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import gradio as gr
import pandas as pd
from haystack.schema import Answer
from haystack.document_stores import InMemoryDocumentStore
from haystack.pipelines import FAQPipeline, ExtractiveQAPipeline
from haystack.nodes import EmbeddingRetriever, TfidfRetriever, FARMReader, PDFToTextConverter
from haystack.utils import print_answers
from haystack.utils import convert_files_to_docs
import logging
# FAQ Haystack function calls
def start_haystack():
document_store = InMemoryDocumentStore(index="document", embedding_field='embedding', embedding_dim=384, similarity='cosine')
retriever = EmbeddingRetriever(document_store=document_store, embedding_model='sentence-transformers/all-MiniLM-L6-v2', use_gpu=True, top_k=1)
load_data_to_store(document_store,retriever)
pipeline = FAQPipeline(retriever=retriever)
return pipeline, document_store
def load_data_to_store(document_store, retriever):
df = pd.read_csv('monopoly_qa-v1.csv')
questions = list(df.Question)
df['embedding'] = retriever.embed_queries(texts=questions)
df = df.rename(columns={"Question":"content","Answer":"answer"})
df.drop('link to source (to prevent duplicate sources)',axis=1, inplace=True)
dicts = df.to_dict(orient="records")
document_store.write_documents(dicts)
faq_pipeline, doc_store = start_haystack()
def predict_faq(question):
prediction = faq_pipeline.run(question)
answer = prediction["answers"][0].meta
faq_response = "FAQ Question: " + answer["query"] + "\n"+"Answer: " + answer["answer"]
return faq_response
# Extractive QA functions
def_start_ex_haystack():
return true
# Gradio App section
input_question =gr.inputs.Textbox(label="enter your monopoly question here")
response = "text"
examples = ["how much cash do we get to start with?", "at what point can I buy houses?", "what happens when I land on free parking?"]
mon_faq = gr.Interface(
fn=predict_faq,
inputs=input_question,
outputs=response,
examples=examples,
title="Monopoly FAQ Semantic Search")
# def return_feedback(input_question,feedback_answer):
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