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import gradio as gr
from huggingface_hub import InferenceClient
from typing import List, Tuple
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(
message: str,
history: List[Tuple[str, str]],
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
):
messages = [{"role": "system", "content": system_message}]
# Add conversation history to the messages
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Append the new message to the conversation
messages.append({"role": "user", "content": message})
response = ""
# Stream the response from the model
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message['choices'][0]['delta']['content']
response += token
yield response
demo = gr.Interface(
fn=respond,
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)"),
gr.Chatbot(label="Conversation History"), # Added chat history as input
],
outputs=[gr.Textbox(label="Response")]
)
if __name__ == "__main__":
demo.launch()
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