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import solara
import random
import torch
import torch.nn.functional as F
import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM
# Cargar el tokenizer y el modelo preentrenado en español
tokenizer = AutoTokenizer.from_pretrained("datificate/gpt2-small-spanish")
model = AutoModelForCausalLM.from_pretrained("datificate/gpt2-small-spanish")
# tokenizer = AutoTokenizer.from_pretrained('gpt2')
# model = AutoModelForCausalLM.from_pretrained('gpt2')
text1 = solara.reactive("Hola, ¿cómo estás")
@solara.component
def Page():
with solara.Column(margin=10):
solara.Markdown("#Siguiente token prediction ")
solara.Markdown("Hemos creado esta herramienta para entender el funcionamiento de los modelos de lenguage autoregresivos... ")
def on_action_cell(column, row_index):
text1.value += tokenizer.decode(top_10.indices[0][row_index])
cell_actions = [solara.CellAction(icon="mdi-thumb-up", name="Select", on_click=on_action_cell)]
solara.InputText("Enter text:", value=text1, continuous_update=True)
if text1.value != "":
tokens = tokenizer.encode(text1.value, return_tensors="pt")
spans1 = ""
spans2 = ""
for i, token in enumerate(tokens[0]):
random.seed(i)
random_color = ''.join([random.choice('0123456789ABCDEF') for k in range(6)])
spans1 += " " + f"<span style='font-family: helvetica; color: #{random_color}'>{token}</span>"
spans2 += " " + f"""<span style="
padding: 6px;
border-right: 3px solid white;
line-height: 3em;
font-family: courier;
background-color: #{random_color};
color: white;
position: relative;
"><span style="
position: absolute;
top: 5.5ch;
line-height: 1em;
left: -0.5px;
font-size: 0.45em"> {token}</span>{tokenizer.decode([token])}</span>"""
solara.Markdown(f'{spans2}')
solara.Markdown(f'{spans1}')
outputs = model.generate(tokens, max_new_tokens=1, output_scores=True, return_dict_in_generate=True, pad_token_id=tokenizer.eos_token_id)
scores = F.softmax(outputs.scores[0], dim=-1)
top_10 = torch.topk(scores, 10)
df = pd.DataFrame()
df["probs"] = top_10.values[0]
df["probs"] = [f"{value:.2%}" for value in df["probs"].values]
df["next token ID"] = [top_10.indices[0][i].numpy() for i in range(10)]
df["predicted next token"] = [tokenizer.decode(top_10.indices[0][i]) for i in range(10)]
solara.Markdown("###Prediction")
solara.DataFrame(df, items_per_page=10, cell_actions=cell_actions)
Page()
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