Update app.py

app.py

@@ -1,11 +1,11 @@
 import torch
-# from utils import label_full_decoder
-# import sys
-# import dataset
-# import engine
-# from model import BERTBaseUncased
+from utils import label_full_decoder
+import sys
+import dataset
+import engine
+from model import BERTBaseUncased
 # from tokenizer import tokenizer
-# import config
+import config
 from transformers import pipeline, AutoTokenizer, AutoModel
 import gradio as gr
 
@@ -16,8 +16,9 @@ URL = "https://huggingface.co/FFZG-cleopatra/bert-emoji-latvian-twitter/blob/mai
 response = requests.get(URL)
 open("pytorch_model.bin", "wb").write(response.content)
 
+model_path = "pytorch_model.bin"
 
-model = AutoModel.from_pretrained("thak123/bert-emoji-latvian-twitter-classifier")
+# model = AutoModel.from_pretrained("thak123/bert-emoji-latvian-twitter-classifier")
 # 7 EPOCH Version
 BERT_PATH = "FFZG-cleopatra/bert-emoji-latvian-twitter"
 
@@ -27,9 +28,9 @@ tokenizer = transformers.BertTokenizer.from_pretrained(
 )
 #AutoTokenizer.from_pretrained("FFZG-cleopatra/bert-emoji-latvian-twitter")
 
-classifier = pipeline("sentiment-analysis",
-                      model= model, 
-                      tokenizer = tokenizer)
+# classifier = pipeline("sentiment-analysis",
+#                       model= model, 
+#                       tokenizer = tokenizer)
 
 # MODEL = BERTBaseUncased()
 # MODEL.load_state_dict(torch.load(config.MODEL_PATH, map_location=torch.device(DEVICE)))
@@ -63,6 +64,7 @@ def preprocess(text):
 
 def sentence_prediction(sentence):
     # sentence = preprocess(sentence)
+    
     # model_path = config.MODEL_PATH
 
     # test_dataset = dataset.BERTDataset(
@@ -79,8 +81,8 @@ def sentence_prediction(sentence):
     # device = config.device
 
     model = BERTBaseUncased()
-    # model.load_state_dict(torch.load(
-    #     model_path, map_location=torch.device(device)))
+    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)
@@ -91,39 +93,7 @@ def sentence_prediction(sentence):
     return outputs #{"label":outputs[0]}
 
 
-     
-# demo = gr.Interface(
-#     fn=sentence_prediction, 
-#     inputs=gr.Textbox(placeholder="Enter a  sentence here..."), 
-#     outputs="label", 
-#     interpretation="default",
-#     examples=[["!"]])
-
-# demo.launch()
-
-# gr.Interface(fn=sentence_prediction,inputs="text",outputs="label").launch()
-
-
-
-# def greet(name):
-#     return "Hello " + name + "!"
-
-# demo = gr.Interface(
-#     fn=greet,
-#     inputs=gr.Textbox(lines=2, placeholder="Name Here..."),
-#     outputs="text",
-# )
-# demo.launch()
-
-
-# import gradio as gr
-
-# from transformers import pipeline
-
-# pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
 
-def predict(text):
-  return pipe(text)[0]["translation_text"]
 
 demo = gr.Interface(
   fn=sentence_prediction,