Update backend.py

backend.py

@@ -1,30 +1,30 @@
-import torch
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-from peft import PeftModel, PeftConfig
-
-# Load model and tokenizer only once at startup
-config = PeftConfig.from_pretrained("rabindra-sss/sentiment-distilbert")
-base_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
-model = PeftModel.from_pretrained(base_model, "rabindra-sss/sentiment-distilbert", config=config)
-tokenizer = AutoTokenizer.from_pretrained("rabindra-sss/sentiment-distilbert")
-
-# Ensure model is in evaluation mode for inference
-model.eval()
-
-# Define id2label mappings
-id2label = {0: "Negative", 1: "Positive"}
-
-def predict(text: str) -> str:
-    # Tokenize the input text
-    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
-
-    # Run the model to get logits
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-
-    # Convert logits to predicted class
-    predictions = torch.argmax(logits, dim=-1)
-    predicted_label = id2label[predictions.item()]
-
-    return predicted_label
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+# Load model and tokenizer only once at startup
+config = PeftConfig.from_pretrained("rabindra-sss/sentiment-distilbert/")
+base_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
+model = PeftModel.from_pretrained(base_model, "rabindra-sss/sentiment-distilbert/", config=config)
+tokenizer = AutoTokenizer.from_pretrained("rabindra-sss/sentiment-distilbert/")
+
+# Ensure model is in evaluation mode for inference
+model.eval()
+
+# Define id2label mappings
+id2label = {0: "Negative", 1: "Positive"}
+
+def predict(text: str) -> str:
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+
+    # Run the model to get logits
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+
+    # Convert logits to predicted class
+    predictions = torch.argmax(logits, dim=-1)
+    predicted_label = id2label[predictions.item()]
+
+    return predicted_label