import streamlit as st

import numpy as np
import re
import pickle
from collections import OrderedDict
import io

from sentence_transformers import SentenceTransformer, CrossEncoder, util
import torch

from nltk.tokenize import sent_tokenize
import nltk

import gdown
import requests


nltk.download('punkt')

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

import pandas as pd

purl = st.secrets["graphs_url"]
print(purl)


@st.cache
def load_embeddings():
    url = "https://drive.google.com/uc?export=download&id=1z9eoBI07p_YtrdK1ZWZeCRT5T5mu5nhV"
    output = "embeddings.npy"
    gdown.download(url, output, quiet=False)

    corpus_embeddings = np.load(output)
    return corpus_embeddings

@st.cache
def load_data(url):
    #url = "https://drive.google.com/uc?export=download&id=1nIBS9is8YCeiPBqA7MifVC5xeaKWH8uL"
    output = "passages.jsonl"
    gdown.download(url, output, quiet=False)

    df = pd.read_json(output, lines=True)

    df.reset_index(inplace=True, drop=True)
    return df


st.title('Sociology Paragraph Search')

st.write('This project is a work-in-progress that searches the text of recently-published articles from a few sociology journals and retrieves the most relevant paragraphs.')


with st.spinner(text="Loading data..."):
    df = load_data(purl)
    passages = df['text'].values

no_of_graphs=len(df)
no_of_articles = len(df['cite'].value_counts())


notes = f'''Notes:
* I have found three types of searches work best:
     * Phrases or specific topics, such as "inequality in latin america", "race color skin tone measurement", "audit study experiment gender",  or "logistic regression or linear probability model".
     * Citations to well-known works, either using author year ("bourdieu 1984") or author idea ("Crenshaw intersectionality")
    * Questions, like "What is a topic model?" or "How did Weber define bureaucracy?"
* The search expands beyond exact matching, so "asia social movements" may return paragraphs on Asian-Americans politics and South Korean labor unions.
* The first search can take up to 10 seconds as the files load. After that, it's quicker to respond.
* The most relevant paragraph to your search is returned first, along with up to four other related paragraphs from that article.
* The most relevant sentence within each paragraph, as determined by math, is displayed. Click on it to see the full paragraph.
* The results are not exhaustive, and seem to drift off even when you suspect there are more relevant articles :man-shrugging:.
* The dataset currently includes articles published in the last five years in *Mobilization*, *Social Forces*, *Social Problems*, *Sociology of Race and Ethnicity*, *Gender and Society*, *Socius*, *JHSB*, *Annual Review of Sociology*, and the *American Sociological Review*.
* Behind the scenes, the semantic search uses [text embeddings](https://www.sbert.net) with a [retrieve & re-rank](https://colab.research.google.com/github/UKPLab/sentence-transformers/blob/master/examples/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) process to find the best matches.
* Let [me](mailto:neal.caren@unc.edu) know what you think or it looks broken.
'''

st.markdown(notes)


def sent_trans_load():
    #We use the Bi-Encoder to encode all passages, so that we can use it with sematic search
    bi_encoder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
    bi_encoder.max_seq_length = 256     #Truncate long passages to 256 tokens, max 512
    return bi_encoder

def sent_cross_load():
    #We use the Bi-Encoder to encode all passages, so that we can use it with sematic search
    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
    return cross_encoder







with st.spinner(text="Loading embeddings..."):
    corpus_embeddings = load_embeddings()





def search(query, top_k=50):

    ##### Sematic Search #####
    # Encode the query using the bi-encoder and find potentially relevant passages
    question_embedding = bi_encoder.encode(query, convert_to_tensor=True).to(device)


    hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)
    hits = hits[0]  # Get the hits for the first query
    ##### Re-Ranking #####
    # Now, score all retrieved passages with the cross_encoder
    cross_inp = [[query, passages[hit['corpus_id']]] for hit in hits]
    cross_scores = cross_encoder.predict(cross_inp)

    # Sort results by the cross-encoder scores
    for idx in range(len(cross_scores)):
        hits[idx]['cross-score'] = cross_scores[idx]

    # Output of top-5 hits from re-ranker
    print("\n-------------------------\n")
    print("Search Results")
    hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)

    hd = OrderedDict()
    for hit in hits[0:30]:

        row_id = hit['corpus_id']
        cite = df.loc[row_id]['cite']
        #graph = passages[row_id]
        graph = df.loc[row_id]['text']

        # Find best sentence
        ab_sentences= [s for s in sent_tokenize(graph)]
        cross_inp = [[query, s] for s in ab_sentences]
        cross_scores = cross_encoder.predict(cross_inp)
        thesis = pd.Series(cross_scores, ab_sentences).sort_values().index[-1]
        graph = graph.replace(thesis, f'**{thesis}**')

        if cite in hd:

          hd[cite].append(graph)
        else:
          hd[cite] = [graph]

    for cite, graphs in hd.items():
        cite = cite.replace(",  ", '. "').replace(', Social ', '", Social ')
        st.write(cite)

        for graph in graphs[:5]:
          # refind the Thesis
          thesis = re.findall('\*\*(.*?)\*\*', graph)[0]

          with st.expander(thesis):
             st.write(f'* {graph}')
        st.write('')
       # print("\t{:.3f}\t{}".format(hit['cross-score'], passages[hit['corpus_id']].replace("\n", " ")))



search_query = st.text_input('Enter your search phrase:')
if search_query!='':
    with st.spinner(text="Searching and sorting results."):
        bi_encoder = sent_trans_load()
        cross_encoder = sent_cross_load()
        search(search_query)