app.py

import gradio as gr
import pandas as pd

from haystack.schema import Answer
from haystack.document_stores import InMemoryDocumentStore
from haystack.pipeline import FAQPipeline
from haystack.retriever.dense import EmbeddingRetriever
from haystack.utils import print_answers
import logging

#Haystack function calls - streamlit structure from Tuana GoT QA Haystack demo
@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True) # use streamlit cache

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

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)
 
pipeline = start_haystack()
 
def predict(question):
  predictions = pipeline.run(question)
  answer =  predictions["answers"]
  return answer
 
 gr.Interface(
    predict,
    inputs=gr.inputs.Textbox(label="enter your monopoly question here"),
    outputs=gr.outputs.Label(num_top_classes=1),
    title="Monopoly FAQ Semantic Search",
).launch()