Dodanie API do rekomendacji

connector/Include/frontend_AI_connector.py

@@ -3,7 +3,7 @@ import psycopg2
 import pandas
 import json
 from configparser import ConfigParser
-
+import requests
 
 app = Flask(__name__)
 db_connector = None
@@ -16,7 +16,7 @@ def error_decorator(fun):
             fun(*args, **kwargs)
         except psycopg2.DatabaseError:
             return jsonify({"status": "Something... unexpected has occured :sweat_smile:"}), 500
-         
+
     return inner1
 
 @app.route("/", methods=["GET"])
@@ -55,8 +55,14 @@ def add_user(oauth_ID, username):
 def get_recommendations(oauth_ID):
     #request od frontu na rekomendacje
     #wysyłanie requestu do AI API o rekomendacje dla usera
-    #przesłanie danych do  
-    return jsonify({"movies": ["3", "Wiedźmin 3", "Najlepszy."]}), 200
+    #przesłanie danych do
+
+    movies = [49026, 155, 312113]  # mock values to be received from backend
+    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'})
+    return jsonify(response.json()), 200
 
 @app.route("/api/v3/get_movie/<int:movie_ID>", methods=["GET"])
 # @error_decorator

movie_recommendations/movie_recommender.py

@@ -3,6 +3,9 @@ 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
+import os
+app = Flask(__name__)
 
 
 def get_director(x):
@@ -104,16 +107,30 @@ 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')
+
+
+@app.route("/api/v3/AI_recommendations", methods=["POST"])
+def AI_recommendations():
+    ids = request.get_json()
+    recommendations = recommender.get_recommendations(ids)
+    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)

movie_recommendations/test.http

@@ -0,0 +1,12 @@
+###
+
+POST http://127.0.0.1:5000/api/v3/AI_recommendations
+Content-Type: application/json
+
+[
+  49026,
+  155,
+  312113
+]
+
+###