Update app.py

app.py

@@ -13,9 +13,6 @@ from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampl
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 import gradio as gr
 
-
-
-
 def greet(co):
     code_text = []
     while True:
@@ -192,8 +189,41 @@ def T5Trainer(dataframe, source_text, model_params, step="test",):
 
 #################################################################################
 
-demo = gr.Interface(
+'''demo = gr.Interface(
     fn = greet,
     inputs = "text",
     outputs= "number")
-demo.launch()
+demo.launch(share=True)
+'''
+with gr.Blocks() as demo1:
+    gr.Markdown(
+    """
+    <h1 align="center">
+    False-Alarm-Detector
+    </h1>
+    """)
+
+    gr.Markdown(
+    """
+    정적 분석기로 오류라고 보고된 코드를 입력하면,\
+    오류가 True-positive 인지 False-positive 인지 분류 해 주는 프로그램이다.
+    """)
+
+    with gr.Accordion(label='모델에 대한 설명 ( 여기를 클릭 하시오. )',open=False):
+        gr.Markdown(
+        """
+        총 3개의 모델을 사용함
+        쌸라쌸라
+        """
+        )
+    with gr.Row():
+        with gr.Columns():
+            inputs_1 = gr.Textbox(placeholder="코드를 입력하시오.", label='Text')
+            with gr.Row():
+                btn = gr.Button("제출하기")
+        with gr.Column():
+            outputs_1 = gr.Label(num_top_classes=2, label = 'Result')
+    btn.click(fn = greet, inputs = inputs_1, outputs= outputs_1)
+
+if __name__ == "__main__":
+    demo1.launch()