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# app.py
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
from threading import Thread
import gradio as gr
import re
import torch
from openai import OpenAI
client = OpenAI(
api_key="sk-420ab66020704eabbe37501ec39b7a2b",
base_url="https://bailingchat.alipay.com",
)
# define chat function
def chat(user_input, max_tokens=11264):
# chat history
messages_template = [
# {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
{"role": "user", "content": user_input}
]
response = client.chat.completions.create(
model="Ling-lite-1.5-250604",
messages=messages_template,
max_tokens=max_tokens,
temperature=0.01,
top_p=1,
)
resp_text = response.choices[0].message.content
print(resp_text)
yield resp_text
# Create a custom layout using Blocks
with gr.Blocks(css="""
#markdown-output {
height: 300px;
overflow-y: auto;
border: 1px solid #ddd;
padding: 10px;
}
""") as demo:
gr.Markdown(
"## Ling-lite-1.5 AI Assistant\n"
"Based on [inclusionAI/Ling-lite-1.5](https://huggingface.co/inclusionAI/Ling-lite-1.5) "
)
with gr.Row():
max_tokens_slider = gr.Slider(minimum=100, maximum=10000, step=100, label="Generated length")
# output_box = gr.Textbox(lines=10, label="Response")
output_box = gr.Markdown(label="Response", elem_id="markdown-output")
input_box = gr.Textbox(lines=8, label="Input you question")
examples = gr.Examples(
examples=[
["Introducing the basic concepts of large language models"],
["How to solve long context dependencies in math problems?"]
],
inputs=input_box
)
interface = gr.Interface(
fn=chat,
inputs=[input_box, max_tokens_slider],
outputs=output_box,
live=False # disable auto-triggering on input change
)
# launch Gradio Service
demo.queue()
demo.launch()
# Construct Gradio Interface
#interface = gr.Interface(
# fn=chat,
# inputs=[
# gr.Textbox(lines=8, label="输入你的问题"),
# gr.Slider(minimum=100, maximum=102400, step=50, label="生成长度")
# ],
# outputs=[
# gr.Textbox(lines=8, label="模型回复")
# ],
# title="Ling-lite-1.5 AI助手",
# description="基于 [inclusionAI/Ling-lite-1.5](https://huggingface.co/inclusionAI/Ling-lite-1.5) 的对话式文本生成演示。",
# examples=[
# ["介绍大型语言模型的基本概念"],
# ["如何解决数学问题中的长上下文依赖？"]
# ]
#)
# launch Gradion Service
#interface.launch()
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