Update app.py

app.py

@@ -13,7 +13,7 @@ description2 = "<h3>Demo EmotioNL</h3>\nThis demo allows you to analyse the emot
 
 inference_modelpath = "model/checkpoint-128"
 
-"""
+
 output_dir = "model"
 model_config = {
     "model_weights": "pdelobelle/robbert-v2-dutch-base",
@@ -57,7 +57,6 @@ trainer = Trainer(
 
 
 def inference_dataset(file_object):
-  #input_file = open(file_object.name, 'r')
   input_file = file_object
   data_paths = {"train": input_file, "inference": input_file}
   dataset = load_dataset('csv', skiprows=1, data_files=data_paths, column_names = ['id', 'text', 'label'], delimiter='\t')
@@ -82,7 +81,7 @@ def inference_dataset(file_object):
   return output
 """
 
-def inference_dataset(file_object, option_list, progress=gr.Progress()):
+def inference_dataset(file_object, option_list):
     tokenizer = AutoTokenizer.from_pretrained(inference_modelpath)
     model = AutoModelForSequenceClassification.from_pretrained(inference_modelpath)
     data_path = open(file_object.name, 'r')
@@ -90,7 +89,6 @@ def inference_dataset(file_object, option_list, progress=gr.Progress()):
     ids = df["id"].tolist()
     texts = df["text"].tolist()
     preds = []
-    progress(0, desc="Starting...")
     for text in tqdm(texts): # progressbar
         inputs = tokenizer(text, return_tensors="pt")
         with torch.no_grad(): # run model
@@ -117,7 +115,7 @@ def inference_dataset(file_object, option_list, progress=gr.Progress()):
     if "topics" in option_list:
         output5 = "This option was selected."
     return [output1, output2, output3, output4, output5]
-
+"""
 def what_happened(text, file_object, option_list):
     if file_object:
         output = "You uploaded a file."
@@ -153,8 +151,7 @@ def what_happened2(file_object, option_list):
         output5 = "This option was selected."
     return [output1, output2, output3, output4, output5]
 
-def inference_sentence(text, progress=gr.Progress()):
-    progress(0, desc="Starting...")
+def inference_sentence(text):
     tokenizer = AutoTokenizer.from_pretrained(inference_modelpath)
     model = AutoModelForSequenceClassification.from_pretrained(inference_modelpath)
     for text in tqdm([text]):