Update index.html

index.html

@@ -24,22 +24,22 @@ import gradio as gr
 from transformers_js_py import pipeline
 from filters import convert
 
-# 기존 파이프라인은 object-detection을 사용하므로, 새로운 파이프라인을 생성하여 koelectra-base-v3-discriminator 모델을 사용하도록 수정합니다.
-pipe = await pipeline('sentiment-analysis', 'monologg/koelectra-base-v3-discriminator')
+# 모델과 tokenizer 파일 경로
+model_name = 'monologg/koelectra-base-v3-discriminator'
+tokenizer_path = 'tokenizer.json'
 
-async def fn(text):
-    result = await pipe(text)
-    return result
+# 모델 및 tokenizer 로드
+pipe = await pipeline('sentiment-analysis', model_name, tokenizer=tokenizer_path)
 
 async def predict(text):
     result = await pipe(text)
-    return result['label'], result['score']
+    return result
 
 demo = gr.Interface(
     fn=predict,
-    inputs=gr.Textbox(lines=7, label="Input Text"),
-    outputs="label",
-    title='Sentiment Analysis with KoELECTRA Model'
+    inputs="text",
+    outputs="text",
+    title="KoELECTRA Sentiment Analysis"
 )
 
 demo.launch()
@@ -74,8 +74,9 @@ def convert(input_data):
 
             <gradio-requirements>
 # Same syntax as requirements.txt
-transformers_js_py
+transformers-js-py            
             </gradio-requirements>
         </gradio-lite>
     </body>
 </html>
+