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| import gradio as gr | |
| import os | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain.document_loaders import UnstructuredURLLoader | |
| from langchain import OpenAI | |
| from langchain import HuggingFaceHub | |
| os.environ[ | |
| "HUGGINGFACEHUB_API_TOKEN"] = "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" | |
| 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" | |
| def main(): | |
| with gr.Blocks() as demo: | |
| with gr.Tab(label="HuggingFaceHub", id="tab1"): #标签页1 | |
| input_url1 = gr.inputs.Textbox(label="输入要总结的 URL", lines=1) | |
| text_button = gr.Button("提交") | |
| text_output_interpret = gr.TextArea(label="结果") | |
| text_button.click(fn=my_inference_function,inputs=input_url1,outputs=text_output_interpret) | |
| with gr.Tab(label="ChatGPT", id="tab2"): #标签页2 | |
| input_api_key = gr.inputs.Textbox(label="ChatGPT API Key", lines=1) | |
| input_api_base = gr.inputs.Textbox(label="ChatGPT API 地址（默认无地址）", lines=1) | |
| input_url2 = gr.inputs.Textbox(label="输入要总结的 URL", lines=1) | |
| vid_button = gr.Button("提交") | |
| vid_output_interpret = gr.TextArea(label="结果") | |
| vid_button.click(fn=my_chatgpt_function,inputs=[input_api_key, input_api_base, input_url2],outputs=vid_output_interpret) | |
| demo.launch() | |
| def my_chatgpt_function(api_key, api_base, url): | |
| os.environ["OPENAI_API_KEY"] = api_key | |
| os.environ['OPENAI_API_BASE'] = api_base | |
| llm = OpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=1024) | |
| 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 | |
| def my_inference_function(url): | |
| llm = HuggingFaceHub(repo_id="declare-lab/flan-alpaca-large", | |
| model_kwargs={ | |
| "temperature": 0.1, | |
| "max_length": 512 | |
| }) | |
| 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 one paragraph") | |
| return response | |
| if __name__ == '__main__': | |
| main() | |