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, AutoModelWithLMHead, AutoModelForCausalLM

st.title('GPT2:')
#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
number_of_outputs = st.sidebar.slider("Number of Outputs", 50, 350)

@st.cache(allow_output_mutation=True)
def get_model():
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal")
    #model = #AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln6")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln5")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln4")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln2")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln24")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln25")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln26")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln27")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln28")
    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln29")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln30")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln31")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln32")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln33")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln34")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln35")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/Points4")
    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPT2Neo1.3BPoints")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints3")
    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln91Paraphrase")
    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln91Paraphrase")
    tokenizer = AutoTokenizer.from_pretrained("BigSalmon/TruncatedLLamaGPT2Large")
    model = AutoModelForCausalLM.from_pretrained("BigSalmon/TruncatedLLamaGPT2Large")
    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: """
with st.form(key='my_form'):
    prompt = st.text_area(label='Enter sentence', value=g)
    submit_button = st.form_submit_button(label='Submit')
    if submit_button:
      with torch.no_grad():
        text = tokenizer.encode(prompt)
        myinput, past_key_values = torch.tensor([text]), None
        myinput = myinput
        #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]
        text.append(best_indices[0].item())
        best_probabilities = probabilities[best_indices].tolist()
        words = []              
        st.write(best_words)