|
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/InformalToFormalLincoln64Paraphrase') |
|
|
|
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/InformalToFormalLincoln99Paraphrase") |
|
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln99Paraphrase") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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}") |
|
|
|
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]: |
|
|
|
col2.append(i) |
|
|
|
|
|
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("\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) |
