Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, StoppingCriteriaList
+import os
+import torch
+
+theme = "darkgrass"
+title = "Polyglot(Korean) Demo"
+model_name = "EleutherAI/polyglot-ko-1.3b"
+
+bad_words = [
+  '...',
+  '....',
+  '(중략)',
+  'http'
+]
+
+description = "polyglot (1.3B 파라미터 사이즈) 한국어 모델을 시연하는 데모페이지 입니다."
+article = "<p style='text-align: center'><a href='https://github.com/EleutherAI/polyglot' target='_blank'>Polyglot: Large Language Models of Well-balanced Competence in Multi-languages</a></p>"
+examples = [
+    ["CPU와 GPU의 차이는,"],
+    ["질문: 우크라이나 전쟁이 세계3차대전으로 확전이 될까요? \n답변:"],
+    ["2040년 미국은, "]
+]
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+  model_name
+)
+model.eval()
+
+pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, eos_token_id=tokenizer.eos_token_id)
+
+def predict(text):
+  with torch.no_grad():
+    tokens = tokenizer(text, return_tensors="pt").input_ids
+    # generate and end generate if <|endoftext|> is not in text
+    gen_tokens = model.generate(
+      tokens, do_sample=True, temperature=0.8, max_new_tokens=64, top_k=50, top_p=0.8,
+      no_repeat_ngram_size=3, repetition_penalty=1.2,
+      bad_words_ids=[
+        tokenizer.encode(bad_word) for bad_word in bad_words
+      ],
+      eos_token_id=tokenizer.eos_token_id,
+      pad_token_id=tokenizer.pad_token_id
+    )
+    generated = tokenizer.batch_decode(gen_tokens)[0]
+    return generated
+  # return pipe(text)[0]['generated_text']
+
+iface = gr.Interface(
+  fn=predict, 
+  inputs='text',
+  outputs='text',
+  examples=examples
+)
+
+iface.launch()