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import gradio as gr
from huggingface_hub import InferenceClient
import requests
import os
url = "http://47.94.86.196:8084/chat_completion"
def respond(
message,
history: list[tuple[str, str]],
do_sample: bool,
seed: int,
max_new_tokens,
temperature,
top_p,
top_k,
repetition_penalty
):
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]})
messages.append({"role": "user", "content": message})
response = ""
request_data = dict(
messages=messages,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
seed=seed,
top_p=top_p,
top_k=top_k,
temperature=temperature,
repetition_penalty=repetition_penalty
)
print(request_data)
with requests.post(url, json=request_data, stream=True, headers={"Authorization": f"Bearer {os.environ['HF_TOKEN']}"}) as r:
# printing response of each stream
for chunk in r.iter_content(1024):
response += chunk.decode("utf8")
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
chatbot=gr.Chatbot(height=600),
additional_inputs=[
# gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Checkbox(True, label="do sample"),
gr.Number(42, precision=0, label="seed"),
gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max new tokens"),
gr.Slider(minimum=0.01, maximum=4.0, value=0.7, step=0.01, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=1.0,
step=0.05,
label="Top-p (nucleus sampling)",
),
gr.Slider(
minimum=0,
maximum=100,
value=0,
step=1,
label="Top-K (Top-K sampling)",
),
gr.Slider(
minimum=1,
maximum=2,
value=1.03,
step=0.01,
label="repetition penalty",
),
],
)
if __name__ == "__main__":
demo.queue(default_concurrency_limit=2, max_size=10)
demo.launch() |