Create app.py

app.py

@@ -0,0 +1,70 @@
+import torch
+import sys
+import dataset
+import engine
+from model import BERTBaseUncased
+from tokenizer import tokenizer
+
+
+config = {
+    "device":
+}
+
+T = tokenizer.TweetTokenizer(
+    preserve_handles=True, preserve_hashes=True, preserve_case=False, preserve_url=False)
+
+def preprocess(text):
+    tokens = T.tokenize(text)
+    print(tokens, file=sys.stderr)
+    ptokens = []
+    for index, token in enumerate(tokens):
+        if "@" in token:
+            if index > 0:
+                # check if previous token was mention
+                if "@" in tokens[index-1]:
+                    pass
+                else:
+                    ptokens.append("mention_0")
+            else:
+                ptokens.append("mention_0")
+        else:
+            ptokens.append(token)
+
+    print(ptokens, file=sys.stderr)
+    return " ".join(ptokens)
+
+
+def sentence_prediction(sentence):
+    sentence = preprocess(sentence)
+    model_path = config.MODEL_PATH
+
+    test_dataset = dataset.BERTDataset(
+        review=[sentence],
+        target=[0]
+    )
+
+    test_data_loader = torch.utils.data.DataLoader(
+        test_dataset,
+        batch_size=config.VALID_BATCH_SIZE,
+        num_workers=3
+    )
+
+    device = config.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 outputs[0]
+
+demo = gr.Interface(
+    fn=sentence_prediction, 
+    inputs=gr.Textbox(placeholder="Enter a  sentence here..."), 
+    outputs="label", 
+    interpretation="default",
+    examples=[["!"]])
+
+demo.launch()