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


st.title('GPT2: To see all prompt outlines: https://huggingface.co/BigSalmon/InformalToFormalLincoln45')

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/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")
    
    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: """


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)

with st.form(key='my_form'):
    prompt = st.text_area(label='Enter sentence', value=g)
    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')

    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(250)
        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 = BestProbs(prompt)
        st.write("___")
        st.write(m)
        st.write("___")
    if submit_button3:
        print("----")
        st.write("___")
        st.write(BestProbs)