更新样式

app.py

@@ -5,7 +5,8 @@ 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[
+    "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"
@@ -13,42 +14,53 @@ os.environ["LANGCHAIN_PROJECT"] = "Sequential-Chain"
 
 
 def main():
-    input_checkbox = gr.inputs.Checkbox(label="启用ChatGPT", default=False)
+    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_url = gr.inputs.Textbox(label="URL", lines=1)
     outputs = "text"
-    interface = gr.Interface(fn=check_model, inputs=input_checkbox, outputs=outputs)
+    with gr.Blocks() as demo:
+        with gr.Tab("Component 1"):  #标签页1
+            interface = gr.Interface(fn=my_inference_function,
+                                     inputs=[input_url],
+                                     outputs=outputs)
+        with gr.Tab("Component 2"):  #标签页2
+            interface = gr.Interface(
+                fn=my_chatgpt_function,
+                inputs=[input_api_key, input_api_base, input_url],
+                outputs=outputs)
     interface.launch()
 
-def check_model(enabled):
-  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_url = gr.inputs.Textbox(label="URL", lines=1)
-  if enabled:
-    return [input_url,input_api_key, input_api_base]
-  else:
-    return [input_url]
 
 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：
+    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
+    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
+    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()