Hugging Face's logo

Spaces:
zArabi
/
Persian-Sentiment-Analysis
Runtime error

App Files Community
Persian-Sentiment-Analysis
File size: 4,659 Bytes 
c5ae5ff
1635166
 
9b69fbc
 
 
 
1635166
 
 
9b69fbc
 
 
1635166
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9b69fbc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2d6aba9
 
 
 
 
 
9b69fbc
c5ae5ff
1635166
 
c5ae5ff
9b69fbc
 
 
1635166
9b69fbc
 
 
 
 
 
 
 
 
 
 
 
 
 
1635166
9b69fbc
 
 
 
8abdd11
c5ae5ff
 
 
9b69fbc
 
01b37c2
c5ae5ff
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
 
import gradio as gr
from transformers import BertModel, BertConfig, BertTokenizer
import torch
import torch.nn as nn
import torch.nn.functional as F
import huggingface_hub
from huggingface_hub import hf_hub_download
import hazm
from cleantext import clean
import regex as re

huggingface_hub.Repository = 'zArabi/Persian-Sentiment-Analysis'

def cleanhtml(raw_html):
    cleanr = re.compile('<.*?>')
    cleantext = re.sub(cleanr, '', raw_html)
    return cleantext

def cleaning(text):
    text = text.strip()
    
    # regular cleaning
    # https://pypi.org/project/clean-text/ >> works well for eng and de languages
    text = clean(text,
        fix_unicode=True,
        to_ascii=False,
        lower=True,
        no_line_breaks=True,
        no_urls=True,
        no_emails=True,
        no_phone_numbers=True,
        no_numbers=False,
        no_digits=False,
        no_currency_symbols=True,
        no_punct=False, #Keep the punc
        replace_with_url="",
        replace_with_email="",
        replace_with_phone_number="",
        replace_with_number="",
        replace_with_digit="0",
        replace_with_currency_symbol="",
    )

    # cleaning htmls
    text = cleanhtml(text)
    
    # normalizing > https://github.com/sobhe/hazm
    normalizer = hazm.Normalizer()
    text = normalizer.normalize(text)
    
    # removing wierd patterns
    wierd_pattern = re.compile("["
        u"\U0001F600-\U0001F64F"  # emoticons
        u"\U0001F300-\U0001F5FF"  # symbols & pictographs
        u"\U0001F680-\U0001F6FF"  # transport & map symbols
        u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
        u"\U00002702-\U000027B0"
        u"\U000024C2-\U0001F251"
        u"\U0001f926-\U0001f937"
        u'\U00010000-\U0010ffff'
        u"\u200d"
        u"\u2640-\u2642"
        u"\u2600-\u2B55"
        u"\u23cf"
        u"\u23e9"
        u"\u231a"
        u"\u3030"
        u"\ufe0f"
        u"\u2069"
        u"\u2066"
        # u"\u200c"
        u"\u2068"
        u"\u2067"
        "]+", flags=re.UNICODE)
    
    text = wierd_pattern.sub(r'', text)
    
    # removing extra spaces, hashtags
    text = re.sub("#", "", text)
    text = re.sub("\s+", " ", text)
    
    return text

class SentimentModel(nn.Module):
    def __init__(self, config):
        super(SentimentModel, self).__init__()
        self.bert = BertModel.from_pretrained(modelName, return_dict=False)
        self.dropout = nn.Dropout(0.3)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
    
    def forward(self, input_ids, attention_mask):
        _, pooled_output = self.bert(
            input_ids=input_ids, 
            attention_mask=attention_mask)
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        return logits

modelName = 'HooshvareLab/bert-fa-base-uncased'
class_names = ['negative', 'neutral', 'positive']
label2id = {label: i for i, label in enumerate(class_names)}
id2label = {v: k for k, v in label2id.items()}

config = BertConfig.from_pretrained(
    modelName,
    num_labels=len(class_names),
    id2label=id2label,
    label2id=label2id)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

path = 'HooshvareLab-bert-fa-base-uncased-3class.bin'
downloadedModelDict = hf_hub_download(repo_id="zArabi/Persian-Sentiment-Analysis", filename=path)
loaded_model = SentimentModel(config=config).to(device)
loaded_model.load_state_dict(torch.load(downloadedModelDict, map_location="cpu"))


tokenizer = BertTokenizer.from_pretrained(modelName)
max_len=512

def predict(text):
  text = cleaning(text)
  encoding = tokenizer.encode_plus(
    text,
    max_length=max_len,
    truncation=True,
    padding="max_length",
    add_special_tokens=True, # Add '[CLS]' and '[SEP]'
    return_token_type_ids=True,
    return_attention_mask=True,
    return_tensors='pt',  # Return PyTorch tensors
  )
  input_ids = encoding["input_ids"].to(device)
  attention_mask = encoding["attention_mask"].to(device)
  outputs = loaded_model (input_ids, attention_mask)
  probs = F.softmax(outputs,dim=1)
  values, indices = torch.max(probs, dim=1)
  data = {
    'comments': text,
    'preds': indices.cpu().numpy()[0],
    'label': class_names[indices.cpu().numpy()[0]],
    'probablities': {class_names[i] : round(probs[0][i].item(),3) for i in range(len(probs[0]))}
  }
  return {class_names[i] : round(probs[0][i].item(),3) for i in range(len(probs[0]))}

gr.Interface(
    predict,
    inputs=gr.Textbox(label="Explore your sentence!",lines=2, placeholder="Type Here..."),
    outputs=gr.outputs.Label(num_top_classes=3),
    title="How are feeling?!!",
).launch()