app.py

import streamlit as st
import numpy as np
import pandas as pd
import os
import torch
import torch.nn as nn
from transformers.activations import get_activation
from transformers import AutoTokenizer, AutoModelForCausalLM


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

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/DefinitionsSynonyms1")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/DefinitionsSynonyms1")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln95Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln95Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/AbstractTest")
    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln99Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/AbstractTest")

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

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln103Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln103Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln105Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln105Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln106Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln106Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln109Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln109Paraphrase")

    tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln113Paraphrase")
    model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln113Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln110Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln110Paraphrase")

    #BigSalmon/InformalToFormalLincoln113Paraphrase

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln101Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln101Paraphrase")

    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/DefinitionsSynonyms2")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/DefinitionsSynonyms2")
    #tokenizer2 = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnMedium")
    #model2 = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincolnMedium")
    return model, tokenizer
    
model, tokenizer = get_model()

g = """informal english: garage band has made people who know nothing about music good at creating music.
Translated into the Style of Abraham Lincoln: garage band ( offers the uninitiated in music the ability to produce professional-quality compositions / catapults those for whom music is an uncharted art the ability the realize masterpieces / stimulates music novice's competency to yield sublime arrangements / begets individuals of rudimentary musical talent the proficiency to fashion elaborate suites ).
informal english: chrome extensions can make doing regular tasks much easier to get done.
Translated into the Style of Abraham Lincoln: chrome extensions ( yield the boon of time-saving convenience / ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks / turbocharges the velocity with which one can conduct their obligations ).
informal english: broadband is finally expanding to rural areas, a great development that will thrust them into modern life.
Translated into the Style of Abraham Lincoln: broadband is ( ( finally / at last / after years of delay ) arriving in remote locations / springing to life in far-flung outposts / inching into even the most backwater corners of the nation ) that will leap-frog them into the twenty-first century.
informal english: google translate has made talking to people who do not share your language easier.
Translated into the Style of Abraham Lincoln: google translate ( imparts communicability to individuals whose native tongue differs / mitigates the trials of communication across linguistic barriers / hastens the bridging of semantic boundaries / mollifies the complexity of multilingual communication / avails itself to the internationalization of discussion / flexes its muscles to abet intercultural conversation / calms the tides of linguistic divergence ).
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated 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.
informal english: """

number_of_outputs = st.sidebar.slider("Number of Outputs", 5, 100)
log_nums = st.sidebar.slider("How Many Log Outputs?", 50, 1000)

def BestProbs(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(10)
  best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
  for i in best_words[0:10]:
    print("_______")
    st.write(f"${i} $\n")
    f = (f"${i} $\n")
    m = (prompt + f"{i}")
    BestProbs2(m)
  return f

def BestProbs2(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(20)
  best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
  for i in best_words[0:20]:
    print(i)
    st.write(i)
    
def LogProbs(prompt):
  col1 = []
  col2 = []
  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(10)
  best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
  for i in best_words[0:10]:
    print("_______")
    f = i
    col1.append(f)
    m = (prompt + f"{i}")
    #print("^^" + f + " ^^")
    prompt = m.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(20)
    best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
    for i in best_words[0:20]:
      #print(i)
      col2.append(i)
  #print(col1)
  #print(col2)
  d = {col1[0]: [col2[0], col2[1], col2[2], col2[3], col2[4], col2[5], col2[6], col2[7], col2[8], col2[9], col2[10], col2[11], col2[12], col2[13], col2[14], col2[15], col2[16], col2[17], col2[18], col2[19]],
    col1[1]: [col2[20], col2[21], col2[22], col2[23], col2[24], col2[25], col2[26], col2[27], col2[28], col2[29], col2[30], col2[31], col2[32], col2[33], col2[34], col2[35], col2[36], col2[37], col2[38], col2[39]],
    col1[2]: [col2[40], col2[41], col2[42], col2[43], col2[44], col2[45], col2[46], col2[47], col2[48], col2[49], col2[50], col2[51], col2[52], col2[53], col2[54], col2[55], col2[56], col2[57], col2[58], col2[59]],
    col1[3]: [col2[60], col2[61], col2[62], col2[63], col2[64], col2[65], col2[66], col2[67], col2[68], col2[69], col2[70], col2[71], col2[72], col2[73], col2[74], col2[75], col2[76], col2[77], col2[78], col2[79]],
    col1[4]: [col2[80], col2[81], col2[82], col2[83], col2[84], col2[85], col2[86], col2[87], col2[88], col2[89], col2[90], col2[91], col2[92], col2[93], col2[94], col2[95], col2[96], col2[97], col2[98], col2[99]],
    col1[5]: [col2[100], col2[101], col2[102], col2[103], col2[104], col2[105], col2[106], col2[107], col2[108], col2[109], col2[110], col2[111], col2[112], col2[113], col2[114], col2[115], col2[116], col2[117], col2[118], col2[119]],
    col1[6]: [col2[120], col2[121], col2[122], col2[123], col2[124], col2[125], col2[126], col2[127], col2[128], col2[129], col2[130], col2[131], col2[132], col2[133], col2[134], col2[135], col2[136], col2[137], col2[138], col2[139]],
    col1[7]: [col2[140], col2[141], col2[142], col2[143], col2[144], col2[145], col2[146], col2[147], col2[148], col2[149], col2[150], col2[151], col2[152], col2[153], col2[154], col2[155], col2[156], col2[157], col2[158], col2[159]],
    col1[8]: [col2[160], col2[161], col2[162], col2[163], col2[164], col2[165], col2[166], col2[167], col2[168], col2[169], col2[170], col2[171], col2[172], col2[173], col2[174], col2[175], col2[176], col2[177], col2[178], col2[179]],
    col1[9]: [col2[180], col2[181], col2[182], col2[183], col2[184], col2[185], col2[186], col2[187], col2[188], col2[189], col2[190], col2[191], col2[192], col2[193], col2[194], col2[195], col2[196], col2[197], col2[198], col2[199]]}
  df = pd.DataFrame(data=d)
  print(df)
  st.write(df)
  return df
  
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_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 = [[7829], [40940]]
  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 + 5, 
    min_length = res + 5, 
    top_k=50,
    temperature=1.0,
    num_return_sequences=3,
    bad_words_ids=bad_word_ids
  )
  for i in range(3):
    e = tokenizer.decode(sample_outputs[i])
    e = e.replace(text, "")
    yo.append(e)
  print(yo)
  return yo

with st.form(key='my_form'):
    prompt = st.text_area(label='Enter sentence', value=g, height=500)
    submit_button = st.form_submit_button(label='Submit')
    submit_button2 = st.form_submit_button(label='Fast Forward')
    submit_button3 = st.form_submit_button(label='Fast Forward 2.0')
    submit_button4 = st.form_submit_button(label='Get Top')

    if submit_button:
      with torch.no_grad():
        text = tokenizer.encode(prompt)
        myinput, past_key_values = torch.tensor([text]), None
        myinput = myinput
        myinput= myinput.to(device)
        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(log_nums)
        best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
        text.append(best_indices[0].item())
        best_probabilities = probabilities[best_indices].tolist()
        words = []              
        st.write(best_words)
    if submit_button2:
        print("----")
        st.write("___")
        m = LogProbs(prompt)
        st.write("___")
        st.write(m)
        st.write("___")
    if submit_button3:
        print("----")
        st.write("___")
        st.write(BestProbs)
    if submit_button4:
      BestProbs5(prompt)