import gradio as gr
import pandas as pd

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

#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

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):
    prediction = pipeline.run(question)
    answer =  prediction["answers"].to_dict()
    faq_response = "FAQ Question: " + answer["context"] + "Answer: " + answer["answer"]
    return faq_response
 
input_question =gr.inputs.Textbox(label="enter your monopoly question here")
response = "text"

gr.Interface(
    fn=predict,
    inputs=input_question,
    outputs=response,
    title="Monopoly FAQ Semantic Search",
).launch()