Cachowanie works dla AI

movie_recommendations/Test.http

@@ -0,0 +1,15 @@
+### GET request to example server
+GET http://127.0.0.1:5000/boop
+
+###
+
+POST http://127.0.0.1:5000/api/v3/AI_recommendations
+Content-Type: application/json
+
+[
+  49026,
+  155,
+  312113
+]
+
+###

movie_recommendations/movie_recommender.py

@@ -1,8 +1,26 @@
+import hashlib
+import json
+from datetime import datetime
+
 import pandas as pd
 import numpy as np
 from ast import literal_eval
 from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
+from flask import Flask, request, jsonify
+from flask_caching import Cache
+
+
+config = {
+    "DEBUG": True,          # some Flask specific configs
+    "CACHE_TYPE": "SimpleCache",
+    "CACHE_DEFAULT_TIMEOUT": 300
+}
+
+app = Flask(__name__)
+
+app.config.from_mapping(config)
+cache = Cache(app)
 
 
 def get_director(x):
@@ -104,16 +122,38 @@ class MovieRecommender:
                 if recommended_id in movie_ids:
                     continue
 
-                if recommended_movies.get(recommended_id) is None:
-                    recommended_movies[recommended_id] = sim_score / len(movie_ids)
+                if recommended_movies.get(int(recommended_id)) is None:
+                    recommended_movies[int(recommended_id)] = float(round((sim_score / len(movie_ids)), 4))
                 else:
-                    recommended_movies[recommended_id] += sim_score / len(movie_ids)
+                    recommended_movies[int(recommended_id)] += float(round((sim_score / len(movie_ids)), 4))
         return recommended_movies
 
 
+recommender = MovieRecommender()
+recommender.fit('movie_recommendations/datasets/tmdb_5000_credits.csv',
+                'movie_recommendations/datasets/tmdb_5000_movies.csv')
+
+
+def make_cache_key():
+    data = request.get_json()
+    if isinstance(data, list):
+        data = sorted(data)
+    key = hashlib.md5(json.dumps(data).encode('utf-8')).hexdigest()
+    return key
+
+
+@app.route("/api/v3/AI_recommendations", methods=["POST"])
+@cache.cached(timeout=300, key_prefix=make_cache_key)
+def AI_recommendations():
+    ids = request.get_json()
+    recommendations = recommender.get_recommendations(ids)
+    recommendations[0] = datetime.now()
+    return jsonify(recommendations)
+
+
 # Przykładowe użycie:
-if __name__ == "__main__":
-    recommender = MovieRecommender()
-    recommender.fit('datasets/tmdb_5000_credits.csv', 'datasets/tmdb_5000_movies.csv')
-    recommendations = recommender.get_recommendations([49026, 155, 312113])
-    print(recommendations)
+# if __name__ == "__main__":
+#     recommender = MovieRecommender()
+#     recommender.fit('datasets/tmdb_5000_credits.csv', 'datasets/tmdb_5000_movies.csv')
+#     recommendations = recommender.get_recommendations([49026, 155, 312113])
+#     print(recommendations)