Upload app.py

app.py

@@ -16,7 +16,6 @@ try:
 except:
     print("english model load error")
 
-'''
 try:
     tokenizer_multilingual = AutoTokenizer.from_pretrained("amir22010/amir22010/PyABSA_Hospital_Multilingual_allenai_tk-instruct-base-def-pos_FinedTuned_Model")
     double_multilingual_generator = AutoModelForSeq2SeqLM.from_pretrained("amir22010/PyABSA_Hospital_Multilingual_allenai_tk-instruct-base-def-pos_FinedTuned_Model")
@@ -28,19 +27,17 @@ try:
     double_keybert_generator = AutoModelForSeq2SeqLM.from_pretrained("amir22010/KeyBert_ABSA_Hospital_Multilingual_allenai_tk-instruct-base-def-pos_FinedTuned_Model")
 except:
     print("keybert model load error")
-'''
 
 
 def perform_asde_inference(text, dataset, model_id):
     if not text:
         if model_id == "PyABSA_Hospital_English_allenai/tk-instruct-base-def-pos_FinedTuned_Model":
             df = pd.read_csv('pyabsa_english.csv')#validation dataset
-        '''
         elif model_id == "PyABSA_Hospital_Multilingual_allenai/tk-instruct-base-def-pos_FinedTuned_Model":
             df = pd.read_csv('pyabsa_multilingual.csv')#validation dataset
         elif model_id == "KeyBert_ABSA_Hospital_allenai/tk-instruct-base-def-pos_FinedTuned_Model":
             df = pd.read_csv('keybert_valid.csv')#validation dataset
-        '''
+
         random_i = np.random.randint(low=0, high=df.shape[0], size=(1,)).flat[0]
         selected_df = df.iloc[random_i]
         text = selected_df['clean_text']
@@ -67,7 +64,6 @@ def perform_asde_inference(text, dataset, model_id):
         output = double_english_generator.generate(tokenized_text.input_ids,max_length=512)
         model_generated = tokenizer_english.decode(output[0], skip_special_tokens=True)
 
-    '''
     elif model_id == "PyABSA_Hospital_Multilingual_allenai/tk-instruct-base-def-pos_FinedTuned_Model":
         tokenized_text = tokenizer_multilingual(bos_instruction + text + delim_instruct + eos_instruct, return_tensors="pt")
         output = double_multilingual_generator.generate(tokenized_text.input_ids,max_length=512)
@@ -77,7 +73,6 @@ def perform_asde_inference(text, dataset, model_id):
         tokenized_text = tokenizer_keybert(bos_instruction + text + delim_instruct + eos_instruct, return_tensors="pt")
         output = double_keybert_generator.generate(tokenized_text.input_ids,max_length=512)
         model_generated = tokenizer_keybert.decode(output[0], skip_special_tokens=True)
-    '''
     
     pred_asp = [i.split(':')[0] for i in model_generated.split(',')]
     pred_sent = [i.split(':')[1] for i in model_generated.split(',')]
@@ -124,8 +119,8 @@ if __name__ == "__main__":
                         asde_model_ids = gr.Radio(
                             choices=[
                                 "PyABSA_Hospital_English_allenai/tk-instruct-base-def-pos_FinedTuned_Model", 
-                                #"PyABSA_Hospital_Multilingual_allenai/tk-instruct-base-def-pos_FinedTuned_Model", 
-                                #"KeyBert_ABSA_Hospital_allenai/tk-instruct-base-def-pos_FinedTuned_Model"
+                                "PyABSA_Hospital_Multilingual_allenai/tk-instruct-base-def-pos_FinedTuned_Model", 
+                                "KeyBert_ABSA_Hospital_allenai/tk-instruct-base-def-pos_FinedTuned_Model"
                             ],
                             value="PyABSA_Hospital_English_allenai/tk-instruct-base-def-pos_FinedTuned_Model",
                             label="Fine-tuned Models on Hospital Review custom data",