Update app.py

app.py

@@ -1,11 +1,39 @@
 import gradio as gr
-from transformers import pipeline
 
-# t5_model_name = "paust/pko-t5-large"
-# output_model_name = "output/brokarry-t5-large"
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-pipe = pipeline("text-generation", model="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, return_tensors="pt").input_ids
+    input_ids = tokenizer(inputs, max_length=700, return_tensors="pt").input_ids
+    
+    # 모델 추론
+    # output_ids = model.generate(input_ids)
+    outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=1024)
+    
+    # 모델 출력을 텍스트로 디코딩
+    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    
+    return output_text
+
+# Gradio 인터페이스 정의
+iface = gr.Interface(
+    fn=generate_answer,
+    inputs=gr.Textbox(),
+    outputs=gr.Textbox(),
+    live=True,
+    title="Hugging Face Transformers + Gradio Demo",
+    description="Enter a text and get the model's response."
+)
+
+# Gradio 앱 시작
+iface.launch()
 
 # gr.load("models/yeye776/t5-OndeviceAI-HomeIoT").launch()
-iface = gr.Interface(fn=pipe, inputs="text", outputs="text")
-iface.launch()
+# iface = gr.Interface(fn=pipe, inputs="text", outputs="text")
+# iface.launch()