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from transformers import AutoTokenizer, AutoModelForCausalLM
from unidecode import unidecode
from collections import Counter
import torch
import os
import gradio as gr
import numpy as np
import re
import string
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("osiria/primo")
model = AutoModelForCausalLM.from_pretrained("osiria/primo")
model = PeftModel.from_pretrained(model, "osiria/primo")
class Prime:
def __init__(self, tokenizer, model):
self.tokenizer = tokenizer
self.model = model
def _check_sublist(self, lst, sub_lst, sep = " "):
l_type = type(lst[0])
lst = sep.join(list(map(str, lst)))
sub_lst = sep.join(list(map(str, sub_lst)))
return sub_lst in lst
def _exclude_sublist(self, lst, sub_lst, sep = " "):
l_type = type(lst[0])
lst = sep.join(list(map(str, lst)))
sub_lst = sep.join(list(map(str, sub_lst)))
lst = re.sub("\s+", " ", lst.replace(sub_lst, "")).strip().split(sep)
lst = list(map(l_type, lst))
return lst
def generate(self, prompt, message = "", sep = " [AI]", max_tokens = 100, excluded = [[40, 19]],
lookback = 5, resample_tokens = [27793], replace_tokens = {11302: 23318},
stop_tokens = [239],
sample = False,
top_k = 5):
if message:
prompt = message + ". " + prompt
prompt = prompt.replace("“", '"').replace("”", '"').replace("’", "'")
if not sample:
top_k = 2
tokens = tokenizer.encode("[HUMAN] " + prompt + sep)
tokens_generated = []
checkpoint = 0
while tokens[-1] not in stop_tokens and len(tokens_generated) < max_tokens:
output = model.forward(input_ids=torch.tensor([tokens]).to(device)).logits[0,-1]
output = torch.softmax(output, dim = 0)
candidates = torch.topk(output, k = top_k)
if sample:
indices = candidates.indices
scores = candidates.values
next_token = indices[torch.multinomial(scores, 1)[0].item()]
else:
next_token = candidates.indices[0]
next_token = next_token.item()
sub_tokens = tokens_generated[-lookback:] + [next_token]
if next_token in resample_tokens:
next_token = candidates.indices[1]
next_token = next_token.item()
if len(tokens_generated) >= (lookback + 1) and next_token in tokens_generated[-2:]:
next_token = candidates.indices[1]
next_token = next_token.item()
elif len(tokens_generated) >= lookback and self._check_sublist(tokens_generated, sub_tokens):
if checkpoint:
tokens = tokens[:checkpoint]
break
else:
next_token = candidates.indices[1]
next_token = next_token.item()
sample = True
if next_token in replace_tokens:
next_token = replace_tokens[next_token]
tokens = tokens + [next_token]
tokens_generated = tokens_generated + [next_token]
if next_token == 5:
checkpoint = len(tokens)
for ex_lst in excluded:
tokens = self._exclude_sublist(tokens, ex_lst)
output = tokenizer.decode(tokens, skip_special_tokens=True)
output = output.split(sep)[-1].strip()
output = output[0].upper() + output[1:]
if output[-1] == tokenizer.decode(stop_tokens[0]):
output = output[:-1]
if len(re.findall("\d\.", output)) > 1:
output = re.sub("\d\.", "<br>•", output)
output = re.sub("^\<br\>", "", output)
return output
model.eval()
device = torch.device("cuda")
prime = Prime(tokenizer = tokenizer, model = model)
def process_input(user_input, max_tokens, sample, top_k, message):
return prime.generate(prompt = user_input, message = message,
max_tokens = max_tokens, sample = sample,
top_k = top_k)
header = '''--------------------------------------------------------------------------------------------------
<style>
.vertical-text {
writing-mode: vertical-lr;
text-orientation: upright;
background-color:red;
}
</style>
<center>
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> </span>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> </span>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> </span>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
</body>
</center>
<br>
<center><img src="file/primo.png" width="100"></center>
'''
import gradio as gr
import random
import time
with gr.Blocks(title="primo", css="footer {visibility: hidden}", theme=gr.themes.Default(text_size="md", spacing_size="md")) as interface:
gr.Markdown(header)
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("<b>opzioni</b>")
max_tokens = gr.Slider(1, 250, value=150, label="massimo numero di token", info="scegli un limite tra 1 e 250")
sample = gr.Checkbox(label="campionamento")
top_k = gr.Slider(1, 5, step=1, value=1, label="creatività", info="scegli un livello tra 1 e 5")
message = gr.Textbox(label="messaggio di sistema", value = "")
clear = gr.Button("pulisci conversazione")
with gr.Column(scale=8):
chatbot = gr.Chatbot(label = "prime").style(height=600)
msg = gr.Textbox(label = "richiesta")
def user(user_message, history):
return gr.update(value="", interactive=False), history + [[user_message, None]]
def bot(history, message, max_tokens, sample, top_k):
bot_message = process_input(history[-1][0], message = message, max_tokens = max_tokens,
sample = sample, top_k = top_k)
history[-1][1] = ""
for character in bot_message:
history[-1][1] += character
time.sleep(0.05)
yield history
response = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, [chatbot, message, max_tokens, sample, top_k], chatbot
)
response.then(lambda: gr.update(interactive=True), None, [msg], queue=False)
clear.click(lambda: None, None, chatbot, queue=False)
with gr.Column(scale=1):
gr.Markdown("<b>attenzione</b>")
gr.Markdown("il modello potrebbe comportarsi in maniera imprevista nel caso in cui riceva prompt troppo lontani dal suo pre-training o fine-tuning e, per via della natura probabilistica del meccanismo di generazione, potrebbe occasionalmente produrre contenuti distorti o offensivi in relazione a tematiche come il genere, le etnie, le ideologie, e le convinzioni politiche o religiose<br><br>per via di queste limitazioni, il modello e i suoi output dovrebbero essere usati con cautela, e non dovrebbero essere coinvolti in contesti che richiedono che il testo generato sia corretto o veritiero")
interface.queue()
interface.launch() |