from haystack.document_stores import FAISSDocumentStore
from haystack.nodes import DensePassageRetriever, FARMReader
from haystack.pipelines import ExtractiveQAPipeline, DocumentSearchPipeline
from haystack.utils import clean_wiki_text, print_answers
from sentence_transformers import SentenceTransformer
from haystack.nodes import EmbeddingRetriever

import streamlit as st
import os
import pandas as pd
from datetime import datetime
import json
import re



def intializeFAISS(docs):
    try:        
        db_file_name='faiss_document_store.db'
        faiss_index_file='faiss_index'

        if 'reader' not in st.session_state:
            st.session_state.reader=FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=False)    

        if  os.path.exists(faiss_index_file):
            document_store=FAISSDocumentStore(
                sql_url=f"sqlite:///{db_file_name}",
                index=faiss_index_file,
                embedding_dim=384
            )  
        else:       
            document_store = FAISSDocumentStore(embedding_dim=384, faiss_index_factory_str="Flat")

        if 'retriever' not in st.session_state:
            st.session_state.retriever = EmbeddingRetriever(
                            document_store=document_store,
                            embedding_model="sentence-transformers/all-MiniLM-L6-v2",
                            model_format="sentence_transformers",
                            use_gpu=False
                        ) 
            
        document_store.delete_all_documents()
        document_store.write_documents(docs)      
        document_store.update_embeddings(st.session_state.retriever)
        document_store.save(index_path=faiss_index_file)  

    except Exception as ex:
        print('--error--')
        print('--intialization method--')        
        print(ex)
        
def dummy(r):
    return{

    }

def prepare_doc(r):
        _content=f'''{r['Salutation']} {r['Initial']} {r['Name']}, Employee Id is {r['Employee Id']} and user id is {r['User Id']},  is from country {r['Country']},  Supervisor / Manager name is {r['Supervisor / Manager']}  and Contracting Company is  {r['Contracting Company']},  Primary Industry is {r['Primary Industry']}  and Secondary Industry	is {r['Secondary Industry']}, there Sector is {r['Sector']}, they are expertise in {r['Expertise']}, there role is {r['Industry Role']}. There last Last Promotion Date is  {r['Last Promotion Date']} and {r['Last Promotional Level']}, There Job Title is {r['Job Title']}, they are working here since {r['Professional Since']}, there hired date is {r['Hired Date']}, there Relevant is {r['Relevant']}, Employee Sponser is {r['Employee Sponser']}, Job Description is {r['Job Description']},  Emergency Contact Name is {r['Emergency Contact Name']} and Emergency Contact Number  is {r['Emergency Contact Number']}, Regional Supervisor is {r['Regional Supervisor']}  Office Supervisor is {r['Office Supervisor']} Engagement Supervisor is {r['Engagement Supervisor']} '''
        return {
            'content':_content,
            'meta':{
                "Salutation":r["Salutation"],
                "Initial":r["Initial"],
                "User Id" :r["User Id"],
                "Name" :r["Name"],
                "Employee Id" :r["Employee Id"],
                "Country":r["Country"],
                "Supervisor / Manager" :r["Supervisor / Manager"],
                "Contracting Company" :r["Contracting Company"],
                "Primary Industry":r["Primary Industry"],
                "Secondary Industry" :r["Secondary Industry"],
                "Sector" :r["Sector"],
                "Expertise":r["Expertise"],
                "Industry Role":r["Industry Role"],
                "Designation" :r["Designation"],
                "Grade" :r["Grade"],
                "Target Chargeability %" :r["Target Chargeability %"],
                "Charge Out Rate":r["Charge Out Rate"],
                "Last Promotion Date" :r["Last Promotion Date"],
                "Last Promotional Level" :r["Last Promotional Level"],
                "Job Title":r["Job Title"],
                "Professional Since" :r["Professional Since"],
                "Hired Date" :r["Hired Date"],
                "Relevant":r["Relevant"],
                "Employee Sponser":r["Employee Sponser"],
                "Job Description" :r["Job Description"],
                "Emergency Contact Name" :r["Emergency Contact Name"],
                "Emergency Contact Number":r["Emergency Contact Number"],
                "Regional Supervisor" :r["Regional Supervisor"],
                "Office Supervisor" :r["Office Supervisor"],
                "Engagement Supervisor":r["Engagement Supervisor"]
            }
        }


def on_submission(question):
    try:        
        print('reader')
        print(st.session_state.reader)
        print('retriever')
        print(st.session_state.retriever)
        qa_pipeline=ExtractiveQAPipeline(reader=st.session_state.reader,retriever=st.session_state.retriever)
        prediction=qa_pipeline.run(query=question,params={
            "Retriever":{"top_k":5},
            "Reader":{"top_k":1}
                                            })
        
        if prediction:            
            text=str(prediction['answers'][0])
                

    except Exception as ex:
        print('--error--')
        print('--on_submission method--')        
        print(ex)

uploaded_file=st.file_uploader(label='please upload your file',type=['csv'])

if uploaded_file is not None:
    try:
        df=pd.read_csv(uploaded_file)
        employee_list=df.to_json(orient='records')
        doc=[]
        json_emp=json.loads(employee_list)
        for e in json_emp:
            doc.append(prepare_doc(e))

        intializeFAISS(docs=doc)
    except Exception as ex:
        print('--uploaded_file method--')
        print(ex)

#intialize form
with st.form(key='workforce_management'):
    st.title('workforce management')    
    
    question=st.text_input(label='please ask your question.')    
    submitted=st.form_submit_button(label='submit')

if submitted and question:
    on_submission(question)