Upload app.py

app.py

@@ -6,16 +6,14 @@ import hopsworks
 import joblib
 import torch
 from huggingface_hub import hf_hub_download
+import transformers
+from transformers import BertModel, BertTokenizer
 
 class_names = ['Not Depressed', 'Depressed']
 pt_file = hf_hub_download(repo_id="liangc40/sentimental_analysis", filename="model.pt")
 
 model = DepressionClassifier(len(class_names), 'bert-base-cased')
 model.load_state_dict(torch.load(pt_file, map_location=torch.device('cpu')))
-
-
-
-
 model.eval()
 #pipe = pipeline(model="liangc40/sentimental_analysis")
 
@@ -28,9 +26,21 @@ model.eval()
 #model_dir = model.download()
 #model = joblib.load(model_dir + "/sentimental_analysis_model.pkl")
 
+
 def analyse(text):
-    label = model(text)
-    return label
+    #text = "I'm depressed"
+    model = model.to('cpu')
+    tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+    encoding = tokenizer.encode_plus(text, max_length=32, add_special_tokens=True, # Add '[CLS]' and '[SEP]'
+                                     return_token_type_ids=False,
+                                     pad_to_max_length=True,
+                                     return_attention_mask=True,
+                                     return_tensors='pt')
+
+    outputs = model(input_ids = encoding['input_ids'], attention_mask = encoding['attention_mask'])
+    _, preds = torch.max(outputs, dim=1)
+    #print(preds)
+    return preds
 
 with gr.Blocks() as demo:
     gr.Markdown("<h1><center>Sentiment Analysis with Fine-tuned BERT Model")