basic demo finished

app/app.py

@@ -53,13 +53,12 @@ def model_predict(text, model):
     # Placeholder for model prediction
     raw_preds = []
     for sentence in text:
-        print(sentence)
-        input_ids = torch.tensor(sentence['input_ids']).to(device)
-        attention_mask = torch.ones(sentence['attention_mask']).to(device)
-        token_type_ids = torch.zeros(sentence['token_type_ids']).to(device)
+        input_ids = torch.tensor(sentence['input_ids']).unsqueeze(0).to(device)
+        attention_mask = torch.tensor(sentence['attention_mask']).unsqueeze(0).to(device)
+        token_type_ids = torch.tensor(sentence['token_type_ids']).unsqueeze(0).to(device)
         with torch.no_grad():
             preds = model(attention_mask = attention_mask, token_type_ids = token_type_ids, input_ids = input_ids)
-        raw_preds.append(preds)
+        raw_preds.append(preds.detach().cpu().numpy().tolist())
     return raw_preds
 
 def tokenize_text(text,tokenizer):
@@ -78,13 +77,13 @@ def upload():
         #read the file and save it to the uploads folder 
         data = request.get_json(force=True)
         text = data['text']
-        print(text)
+        # print(text)
         tokenized_text = tokenize_text(text,tokenizer)
         #make a prediction
-        print(tokenized_text[0]['input_ids'])
+        # print(tokenized_text[0]['input_ids'])
         preds = model_predict(tokenized_text, model)
         #process the prediction to determine the output
-        print(preds)
+        # print(preds)
         return jsonify(prediction=preds)
     return None