app.py

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, 5, 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)