Update app.py

app.py

@@ -0,0 +1,73 @@
+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
+
+tutorial_running(6)
+
+logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
+logging.getLogger("haystack").setLevel(logging.INFO)
+
+document_store = InMemoryDocumentStore()
+
+f = codecs.open('faq.txt','r','UTF-8')
+line = f.readlines()
+
+lines = []
+for i in range(2,33,2):
+    line.pop(i)
+
+for i in range(33):
+    line[i] = line[i][:-2]
+
+for i in range(33):
+    lines.append([line[i],line[i+1]])
+
+colu = ['question','answer']
+
+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)
+
+
+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")
+
+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)