Delete handler.py

handler.py

@@ -1,33 +0,0 @@
-import torch
-from transformers import BertTokenizer, BertForSequenceClassification
-from typing import List
-
-class SentimentModel:
-    def __init__(self, model_path):
-        self.tokenizer = BertTokenizer.from_pretrained(model_path)
-        self.model = BertForSequenceClassification.from_pretrained(model_path)
-        self.model.eval()
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.model.to(self.device)
-
-    def predict(self, texts: List[str]) -> List[float]:
-        max_length = 128
-        encodings = self.tokenizer(texts, truncation=True, padding=True, max_length=max_length, return_tensors="pt")
-        input_ids = encodings['input_ids'].to(self.device)
-        attention_masks = encodings['attention_mask'].to(self.device)
-        
-        with torch.no_grad():
-            outputs = self.model(input_ids, attention_mask=attention_masks)
-            logits = outputs.logits
-            probs = torch.nn.functional.softmax(logits, dim=-1)
-            strong_probs = probs[:, 1].tolist()  # Probability of the "strong" class (class 1)
-
-        return strong_probs
-
-# Initialize the model
-model = SentimentModel(".")
-
-def infer(inputs):
-    texts = [inputs[i]['data'] for i in range(len(inputs))]
-    probabilities = model.predict(texts)
-    return [{"sentiment intensity": prob} for prob in probabilities]