Update app.py

app.py

@@ -1,7 +1,32 @@
 import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM 
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+tokenizer = AutoTokenizer.from_pretrained(
+  'kakaobrain/kogpt', revision='KoGPT6B-ryan1.5b',
+  bos_token='[BOS]', eos_token='[EOS]', unk_token='[UNK]', pad_token='[PAD]', mask_token='[MASK]'
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+  'kakaobrain/kogpt', revision='KoGPT6B-ryan1.5b',
+  pad_token_id=tokenizer.eos_token_id,
+  torch_dtype=torch.float16, low_cpu_mem_usage=True
+).to(device='cpu', non_blocking=True)
+_ = model.eval()
+
+title = "KoGPT"
+description = "Gradio demo for KoGPT(Korean Generative Pre-trained Transformer). To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://github.com/kakaobrain/kogpt' target='_blank'>KoGPT: KakaoBrain Korean(hangul) Generative Pre-trained Transformer</a> | <a href='https://huggingface.co/kakaobrain/kogpt' target='_blank'>Huggingface Model</a></p>"
+examples=[['인간처럼 생각하고, 행동하는 \'지능\'을 통해 인류가 이제까지 풀지 못했던']]
+def greet(text):
+    prompt = text
+    with torch.no_grad():
+      tokens = tokenizer.encode(prompt, return_tensors='pt').to(device='cpu', non_blocking=True)
+      gen_tokens = model.generate(tokens, do_sample=True, temperature=0.8, max_length=64)
+      generated = tokenizer.batch_decode(gen_tokens)[0]
+      
+    return generated
+    
+iface = gr.Interface(fn=greet, inputs="text", outputs="text", title=title, description=description, article=article, examples=examples,enable_queue=True)
 iface.launch()