import pandas as pd
import os
import pickle
from sentence_transformers import SentenceTransformer, util
import streamlit as st
import io
import torch

@st.cache(allow_output_mutation=True)
def load_model():
    return SentenceTransformer('all-MiniLM-L6-v2')

def find_top_similar(sentence, corpus_sentences, corpus_embeddings):

    # preprocess query
    model = load_model()
    query_embeddings = model.encode(sentence, convert_to_tensor=True) # encode to tensor
    # query_embeddings = query_embeddings.to('cuda') # put into gpu
    query_embeddings = util.normalize_embeddings(query_embeddings) # normalize

    # find the closest 5 sentences of the corpus for each query sentence based on cosine similarity
    hits = util.semantic_search(query_embeddings,
                                corpus_embeddings,
                                top_k=len(corpus_embeddings),
                                score_function=util.dot_score)
    hits = hits[0] # get the hits for the first query

    # Create dataframe to store top searches
    records = []

    for hit in hits[0:len(corpus_embeddings)]:
        records.append(corpus_sentences[hit['corpus_id']])

    return records

def top_k_similarity(df, query, corpus_sentences, corpus_embeddings):
    hits = find_top_similar([query], corpus_sentences, corpus_embeddings)

    res = pd.DataFrame()

    for h in hits:
        s = df[df['Last job role'] == h]
        res = pd.concat([res, s])

    return res

def get_result(df, query, corpus_sentences, corpus_embeddings):
    result = top_k_similarity(df, query, corpus_sentences, corpus_embeddings)
    result.drop_duplicates(inplace=True)
    return result

class cpu_unpickler(pickle.Unpickler):
    """
    Overrides the default behavior of the `Unpickler` class to load
    a `torch.storage` object from abyte string
    """
    def find_class(self, module, name):
        if module == 'torch.storage' and name == '_load_from_bytes':
            return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
        return super().find_class(module, name)

@st.cache(allow_output_mutation=True)
def load_embedding():
    """Loads the embeddings from the pickle file"""
    with open('corpus_embeddings.pkl', 'rb') as file:
        cache_data = cpu_unpickler(file).load()
        corpus_sentences = cache_data['sentences']
        corpus_embeddings = cache_data['embeddings']

    return corpus_sentences, corpus_embeddings

def main():
    # get dataset
    sheet_id = '1KeuPPVw9gueNmMrQXk1uGFlY9H1vvhErMLiX_ZVRv_Y'
    sheet_name = 'Form Response 3'.replace(' ', '%20')
    url = f'https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}'
    print(url)
    df = pd.read_csv(url)
    df  = df.iloc[: , :7]

    # get embeddings
    corpus_sentences, corpus_embeddings = load_embedding()

    # streamlit form
    st.title('Job Posting Similarity')
    job_title = st.text_input('Insert the job title below:', '')
    submitted = st.button('Submit')

    if submitted:
        result = get_result(df, job_title, corpus_sentences, corpus_embeddings)
        result.reset_index(drop=True, inplace=True)
        result.index += 1
        st.table(result)

if __name__ == '__main__':
    main()