Update app.py

app.py

@@ -1,27 +1,23 @@
 import gradio as gr
 
 # Load model directly
-# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# tokenizer = AutoTokenizer.from_pretrained("yeye776/t5-OndeviceAI-HomeIoT")
-# model = AutoModelForSeq2SeqLM.from_pretrained("yeye776/t5-OndeviceAI-HomeIoT")
+tokenizer = AutoTokenizer.from_pretrained("yeye776/t5-OndeviceAI-HomeIoT")
+model = AutoModelForSeq2SeqLM.from_pretrained("yeye776/t5-OndeviceAI-HomeIoT")
 
 # Gradio 인터페이스 구성
 def generate_answer(input_text):
     # 입력 텍스트를 모델 토크나이저로 토큰화
-    # input_ids = tokenizer(input_text, max_length=700, return_tensors="pt").input_ids
-    # input_ids = tokenizer(input_text, max_length=700, return_tensors="pt").input_ids
+    input_ids = tokenizer(input_text, max_length=700, return_tensors="pt").input_ids
     
     # 모델 추론
-    # output_ids = model.generate(input_ids, top_k=10, max_length=1024)
+    output_ids = model.generate(input_ids, top_k=10, max_length=1024)
     # output_ids = model.generate(input_ids, num_beams=10, top_k=10, max_length=1024)
     
     # 모델 출력을 텍스트로 디코딩
-    # output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    output_text = "ActionID:1060, Value:없음, Home:안방, NickName:조명1"
-    output_text['장소명'] = output_text['Home']
-    output_text['장치명'] = output_text['NickName']
-    print(output_text)
+    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
     return output_text
 
 # Gradio 인터페이스 정의