app.py

import gradio
import os

from langchain.chains.question_answering import load_qa_chain
from langchain.document_loaders import UnstructuredURLLoader
from langchain import HuggingFaceHub
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"

openai.api_key = 'sk-siyoMOttFuCrzfdETrRFS7bz140Dk5DUklCIW3UyVTzooiKj'
openai.api_base = 'https://api.chatanywhere.com.cn'

llm = openai.Completion.create(
        engine="text-davinci-003",  # 使用 GPT-3.5 Turbo 引擎
        max_tokens=50,  # 设置生成的回复最大长度
        temperature=0.7,  # 控制生成回复的随机性
        n=1,  # 生成一个回复
        stop=None,  # 可选的停止标记，用于结束回复的生成
    )

def main():

    gradio_interface = gradio.Interface(
    fn = my_inference_function,
    inputs = "text",
    outputs = "text")

    gradio_interface.launch()


def my_inference_function(url):
  loader = UnstructuredURLLoader(urls=[url])
  data = loader.load()
  chain = load_qa_chain(llm=llm, chain_type="stuff")
  response = chain.run(input_documents=data, question="Summarize this article in a paragraph and provide a name and link")
  return response

if __name__ == '__main__':
    main()