Final app for version 2 with Flagging added

app.py

@@ -7,16 +7,16 @@ model_name = 'anugrahap/gpt2-indo-textgen'
 HF_TOKEN = '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'
 hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "output-gpt2-indo-textgen")
 
-# # define the tokenization method
-# tokenizer = AutoTokenizer.from_pretrained(model_name,
-#                                           model_max_length=1e30,
-#                                           padding_side='right',
-#                                           return_tensors='pt')
+# define the tokenization method
+tokenizer = AutoTokenizer.from_pretrained(model_name,
+                                          model_max_length=1e30,
+                                          padding_side='right',
+                                          return_tensors='pt')
 
-# # add the EOS token as PAD token to avoid warnings
-# model = AutoModelForCausalLM.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id)
+# add the EOS token as PAD token to avoid warnings
+model = AutoModelForCausalLM.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id)
 
-generator = pipeline('text-generation', model=model_name)
+generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
 
 # create the decoder parameter to generate the text
 def single_generation(text,min_length,max_length,temperature,top_k,top_p,num_beams,repetition_penalty,do_sample):