Create handler.py

handler.py

@@ -0,0 +1,61 @@
+from typing import Dict, List, Any
+import pickle
+import os
+import __main__
+import numpy as np
+
+class CollaborativeRecommender:
+    def __init__(self, algo, trainset):
+        self.algo = algo
+        self.trainset = trainset
+
+    def predict(self, user_id, k=10):
+        try:
+            # Convert raw user_id to inner user_id
+            inner_user_id = self.trainset.to_inner_uid(user_id)
+        except ValueError:
+            # User not found in trainset, return None
+            return None
+
+        # Get the list of books the user has interacted with
+        user_books = set(self.trainset.ur[inner_user_id])
+        all_books = set(self.trainset.all_items())
+        unseen_books = all_books - user_books
+        # Predict the ratings for unseen books
+        predictions = [self.algo.predict(self.trainset.to_raw_uid(inner_user_id), self.trainset.to_raw_iid(book_id)) for book_id in unseen_books]
+        # Sort the predictions by estimated rating and return the top-k books
+        top_predictions = sorted(predictions, key=lambda x: x.est, reverse=True)[:k]
+        top_books = [pred.iid for pred in top_predictions]
+        return top_books
+
+__main__.CollaborativeRecommender = CollaborativeRecommender
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        model_path = os.path.join(path, "model.pkl")
+        with open(model_path, 'rb') as f:
+            self.model = pickle.load(f)
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        # Extract the 'inputs' from the data
+        inputs = data.get('inputs', {})
+        
+        # If inputs is a string (for single user_id input), convert it to a dict
+        if isinstance(inputs, str):
+            inputs = {'user_id': inputs}
+        
+        user_id = inputs.get('user_id')
+        k = inputs.get('k', 10)  # Default to 10 if not provided
+
+        if user_id is None:
+            return [{"error": "user_id is required"}]
+
+        try:
+            recommended_books = self.model.predict(user_id, k=k)
+            return [{"recommended_books": recommended_books}]
+        except Exception as e:
+            return [{"error": str(e)}]
+
+def load_model(model_path):
+    handler = EndpointHandler(model_path)
+    return handler