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import gradio as gr
from transformers import BertModel, BertConfig
import torch.nn as nn
import torch.nn.functional as F
import huggingface_hub
from huggingface_hub import hf_hub_download
huggingface_hub.Repository = 'zArabi/Persian-Sentiment-Analysis'
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)
downloadedModelFile = hf_hub_download(repo_id="zArabi/Persian-Sentiment-Analysis", filename="persianModel")
loaded_model = torch.load(downloadedModelFile)
max_len=512
pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
def predict(text):
text = cleaning(text)
encoding = tokenizer.encode_plus(
sample_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': sample_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 data
gr.Interface(
predict,
inputs=gr.Textbox(label="Explore your sentence!",lines=2, placeholder="Type Here..."),
outputs=gr.outputs.Label(num_top_classes=3),
title="What are feeling?!",
).launch() |