AI API fully working

connector/Include/frontend_AI_connector.py

@@ -4,8 +4,8 @@ import pandas
 import json
 from configparser import ConfigParser
 from datetime import datetime
-
 import requests
+from flask_caching import Cache
 
 app = Flask(__name__)
 cache = Cache(config={'CACHE_TYPE': 'SimpleCache'})  
@@ -57,12 +57,11 @@ def add_user(oauth_ID, username):
 #roboczy endpoint służący do wyciąganiu rekomendacji
 @app.route("/api/v3/ai/<string:oauth_ID>", methods=["GET"])
 def get_recommendations(oauth_ID):
-    #request od frontu na rekomendacje
+    cursor = conn.cursor()
-    #wysyłanie requestu do AI API o rekomendacje dla usera
+    cursor.execute("select movie_ID from ratings where oauth_ID='{}'", oauth_ID)
-    #przesłanie danych do
+    res = cursor.fetchall()
-
+    movies = [int(i) for i in res[0]]
-    movies = [49026, 155, 312113]  # mock values to be received from backend
+    url = 'http://localhost:4200/api/v3/AI_recommendations'
-    url = 'http://localhost:8080/api/v3/AI_recommendations'  # nie wiem, jakie powinno być url
     response = requests.post(url,
                              json=movies,
                              headers={'Content-Type': 'application/json'})

connector/Include/init_scripts/constants.ini

@@ -1,5 +1,5 @@
 [postgres]
-host=db
+host=localhost
 database=test_db
 user=root
 password=root

movie_recommendations/Test.http

@@ -3,7 +3,11 @@ GET http://127.0.0.1:5000/boop
 
 ###
 
-POST http://127.0.0.1:5000/api/v3/AI_recommendations
+GET http://127.0.0.1:8090/api/v3/ai/1111
+
+###
+
+POST http://127.0.0.1:4200/api/v3/AI_recommendations
 Content-Type: application/json
 
 [

movie_recommendations/movie_recommender.py

@@ -1,26 +1,20 @@
-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
+import hashlib
+import json
+from configparser import ConfigParser
+import psycopg2
 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__)
-
+cache = Cache(config={'CACHE_TYPE': 'SimpleCache'})
-app.config.from_mapping(config)
+db_connector = None
-cache = Cache(app)
+conn = None
 
 
 def get_director(x):
@@ -130,8 +124,8 @@ class MovieRecommender:
 
 
 recommender = MovieRecommender()
-recommender.fit('movie_recommendations/datasets/tmdb_5000_credits.csv',
+recommender.fit('datasets/tmdb_5000_credits.csv',
-                'movie_recommendations/datasets/tmdb_5000_movies.csv')
+                'datasets/tmdb_5000_movies.csv')
 
 
 def make_cache_key():
@@ -147,13 +141,30 @@ def 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__":
-# if __name__ == "__main__":
+    config = ConfigParser()
-#     recommender = MovieRecommender()
+    config.read("../connector/Include/init_scripts/constants.ini")
-#     recommender.fit('datasets/tmdb_5000_credits.csv', 'datasets/tmdb_5000_movies.csv')
+
-#     recommendations = recommender.get_recommendations([49026, 155, 312113])
+    while True:
-#     print(recommendations)
+        try:
+            conn = psycopg2.connect(
+                host=config["postgres"]["host"],
+                database=config["postgres"]["database"],
+                user=config["postgres"]["user"],
+                password=config["postgres"]["password"],
+                port=int(config["postgres"]["port"])
+            )
+
+        except Exception:
+            print("Trying to connect with database")
+            continue
+        else:
+            break
+
+    cache.init_app(app)
+    app.run(host="0.0.0.0", port=4200, debug=False)
+
+    conn.close()