Update app.py

app.py

@@ -34,41 +34,36 @@ def principal(tweets):
     tweet_list = [i.text for i in tweets]
     text= pd.DataFrame(tweet_list)
     text1=text[0].values
-    indices1=tokenizer.batch_encode_plus(text1.tolist(),
-                                     max_length=128,
-                                     add_special_tokens=True, 
-                                     return_attention_mask=True,
-                                     pad_to_max_length=True,
-                                     truncation=True)
-     input_ids1=indices1["input_ids"]
-     attention_masks1=indices1["attention_mask"]
-     prediction_inputs1= torch.tensor(input_ids1)
-     prediction_masks1 = torch.tensor(attention_masks1)
-     # Set the batch size.  
-     batch_size = 25
-     # Create the DataLoader.
-     prediction_data1 = TensorDataset(prediction_inputs1, prediction_masks1)
-     prediction_sampler1 = SequentialSampler(prediction_data1)
-     prediction_dataloader1 = DataLoader(prediction_data1, sampler=prediction_sampler1, batch_size=batch_size)
-     print('Predicting labels for {:,} test sentences...'.format(len(prediction_inputs1)))
-     # Put model in evaluation mode
-     model.eval()
-     # Tracking variables 
-     predictions = []
-     # Predict 
-     for batch in prediction_dataloader1:
-     batch = tuple(t.to(device) for t in batch)
-     # Unpack the inputs from our dataloader
-     b_input_ids1, b_input_mask1 = batch
-     # Telling the model not to compute or store gradients, saving memory and   # speeding up prediction
-     with torch.no_grad():
-         # Forward pass, calculate logit predictions
-         outputs1 = model(b_input_ids1, token_type_ids=None,attention_mask=b_input_mask1)
-         logits1 = outputs1[0]
-         # Move logits and labels to CPU
-         logits1 = logits1.detach().cpu().numpy()
-         # Store predictions and true labels
-         predictions.append(logits1)
+    indices1=tokenizer.batch_encode_plus(text1.tolist(), max_length=128,add_special_tokens=True,return_attention_mask=True,pad_to_max_length=True,truncation=True)
+    input_ids1=indices1["input_ids"]
+    attention_masks1=indices1["attention_mask"]
+    prediction_inputs1= torch.tensor(input_ids1)
+    prediction_masks1 = torch.tensor(attention_masks1)
+    # Set the batch size.  
+    batch_size = 25
+    # Create the DataLoader.
+    prediction_data1 = TensorDataset(prediction_inputs1, prediction_masks1)
+    prediction_sampler1 = SequentialSampler(prediction_data1)
+    prediction_dataloader1 = DataLoader(prediction_data1, sampler=prediction_sampler1, batch_size=batch_size)
+    print('Predicting labels for {:,} test sentences...'.format(len(prediction_inputs1)))
+    # Put model in evaluation mode
+    model.eval()
+    # Tracking variables 
+    predictions = []
+    # Predict 
+    for batch in prediction_dataloader1:
+        batch = tuple(t.to(device) for t in batch)
+        # Unpack the inputs from our dataloader
+        b_input_ids1, b_input_mask1 = batch
+        # Telling the model not to compute or store gradients, saving memory and   # speeding up prediction
+        with torch.no_grad():
+            # Forward pass, calculate logit predictions
+            outputs1 = model(b_input_ids1, token_type_ids=None,attention_mask=b_input_mask1)
+        logits1 = outputs1[0]
+        # Move logits and labels to CPU
+        logits1 = logits1.detach().cpu().numpy()
+        # Store predictions and true labels
+        predictions.append(logits1)
     flat_predictions = [item for sublist in predictions for item in sublist]
     flat_predictions = np.argmax(flat_predictions, axis=1).flatten()#p = [i for i in classifier(tweet_list)]
     df = pd.DataFrame(list(zip(tweet_list, flat_predictions)),columns =['Latest'+str(number_of_tweets)+'Tweets'+' on '+search_words, 'Sexista'])