"Add custom pipeline model logic"

model.py

@@ -0,0 +1,37 @@
+from typing import List, Dict
+import numpy as np
+import torch
+from transformers import BertForSequenceClassification, BertTokenizer
+from sklearn.preprocessing import LabelEncoder
+from huggingface_hub import hf_hub_download
+
+class CustomBertClassifier:
+    def __init__(self):
+        # Load model and tokenizer
+        self.model = BertForSequenceClassification.from_pretrained(".")
+        self.tokenizer = BertTokenizer.from_pretrained(".")
+        self.model.eval()
+
+        # Load label classes
+        label_path = hf_hub_download(repo_id="JaySenpai/bert-model", filename="label_classes.npy")
+        self.le = LabelEncoder()
+        self.le.classes_ = np.load(label_path, allow_pickle=True)
+
+    def __call__(self, inputs: str) -> List[Dict]:
+        # Tokenize input
+        inputs = self.tokenizer(inputs, return_tensors="pt", truncation=True, padding=True)
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        probs = probs[0].tolist()
+
+        # Map to labels
+        results = []
+        for i, prob in enumerate(probs):
+            results.append({
+                "label": self.le.classes_[i],
+                "score": round(prob, 4)
+            })
+        # Sort by score descending
+        results = sorted(results, key=lambda x: x["score"], reverse=True)
+        return results