from transformers import AutoModelForMaskedLM , AutoModelForSequenceClassification
from transformers import AutoTokenizer
from sklearn.metrics.pairwise import cosine_similarity
import streamlit as st
import torch
import pickle
import numpy as np
import itertools

choice = st.radio("Choose model",["distilbert-cvent","finbert"])
if choice == "distilbert-cvent":
  model_checkpoint = "vives/distilbert-base-uncased-finetuned-cvent-2019_2022"
  model = AutoModelForMaskedLM.from_pretrained(model_checkpoint, output_hidden_states=True)
  tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
elif choice == "finbert":
  model_checkpoint = "ProsusAI/finbert"
  tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
  model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert", output_hidden_states=True)

text = st.text_input("Enter word or key-phrase")

exclude_words = st.radio("exclude_words",[True,False], help="Exclude results that contain any words in the query (i.e exclude 'hot coffee' if the query is 'cold coffee')")

exclude_text = st.radio("exclude_text",[True,False], help="Exclude results that contain the query (i.e exclude 'tomato soup recipe' if the query is 'tomato soup')")
k = st.number_input("Top k nearest key-phrases",1,10,5)

with st.sidebar:
  diversify_box = st.checkbox("Diversify results",True)
  if diversify_box:
    k_diversify = st.number_input("Set of key-phrases to diversify from",10,30,20)

#load kp dict
with open("kp_dict_merged.pickle",'rb') as handle:
  kp_dict = pickle.load(handle)
keys = list(kp_dict.keys())

for key in kp_dict.keys():
  kp_dict[key] = kp_dict[key].detach().numpy()

#load cosine distances of kp dict
with open("cosine_kp.pickle",'rb') as handle:
  cosine_kp = pickle.load(handle)
  
def calculate_top_k(out, tokens,text,exclude_text=False,exclude_words=False, k=5):
  sim_dict = {}
  pools = pool_embeddings(out, tokens).detach().numpy()
  for key in kp_dict.keys():
    if key == text:
      continue
    if exclude_text and text in key:
      continue
    if exclude_words and True in [x in key for x in text.split(" ")]:
      continue
    sim_dict[key] = cosine_similarity(
        pools,
        [kp_dict[key]]
    )[0][0]
  sims = sorted(sim_dict.items(), key= lambda x: x[1], reverse = True)[:k]
  return {x:y for x,y in sims}
def concat_tokens(sentences):
  tokens = {'input_ids': [], 'attention_mask': [], 'KPS': []}
  for sentence in sentences:
      # encode each sentence and append to dictionary
      new_tokens = tokenizer.encode_plus(sentence, max_length=64,
                                         truncation=True, padding='max_length',
                                         return_tensors='pt')
      tokens['input_ids'].append(new_tokens['input_ids'][0])
      tokens['attention_mask'].append(new_tokens['attention_mask'][0])
      tokens['KPS'].append(sentence)
  # reformat list of tensors into single tensor
  tokens['input_ids'] = torch.stack(tokens['input_ids'])
  tokens['attention_mask'] = torch.stack(tokens['attention_mask'])
  return tokens

def pool_embeddings(out, tok):
  embeddings = out["hidden_states"][-1]
  attention_mask = tok['attention_mask']
  mask = attention_mask.unsqueeze(-1).expand(embeddings.size()).float()
  masked_embeddings = embeddings * mask
  summed = torch.sum(masked_embeddings, 1)
  summed_mask = torch.clamp(mask.sum(1), min=1e-9)
  mean_pooled = summed / summed_mask
  return mean_pooled
  
def extract_idxs(top_dict, kp_dict):
  idxs = []
  c = 0
  for i in list(kp_dict.keys()):
    if i in top_dict.keys():
      idxs.append(c)
    c+=1
  return idxs
  
if text:
  text = text.lower()
  new_tokens = concat_tokens([text])
  new_tokens.pop("KPS")
  with torch.no_grad():
    outputs = model(**new_tokens)
  if not diversify_box:
    sim_dict = calculate_top_k(outputs, new_tokens, text, exclude_text=exclude_text,exclude_words=exclude_words,k=k)
    st.json(sim_dict)
  else:
    sim_dict = calculate_top_k(outputs, new_tokens, text, exclude_text=exclude_text,exclude_words=exclude_words,k=k_diversify)
    idxs = extract_idxs(sim_dict, kp_dict)
    distances_candidates = cosine_kp[np.ix_(idxs, idxs)]
    min_sim = np.inf
    candidate = None
    for combination in itertools.combinations(range(len(idxs)), k):
      sim = sum([distances_candidates[i][j] for i in combination for j in combination if i != j])
      if sim < min_sim:
        candidate = combination
        min_sim = sim
    ret = {keys[idxs[idx]]:sim_dict[keys[idxs[idx]]] for idx in candidate}
    ret = sorted(ret.items(), key= lambda x: x[1], reverse = True)
    ret = {x:y for x,y in ret}
    st.json(ret)