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import pandas as pd
import re
import gradio as gr
import torch
from transformers import BertTokenizerFast, BertForSequenceClassification
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
dataset_path = './codice_civile_ITA_LIBRI_2_withArtRef_v2.csv'
input_model_path = './MODELLO_LOCALE_LIBRI_2_v5_2_subset60UniRRemphT4'
def load_CC_from_CSV(path):
NUM_ART = 0
cc = pd.read_csv(path, header=None, sep='|', usecols=[1,2,3], names=['art','title','text'], engine='python')
article_id={}
id_article={}
article_text={}
for i in range(len(cc)):
NUM_ART +=1
art = re.sub('(\s|\.|\-)*', '', str(cc['art'][i]).lower())
article_id[art] = i
id_article[i] = art
article_text[art] = str(cc['title'][i]).lower() + " -> " + str(cc['text'][i]).lower()
if i == 59:
break
return article_id, id_article, article_text, NUM_ART
article_id, id_article, article_text, NUM_ART = load_CC_from_CSV(dataset_path)
model = BertForSequenceClassification.from_pretrained(input_model_path)
tokenizer = BertTokenizerFast.from_pretrained(input_model_path)
def LamBERTa_v5_placeholder(query):
n = 345
predictions = torch.softmax(torch.randn(n), dim=0)
values, indices = torch.topk(predictions, 5)
confidences = {id_article[i.item()] : v.item() for i, v in zip(indices, values)}
# confidences = {id_article[i] : float(predictions[i]) for i in range(n)}
return confidences
def LamBERTa(query):
texts = []
input_ids = torch.tensor(tokenizer.encode(query, add_special_tokens=True)).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
log_probs = torch.softmax(logits, dim=1)
values, indices = torch.topk(log_probs, 3, dim=1)
confidences = {id_article[i.item()] : v.item() for i, v in zip(indices[0], values[0])}
for art, prob in confidences.items():
texts.append(
{
"art": art,
"text": article_text[art],
}
)
return confidences, texts
demo = gr.Interface(fn=LamBERTa, inputs="text", outputs=["label", "json"], examples=["Quando si apre la successione","Dove si apre la successione","In quali casi, alla morte, non spetta l'eredità"], live=True)
demo.launch()
demo.launch(share=True) |