app.py

import gradio as gr
import os

from langchain.chains.question_answering import load_qa_chain
from langchain.document_loaders import UnstructuredURLLoader
# from langchain import HuggingFaceHub
from langchain import OpenAI

# os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"

# llm = HuggingFaceHub(repo_id="declare-lab/flan-alpaca-large", model_kwargs={"temperature":0.1, "max_length":512})

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
# os.environ["LANGCHAIN_API_KEY"] = "ls__ae9b316f4ee9475b84f66c616344d713"
# os.environ["LANGCHAIN_PROJECT"] = "Sequential-Chain"
# os.environ["OPENAI_API_KEY"] = 'sk-siyoMOttFuCrzfdETrRFS7bz140Dk5DUklCIW3UyVTzooiKj'
# os.environ['OPENAI_API_BASE'] = 'https://api.chatanywhere.com.cn'

llm=OpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=1024)

def main():

    input_api_key = gr.inputs.Textbox(label="API Key", lines=1)
    input_api_base = gr.inputs.Textbox(label="API Base", lines=1)
    input_url = gr.inputs.Textbox(label="URL", lines=1)
    gradio_interface = gr.Interface(fn=my_inference_function, inputs=[input_api_key, input_api_base, input_url], outputs="text")
    gradio_interface.launch()

def my_inference_function(api_key, api_base, url):
    os.environ["OPENAI_API_KEY"] = api_key
    os.environ['OPENAI_API_BASE'] = api_base

    loader = UnstructuredURLLoader(urls=[url])
    data = loader.load()
    chain = load_qa_chain(llm=llm, chain_type="stuff")
    response = chain.run(input_documents=data, question="""请用中文总结文章的内容，并以下面模版给出结果：
《文章标题》摘要如下：
## 一句话描述
文章摘要内容
## 文章略读
文章要点""")
    return response

if __name__ == '__main__':
    main()