Update app.py

app.py

@@ -11,8 +11,8 @@ import gradio as gr
 
 # DEVICE = config.device
 
-# model = AutoModel.from_pretrained("thak123/bert-emoji-latvian-twitter-classifier")
-# tokenizer = AutoTokenizer.from_pretrained("FFZG-cleopatra/bert-emoji-latvian-twitter")
+model = AutoModel.from_pretrained("thak123/bert-emoji-latvian-twitter-classifier")
+tokenizer = AutoTokenizer.from_pretrained("FFZG-cleopatra/bert-emoji-latvian-twitter")
 
 # classifier = pipeline("sentiment-analysis",
 #                       model= model, 
@@ -49,7 +49,7 @@ def preprocess(text):
 
 
 def sentence_prediction(sentence):
-    sentence = preprocess(sentence)
+    # sentence = preprocess(sentence)
     # model_path = config.MODEL_PATH
 
     # test_dataset = dataset.BERTDataset(
@@ -92,30 +92,30 @@ def sentence_prediction(sentence):
 
 
 
-def greet(name):
-    return "Hello " + name + "!"
+# def greet(name):
+#     return "Hello " + name + "!"
 
-demo = gr.Interface(
-    fn=greet,
-    inputs=gr.Textbox(lines=2, placeholder="Name Here..."),
-    outputs="text",
-)
-demo.launch()
+# demo = gr.Interface(
+#     fn=greet,
+#     inputs=gr.Textbox(lines=2, placeholder="Name Here..."),
+#     outputs="text",
+# )
+# demo.launch()
 
 
-import gradio as gr
+# import gradio as gr
 
-from transformers import pipeline
+# from transformers import pipeline
 
-pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
+# pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
 
 def predict(text):
   return pipe(text)[0]["translation_text"]
 
 demo = gr.Interface(
-  fn=predict, 
+  fn=sentence_prediction, 
   inputs='text',
-  outputs='text',
+  outputs='label',
 )
 
 demo.launch()