Upload app.py

app.py

@@ -1,11 +1,13 @@
 import gradio as gr
+import fasttext
+
 from transformers import AutoModelForSequenceClassification
 from transformers import AutoTokenizer
+
 import random
 import numpy as np
 import pandas as pd
 import torch
-import fasttext
 
 
 
@@ -14,8 +16,8 @@ label2id = {"NEGATIVE": 0, "POSITIVE": 1}
 
 
 title = "Movie Review Score Discriminator"
-description = "It is a program that classifies whether it is positive or negative by entering movie reviews. \
-                You can choose between the Korean version and the English version. \
+description = "It is a program that classifies whether it is positive or negative by entering movie reviews.  \
+                You can choose between the Korean version and the English version.  \
                 It also provides a version called Any, which determines whether it is Korean or English and predicts it."
 
 
@@ -94,8 +96,6 @@ def builder(lang, text):
     with torch.no_grad():
         logits = model(input_ids=inputs['input_ids'], 
             attention_mask=inputs['attention_mask']).logits
-
-
     
     m = torch.nn.Softmax(dim=1)
     output = m(logits)
@@ -109,7 +109,7 @@ def builder(lang, text):
 
 
 demo = gr.Interface(builder, inputs=[gr.inputs.Dropdown(['Any', 'Eng', 'Kor']), "text"], 
-                            outputs=gr.Label(num_top_classes=2, label='Res', color='CadetBlue'), 
+                            outputs=gr.Label(num_top_classes=2, label='Result', color='CadetBlue'), 
                             # outputs='label',
                             title=title, description=description, examples=examples)
 
@@ -118,7 +118,6 @@ demo = gr.Interface(builder, inputs=[gr.inputs.Dropdown(['Any', 'Eng', 'Kor']),
 #                          title=title, theme="peach",
 #                          allow_flagging="auto",
 #                          description=description, examples=examples)
-output = []
 
 if __name__ == "__main__":
     # print(examples)