Update app.py

app.py

@@ -1,4 +1,5 @@
 import torch
+from utils import label_full_decoder
 import sys
 import dataset
 import engine
@@ -8,6 +9,9 @@ import config
 
 import gradio as gr
 
+MODEL = None
+DEVICE = config.device
+
 
 T = tokenizer.TweetTokenizer(
     preserve_handles=True, preserve_hashes=True, preserve_case=False, preserve_url=False)
@@ -48,24 +52,30 @@ def sentence_prediction(sentence):
         num_workers=3
     )
 
-    device = config.device
+    # device = config.device
 
-    model = BERTBaseUncased()
-    model.load_state_dict(torch.load(
-        model_path, map_location=torch.device(device)))
-    model.to(device)
+    # model = BERTBaseUncased()
+    # model.load_state_dict(torch.load(
+    #     model_path, map_location=torch.device(device)))
+    # model.to(device)
 
     outputs, [] = engine.predict_fn(test_data_loader, model, device)
     print(outputs)
     return {"label":outputs[0]}
 
-demo = gr.Interface(
-    fn=sentence_prediction, 
-    inputs=gr.Textbox(placeholder="Enter a  sentence here..."), 
-    # outputs="label", 
-    # interpretation="default",
-    examples=[["!"]])
-
-demo.launch(debug = True,
-            enable_queue=True,
-            show_error = True)
+if __name__ == "__main__":
+    MODEL = BERTBaseUncased()
+    MODEL.load_state_dict(torch.load(
+        config.MODEL_PATH, map_location=torch.device(DEVICE)))
+    MODEL.eval()
+    
+    demo = gr.Interface(
+        fn=sentence_prediction, 
+        inputs=gr.Textbox(placeholder="Enter a  sentence here..."), 
+        outputs="label", 
+        # interpretation="default",
+        examples=[["!"]])
+    
+    demo.launch(debug = True,
+                enable_queue=True,
+                show_error = True)