Update app.py

app.py

@@ -1,72 +1,72 @@
-from haystack.telemetry import tutorial_running
-import logging
-from haystack.document_stores import InMemoryDocumentStore
-from haystack.pipelines.standard_pipelines import TextIndexingPipeline
-from haystack.nodes import BM25Retriever
-from haystack.nodes import FARMReader
-from haystack.pipelines import ExtractiveQAPipeline
-from pprint import pprint
-from haystack.utils import print_answers
-from haystack.nodes import EmbeddingRetriever
-import codecs
-from haystack.pipelines import FAQPipeline
-from haystack.utils import print_answers
-import logging
-from haystack.telemetry import tutorial_running
-from haystack.document_stores import InMemoryDocumentStore
-from haystack.nodes import EmbeddingRetriever
-import pandas as pd
-from haystack.pipelines import FAQPipeline
-from haystack.utils import print_answers
+从haystack.telemetry导入tutorial_running
+导入日志
+从haystack.document_stores导入InMemoryDocumentStore
+从haystack.pipelines.standard_pipelines导入TextIndexingPipeline
+从haystack.nodes导入BM25Retriever
+从haystack.nodes导入FARMReader
+从haystack.pipelines导入ExtractiveQAPipeline
+从pprint导入pprint
+从haystack.utils导入print_answers
+从 haystack.nodes 导入 EmbeddingRetriever
+导入解码器
+从 haystack.pipelines 导入 FAQPipeline
+从 haystack.utils 导入 print_answers
+导入日志
+从 haystack.telemetry 导入tutorial_running
+从 haystack.document_stores 导入 InMemoryDocumentStore
+从 haystack.nodes 导入 EmbeddingRetriever
+将熊猫导入为pd
+从 haystack.pipelines 导入 FAQPipeline
+从 haystack.utils 导入 print_answers
 
-tutorial_running(6)
+教程_运行（6）
 
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
+logging.basicConfig(format= "%(levelname)s - %(name)s - %(message)s" , level=logging.WARNING)
+logging.getLogger( “干草堆” ).setLevel(logging.INFO)
 
-document_store = InMemoryDocumentStore()
+文档存储器 = InMemoryDocumentStore()
 
-f = codecs.open('faq.txt','r','UTF-8')
-line = f.readlines()
-lines = []
-for i in range(2,33,2):
-    line.pop(i)
+f = codecs.open( 'faq.txt' , 'r' , 'UTF-8' )
+行 = f.readlines()
+行 = []
+对于范围内的 i ( 2 , 33 , 2 ):
+    行.pop(i)
 
-for i in range(33):
-    line[i] = line[i][:-2]
+对于我在范围内（33）：
+    行[i] = 行[i][:- 2 ]
 
-for i in range(0,33,2):
-    lines.append([line[i],line[i+1]])
+对于我在范围内（0、33、2）： _
+    lines.append([line[i],line[i+ 1 ]])
 
-colu = ['question','answer']
+colu = [ '问题' , '答案' ]
 
-df = pd.DataFrame(data=lines, columns=colu)
-retriever = EmbeddingRetriever(
-        document_store=document_store,
-        embedding_model="sentence-transformers/all-MiniLM-L6-v2",
-        use_gpu=True,
-        scale_score=False,
+df = pd.DataFrame（数据=行，列=列）
+检测器 = EmbeddingRetriever(
+        文档存储器=文档存储器，
+        embedding_model= "句子变换器/all-MiniLM-L6-v2" ,
+        use_gpu=真，
+        scale_score=假,
     )
-question = list(df['question'].values)
 
-df['embedding'] = retriever.embed_queries(queries=question).tolist()
-df = df.rename(columns={'question': 'content'})
-question = list(df['question'].values)
-docs_to_index = df.to_dict(orient='records')
-document_store.write_documents(docs_to_index)
 
+df[ '嵌入'] = retriever.embed_queries(查询=问题).tolist()
+df = df.rename(columns={ 'question' : 'content' })
+问题 = 列表（df[ '问题' ].values）
+docs_to_index = df.to_dict(orient= '记录' )
+document_store.write_documents（docs_to_index）
 
-def haysstack(input,retriever=retriever):
-    pipe = FAQPipeline(retriever=retriever)
-    prediction = pipe.run(query=input, params={"Retriever": {"top_k": 1}})
-    return prediction['answers']
-# Run any question and change top_k to see more or less answers
 
-import gradio as gr
-from gradio.components import Textbox
-inputs = Textbox(lines=7, label="请输入你的问题")
-outputs = Textbox(lines=7, label="来自ChatGPT的回答")
+定义haysstack（输入，检查器=检查器）：
+    pipe = FAQPipeline（检测器=检测器）
+    prediction = pipe.run(query=input, params={ "Retriever" : { "top_k" : 1 }})
+    返回预测
+# 运行任何问题并更改 top_k 以查看更多或更少的答案
 
-gr.Interface(fn=haysstack, inputs=inputs, outputs=outputs, title="电商客服",
-             description="我是您的电商客服，您可以问任何你想知道的问题",
-             theme=gr.themes.Default()).launch(share=True)
+将易变导入为gr
+从gradio.components导入文档
+inputs = Textbox(lines= 7 , label= "请输入你的问题" )
+outputs = Textbox(lines= 7 , label= "来自ChatGPT的回答" )
+
+gr.Interface(fn=haysstack, inputs=inputs, outputs=outputs, title= "电商客服" ,
+             description= "我是您的电商客服，您可以问任何您想知道的问题" ,
+             主题=gr.themes.Default()).launch(share= True )