app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
import torch

first = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.\n\ninformal english: """

@st.cache(allow_output_mutation=True)
def get_model():

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln86Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln86Paraphrase")
    
    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln82Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln82Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln79Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln79Paraphrase")
    
    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln74Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln74Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln72Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln72Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln64Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln64Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln60Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln60Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo1.3BInformalToFormal")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo1.3BInformalToFormal")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln55")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln55")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln51")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln51")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln45")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln49")
    
    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln43")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln43")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln41")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln38")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln38")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln37")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln37")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln36")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln36")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln35")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln35")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln31")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln31")
    
    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln21")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln21")
    
    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsOneSent")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsOneSent")
    
    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsToSentence")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsToSentence")
    tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln89Paraphrase")
    model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln89Paraphrase")
    tokenizer2 = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnMedium")
    model2 = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincolnMedium")
    return model, model2, tokenizer, tokenizer2
    
model, model2, tokenizer, tokenizer2 = get_model()

st.text('''For Prompt Templates: https://huggingface.co/BigSalmon/InformalToFormalLincoln82Paraphrase''')

temp = st.sidebar.slider("Temperature", 0.7, 1.5)
number_of_outputs = st.sidebar.slider("Number of Outputs", 5, 50)
lengths = st.sidebar.slider("Length", 3, 500)
bad_words = st.text_input("Words You Do Not Want Generated", " core lemon height time ")
logs_outputs = st.sidebar.slider("Logit Outputs", 50, 300)

def run_generate(text, bad_words):
  yo = []
  input_ids = tokenizer.encode(text, return_tensors='pt')
  res = len(tokenizer.encode(text))
  bad_words = bad_words.split()
  bad_word_ids = []
  for bad_word in bad_words: 
    bad_word = " " + bad_word
    ids = tokenizer(bad_word).input_ids
    bad_word_ids.append(ids)
  sample_outputs = model.generate(
    input_ids,
    do_sample=True, 
    max_length= res + lengths, 
    min_length = res + lengths, 
    top_k=50,
    temperature=temp,
    num_return_sequences=number_of_outputs,
    bad_words_ids=bad_word_ids
  )
  for i in range(number_of_outputs):
    e = tokenizer.decode(sample_outputs[i])
    e = e.replace(text, "")
    yo.append(e)
  return yo
  
def BestProbs5(prompt):
  prompt = prompt.strip()
  text = tokenizer.encode(prompt)
  myinput, past_key_values = torch.tensor([text]), None
  myinput = myinput
  logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
  logits = logits[0,-1]
  probabilities = torch.nn.functional.softmax(logits)
  best_logits, best_indices = logits.topk(number_of_outputs)
  best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
  for i in best_words[0:number_of_outputs]:
    #print(i)
    print("\n")
    g = (prompt + i)
    st.write(g)
    l = run_generate(g, "hey")
    st.write(l)
  
def run_generate2(text, bad_words):
  yo = []
  input_ids = tokenizer2.encode(text, return_tensors='pt')
  res = len(tokenizer2.encode(text))
  bad_words = bad_words.split()
  bad_word_ids = []
  for bad_word in bad_words: 
    bad_word = " " + bad_word
    ids = tokenizer2(bad_word).input_ids
    bad_word_ids.append(ids)
  sample_outputs = model2.generate(
    input_ids,
    do_sample=True, 
    max_length= res + lengths, 
    min_length = res + lengths, 
    top_k=50,
    temperature=temp,
    num_return_sequences=number_of_outputs,
    bad_words_ids=bad_word_ids
  )
  for i in range(number_of_outputs):
    e = tokenizer2.decode(sample_outputs[i])
    e = e.replace(text, "")
    yo.append(e)
  return yo
  
def prefix_format(sentence):
  words = sentence.split()
  if "[MASK]" in sentence:
    words2 = words.index("[MASK]")
    #print(words2)
    output = ("<Prefix> " + ' '.join(words[:words2]) + " <Prefix> " + "<Suffix> " + ' '.join(words[words2+1:]) + " <Suffix>" + " <Middle>")
    st.write(output)
  else:
    st.write("Add [MASK] to sentence")
 
with st.form(key='my_form'):
    text = st.text_area(label='Enter sentence', value=first)
    submit_button = st.form_submit_button(label='Submit')
    submit_button2 = st.form_submit_button(label='Submit Log Probs')
    
    submit_button3 = st.form_submit_button(label='Submit Other Model')
    submit_button4 = st.form_submit_button(label='Submit Log Probs Other Model')
    
    submit_button5 = st.form_submit_button(label='Most Prob')
    
    submit_button6 = st.form_submit_button(label='Turn Sentence with [MASK] into <Prefix> Format')
    
    if submit_button:
      translated_text = run_generate(text, bad_words)
      st.write(translated_text if translated_text else "No translation found")
    if submit_button2:
      with torch.no_grad():
        text2 = str(text)
        print(text2)
        text3 = tokenizer.encode(text2)
        myinput, past_key_values = torch.tensor([text3]), None
        myinput = myinput
        logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
        logits = logits[0,-1]
        probabilities = torch.nn.functional.softmax(logits)
        best_logits, best_indices = logits.topk(logs_outputs)
        best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]      
        st.write(best_words)
    if submit_button3:
      translated_text = run_generate2(text, bad_words)
      st.write(translated_text if translated_text else "No translation found")
    if submit_button4:
      text2 = str(text)
      print(text2)
      text3 = tokenizer2.encode(text2)
      myinput, past_key_values = torch.tensor([text3]), None
      myinput = myinput
      logits, past_key_values = model2(myinput, past_key_values = past_key_values, return_dict=False)
      logits = logits[0,-1]
      probabilities = torch.nn.functional.softmax(logits)
      best_logits, best_indices = logits.topk(logs_outputs)
      best_words = [tokenizer2.decode([idx.item()]) for idx in best_indices]      
      st.write(best_words)
    if submit_button5:
      BestProbs5(text)
    if submit_button6:
      text2 = str(text)
      prefix_format(text2)