Spaces:
Sleeping
Sleeping
File size: 1,861 Bytes
5cbf26d 7d79d16 5cbf26d 7d79d16 5cbf26d 7d79d16 5cbf26d 7d79d16 5cbf26d 7d79d16 5cbf26d ccb7f32 45dc82d ccb7f32 45dc82d 5cbf26d cd5e4cc 45dc82d cd5e4cc 45dc82d 5cbf26d cd5e4cc 5cbf26d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import gradio as gr
from huggingface_hub import InferenceClient
import os
client = InferenceClient(
model="mistralai/Mistral-7B-Instruct-v0.3",
token=os.getenv('HF_TOKEN')
)
def chat_fn(message, system_message, history_str, max_tokens, temperature, top_p):
# Convert history string (optional) to message list
messages = [{"role": "system", "content": system_message}]
if history_str:
# Format: user1||assistant1\nuser2||assistant2
for pair in history_str.split("\n"):
if "||" in pair:
user_msg, assistant_msg = pair.split("||", 1)
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
# Get response from HF
response = ""
for chunk in client.chat_completion(
messages=messages,
stream=True,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
):
response += chunk.choices[0].delta.content or ""
return response
demo = gr.Interface(
fn=chat_fn,
inputs=[
gr.Textbox(lines=2, label="User Message"),
gr.Textbox(value="You are a friendly Chatbot.", label="System Prompt"),
gr.Textbox(lines=4, placeholder="user||bot\nuser2||bot2", label="Conversation History (optional)"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max 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"),
],
outputs="text",
allow_flagging="never",
title="LLM Budaya",
description="Chatbot menggunakan model HuggingFace Zephyr-7B"
)
if __name__ == "__main__":
demo.launch() |