Update app.py

app.py

@@ -1,72 +1,72 @@
-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
+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
 
-教程_运行（6）
+tutorial_running(6)
 
-logging.basicConfig(format= "%(levelname)s - %(name)s - %(message)s" , level=logging.WARNING)
-logging.getLogger( “干草堆” ).setLevel(logging.INFO)
+logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
+logging.getLogger("haystack").setLevel(logging.INFO)
 
-文档存储器 = InMemoryDocumentStore()
+document_store = InMemoryDocumentStore()
 
-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)
+f = codecs.open('faq.txt','r','UTF-8')
+line = f.readlines()
+lines = []
+for i in range(2,33,2):
+    line.pop(i)
 
-对于我在范围内（33）：
-    行[i] = 行[i][:- 2 ]
+for i in range(33):
+    line[i] = line[i][:-2]
 
-对于我在范围内（0、33、2）： _
-    lines.append([line[i],line[i+ 1 ]])
+for i in range(0,33,2):
+    lines.append([line[i],line[i+1]])
 
-colu = [ '问题' , '答案' ]
+colu = ['question','answer']
 
-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,
+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,
     )
+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'].split(',')
+# Run any question and change top_k to see more or less answers
 
-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
+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="来自智能客服的回答")
 
-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 )
+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)