond(message, history, system_message, max_tokens, temperature, top_p):
# Convert tuple-based history to messages if needed
messages = [{"role": "system", "content": system_message}]
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
response = ""
for partial in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = partial.choices[0].delta.content
response += token
yield response
with gr.Blocks(title="Enhanced LORA Chat Interface") as demo:
gr.Markdown(
"""
# LORA Chat Assistant
Welcome! This is a demo of a LORA-based Chat Assistant.
Start by entering your prompt below.
"""
)
with gr.Row():
# System message and other parameters
with gr.Column():
system_message = gr.Textbox(
value="You are a friendly Chatbot.",
label="Initial Behavior (System Message)",
lines=3,
placeholder="Describe how the assistant should behave..."
)
max_tokens = gr.Slider(
minimum=1, maximum=2048, value=512, step=1,
label="Max new tokens"
)
temperature = gr.Slider(
minimum=0.1, maximum=4.0, value=0.7, step=0.1,
label="Temperature"
)
top_p = gr.Slider(
minimum=0.1, maximum=1.0, value=0.95, step=0.05,
label="Top-p (nucleus sampling)"
)
# Create the chat interface using tuple format
# Note: `type='tuple'` preserves the (user, assistant) tuple format.
chat = gr.ChatInterface(
fn=respond,
additional_inputs=[system_message, max_tokens, temperature, top_p],
type='tuple'
)
if __name__ == "__main__":
demo.launch()
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