Create handler.py

handler.py

@@ -0,0 +1,35 @@
+from typing import Dict, List, Any
+import spacy
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # This loads the model. 
+        # 'path' points to the repo storage, but since we will install 
+        # the model via requirements.txt, we load it by package name.
+        try:
+            self.nlp = spacy.load("en_core_web_sm")
+        except OSError:
+            # Fallback: try loading from path if you uploaded model files directly
+            self.nlp = spacy.load(path)
+
+    def __call__(self, data: Dict[str, Any]) -> List[str]:
+        """
+        Args:
+            data (:obj:): The payload of the request. 
+            Usually contains {"inputs": "Your text here"}
+        """
+        # 1. Get the raw text from the input JSON
+        raw_text = data.pop("inputs", data)
+        
+        # Handle edge case if input is sent as a list
+        if isinstance(raw_text, list):
+            raw_text = raw_text[0]
+
+        # 2. Process with spaCy (The exact logic you provided)
+        doc = self.nlp(raw_text)
+        
+        # 3. Extract sentences (Your specific list comprehension)
+        raw_sentences = [s.text.strip() for s in doc.sents if s.text.strip()]
+        
+        # 4. Return the list of strings
+        return raw_sentences