Update app.py

app.py

@@ -21,40 +21,6 @@ def get_image_base64(path):
 def load_model():
     return ModelWrapper()
 
-"""@st.cache_resource
-class ModelWrapper(object):
-    MODELS_DIR: str = "./new_models/"
-    MODEL_NAME: str = "model"
-    TOKENIZER: str = "tokenizer"
-
-    def __init__(self):
-        self.model = AutoModelForSequenceClassification.from_pretrained(
-            ModelWrapper.MODELS_DIR + ModelWrapper.MODEL_NAME, torchscript=True
-        )
-        self.tokenizer = BertTokenizerFast.from_pretrained(
-            "blanchefort/rubert-base-cased-sentiment"
-        )
-        self.id2label: dict[int, str] = {0: "__label__positive", 1: "__label__negative"}
-
-    @torch.no_grad()
-    def __call__(self, text: str) -> str:
-        max_input_length = (
-            self.model.config.max_position_embeddings
-        )  # 512 for this model
-        inputs = self.tokenizer(
-            text,
-            max_length=max_input_length,
-            padding=True,
-            truncation=True,
-            return_tensors="pt",
-        )
-        outputs = self.model(
-            **inputs, return_dict=True
-        )  # output is logits for huggingfcae transformers
-        predicted = torch.nn.functional.softmax(outputs.logits, dim=1)
-        predicted_id = torch.argmax(predicted, dim=1).numpy()[0]
-        return self.id2label[predicted_id]"""
-
 model_wrapper= load_model()
 
 bin_str = get_image_base64("./билли.png")