Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import transformers
+import torch
+from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup
+
+class_names = ['left', 'neutral', 'right']
+PRE_TRAINED_MODEL_NAME = 'bert-base-uncased'
+tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)
+MAX_LEN = 256
+bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)
+
+class SentimentClassifier(nn.Module):
+  def __init__(self, n_classes):
+    super(SentimentClassifier, self).__init__()
+    self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)
+    self.drop = nn.Dropout(p=0.4)
+    self.out = nn.Linear(self.bert.config.hidden_size, n_classes)
+  def forward(self, input_ids, attention_mask):
+    _, pooled_output = self.bert(
+      input_ids=input_ids,
+      attention_mask=attention_mask,
+      return_dict=False
+      
+    )
+    output = self.drop(pooled_output)
+    return self.out(output)
+
+model = SentimentClassifier(len(class_names))
+
+def result_final(new_article):
+
+  encoded_review = tokenizer.encode_plus(
+  review_text,
+  max_length=MAX_LEN,
+  add_special_tokens=True,
+  return_token_type_ids=False,
+  padding="max_length",
+  truncation=True,
+  return_attention_mask=True,
+  return_tensors='pt',
+)
+  
+  input_ids = encoded_review['input_ids'].to(device)
+  attention_mask = encoded_review['attention_mask'].to(device)
+  output = model2(input_ids, attention_mask)
+  _, prediction = torch.max(output, dim=1)
+
+  return class_names[prediction]
+
+
+iface = gr.Interface(fn = result_final, inputs = "text", outputs = ["text"], title = "News Bias Classifer")
+iface.launch()
+