Update app.py

app.py

@@ -1,27 +1,9 @@
 import gradio as gr
-from typing import List
+# Use a pipeline as a high-level helper
+from transformers import pipeline
 
-def prepare_input(question: str):
-    inputs = f"{prompt} {question}"
-    input_ids = tokenizer(inputs, max_length=700, return_tensors="pt").input_ids
-    return input_ids
+pipe = pipeline("text2text-generation", model="yeye776/t5-OndeviceAI-HomeIoT")
 
-def inference(question: str) -> str:
-    input_data = prepare_input(question=question)
-    input_data = input_data.to(model.device)
-    outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=1024)
-
-    result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
-
-    # print("question:", question, "answer:", result)
-    return result
-
-# test_id = 1160
-# # 거실조명1 꺼주세요	"Home:거실,NickName:조명1,ActionID:1061,Value:없음"
-# print("model result:", inference(dataset_finetuning["train"][test_id]["Q"]))
-# print("real result:", dataset_finetuning["train"][test_id]["C"])
-print(inference("거실 조명1 꺼주세요"))
-
-gr.load("models/yeye776/t5-OndeviceAI-HomeIoT").launch()
-# iface = gradio.Interface(fn=pipe_home, inputs="text", outputs="text")
-# iface.launch()
+# gr.load("models/yeye776/t5-OndeviceAI-HomeIoT").launch()
+iface = gradio.Interface(fn=pipe, inputs="text", outputs="text")
+iface.launch()