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(0,33,2):
    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'})

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}})
    answer = str(prediction['answers'][0])
    ans = answer.split(',')[0]
    return '亲，' + ans[19:]
    #prediction['answers'][0]
# 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="来自智能客服的回答")

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()