add token

app.py

@@ -1,3 +1,4 @@
+import os
 import gradio as gr
 
 import numpy as np
@@ -10,7 +11,7 @@ from models import StanceEncoderModel
 CLASS_DICT = {0: 'FAVOR', 1: 'AGAINST', 2: 'NEITHER'}
 
 params = {'lang': 'pl',
-          'masked_lm_prompt': 4,}
+          'masked_lm_prompt': 4, }
 
 
 class StancePipeline(Pipeline):
@@ -19,7 +20,8 @@ class StancePipeline(Pipeline):
         return pipeline_parameters, {}, {}
 
     def preprocess(self, input):
-        prompt_text, prompt_target = prepare_stance_texts([input['text'],], [input['target'],], params, self.tokenizer)
+        prompt_text, prompt_target = prepare_stance_texts([input['text'], ], [input['target'], ], params,
+                                                          self.tokenizer)
         inputs = self.tokenizer(prompt_text, prompt_target, return_tensors="pt", padding=True, truncation='only_first')
         return {'input_ids': inputs['input_ids'], 'attention_mask': inputs['attention_mask'],
                 'sequence_ids': torch.tensor((np.array(inputs.sequence_ids()) == 1).astype(int)).unsqueeze(0)}
@@ -34,9 +36,11 @@ class StancePipeline(Pipeline):
         return {'stance': CLASS_DICT[probas.argmax(-1).item()], 'score': score}
 
 
-pipeline = StancePipeline(model=StanceEncoderModel.from_pretrained('clarin-knext/stance-pl-1'),
-                          tokenizer=AutoTokenizer.from_pretrained('clarin-knext/stance-pl-1'),
-                          batch_size=1)
+model = StanceEncoderModel.from_pretrained('clarin-knext/stance-pl-1',
+                                           use_auth_token=os.environ['TOKEN'])
+tokenizer = AutoTokenizer.from_pretrained('clarin-knext/stance-pl-1',
+                                          use_auth_token=os.environ['TOKEN'])
+pipeline = StancePipeline(model=model, tokenizer=tokenizer, batch_size=1)
 
 
 def predict(text, target):
@@ -53,4 +57,4 @@ gradio_app = gr.Interface(
 )
 
 if __name__ == "__main__":
-    gradio_app.launch()
+    gradio_app.launch()