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import gradio as gr
import aiohttp
import os
import json
from collections import deque
TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
if not TOKEN:
raise ValueError("API token is not set. Please set the HUGGINGFACE_API_TOKEN environment variable.")
memory = deque(maxlen=10)
async def respond(
message,
history: list[tuple[str, str]],
system_message="AI Assistant Role",
max_tokens=512,
temperature=0.7,
top_p=0.95,
):
system_prefix = "System: 입력어의 언어(영어, 한국어, 중국어, 일본어 등)에 따라 동일한 언어로 답변하라."
full_system_message = f"{system_prefix}{system_message}"
memory.append((message, None))
messages = [{"role": "system", "content": full_system_message}]
for val in memory:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
headers = {
"Authorization": f"Bearer {TOKEN}",
"Content-Type": "application/json"
}
payload = {
"model": "mistralai/Mistral-Nemo-Instruct-2407",
"max_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p,
"messages": messages,
"stream": True
}
async with aiohttp.ClientSession() as session:
async with session.post("https://api-inference.huggingface.co/v1/chat/completions", headers=headers, json=payload) as response:
try:
async for chunk in response.content:
if chunk:
chunk_data = chunk.decode('utf-8')
response_json = json.loads(chunk_data)
if "choices" in response_json:
content = response_json["choices"][0]["message"]["content"]
yield content
except json.JSONDecodeError:
# Log the error or handle it appropriately
pass
except StopAsyncIteration:
# Handle the case where the stream is prematurely terminated
pass
finally:
# Ensure that the generator is properly closed
yield "Stream ended"
theme = "Nymbo/Nymbo_Theme"
demo = gr.ChatInterface(
fn=respond,
theme=theme,
additional_inputs=[
gr.Textbox(value="AI Assistant Role", 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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