Update index.html

index.html

@@ -24,28 +24,24 @@ import gradio as gr
 from transformers_js_py import pipeline
 from filters import convert
 
-pipe = await pipeline('object-detection', 'Xenova/detr-resnet-50')
+# 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')
 
-async def fn(image):
-	result = await pipe(image)
-	return result
-              
-#demo = gr.Interface.from_pipeline(pipe)
+async def fn(text):
+    result = await pipe(text)
+    return result
 
-async def predict(image):
-  result = await pipe(image)
-  print(result)
-  result = convert(result)
-  print(result) 
-  return image, result
+async def predict(text):
+    result = await pipe(text)
+    return result['label'], result['score']
 
 demo = gr.Interface(
-  fn=predict,
-  inputs=gr.Image(type='pil'),
-  outputs=gr.AnnotatedImage(),
-  title='On-Device Object-Detection with Gradio-Lite & Transformers.js'
+    fn=predict,
+    inputs=gr.Textbox(lines=7, label="Input Text"),
+    outputs="label",
+    title='Sentiment Analysis with KoELECTRA Model'
 )
-         
+
 demo.launch()
             </gradio-file>
 
@@ -78,8 +74,8 @@ def convert(input_data):
 
             <gradio-requirements>
 # Same syntax as requirements.txt
-transformers-js-py            
+transformers_js_py
             </gradio-requirements>
         </gradio-lite>
     </body>
-</html>
+</html>