recommendation engine 75% polished

recommondation-engine/app.py

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+# pip install mysql.connector
+
+import mysql.connector
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import logging
+from unittest import result
+
+def database():
+    db_connection = mysql.connector.connect(
+        host = "localhost",
+        port = "3306",
+        user = "root",
+        database = "Marketplace"
+    )
+    return db_connection
+
+
+def get_all_products():
+
+    db_con = database()
+    cursor = db_con.cursor()
+
+    cursor.execute("SELECT CategoryID FROM Category")
+    categories = cursor.fetchall()
+
+    select_clause = "SELECT p.ProductID"
+    for category in categories:
+        category_id = category[0]
+        select_clause += f", MAX(CASE WHEN pc.CategoryID = {category_id} THEN 1 ELSE 0 END) AS `Cat_{category_id}`"
+
+    final_query = f"""
+        {select_clause}
+        FROM Product p
+        LEFT JOIN Product_Category pc ON p.ProductID = pc.ProductID
+        LEFT JOIN Category c ON pc.CategoryID = c.CategoryID
+        GROUP BY p.ProductID;
+        """
+
+    cursor.execute(final_query)
+    results = cursor.fetchall()
+
+    final = []
+    for row in results:
+        text_list = list(row)
+        text_list.pop(0)
+        final.append(text_list)
+
+    cursor.close()
+    db_con.close()
+    return final
+
+def get_user_history(user_id):
+    db_con = database()
+    cursor = db_con.cursor()
+
+    cursor.execute("SELECT CategoryID FROM Category")
+    categories = cursor.fetchall()
+
+    select_clause = "SELECT p.ProductID"
+    for category in categories:
+        category_id = category[0] # get the uid of the catefory and then append that to the new column
+        select_clause += f", MAX(CASE WHEN pc.CategoryID = {category_id} THEN 1 ELSE 0 END) AS `Cat_{category_id}`"
+
+    final_query = f"""
+        {select_clause}
+        FROM Product p
+        LEFT JOIN Product_Category pc ON p.ProductID = pc.ProductID
+        LEFT JOIN Category c ON pc.CategoryID = c.CategoryID
+        where p.ProductID in (select ProductID from History where UserID = {user_id})
+        GROUP BY p.ProductID;
+        """
+
+    cursor.execute(final_query)
+    results = cursor.fetchall()
+    final = []
+    for row in results:
+        text_list = list(row)
+        text_list.pop(0)
+        final.append(text_list)
+
+    cursor.close()
+    db_con.close()
+    return final
+
+
+def get_recommendations(user_id, top_n=10):
+    try:
+        # Get all products and user history with their category vectors
+        all_products = get_all_products()
+        user_history = get_user_history(user_id)
+        # if not user_history:
+        #     # Cold start: return popular products
+        #     return get_popular_products(top_n)
+        # Calculate similarity between all products and user history
+        user_profile = np.mean(user_history, axis=0)  # Average user preferences
+        similarities = cosine_similarity([user_profile], all_products)
+        # finds the indices of the top N products that have the highest
+        # cosine similarity with the user's profile and sorted from most similar to least similar.
+        product_indices = similarities[0].argsort()[-top_n:][::-1]
+        print("product", product_indices)
+
+        # Get the recommended product IDs
+        recommended_products = [all_products[i][0] for i in product_indices]  # Product IDs
+
+        # Upload the recommendations to the database
+        history_upload(user_id, product_indices)  # Pass the indices directly to history_upload
+
+        # Return recommended product IDs
+        return recommended_products
+    except Exception as e:
+        logging.error(f"Recommendation error for user {user_id}: {str(e)}")
+        # return get_popular_products(top_n)  # Fallback to popular products
+
+def history_upload(userID, anrr):
+    db_con = database()
+    cursor = db_con.cursor()
+
+    try:
+        for item in anrr:
+            #Product ID starts form index 1
+            item_value = item + 1
+            print(item_value)
+            # Use parameterized queries to prevent SQL injection
+            cursor.execute(f"INSERT INTO Recommendation (UserID, RecommendedProductID) VALUES ({userID}, {item_value});")
+
+        # Commit the changes
+        db_con.commit()
+
+        # If you need results, you'd typically fetch them after a SELECT query
+        # results = cursor.fetchall()
+        # print(results)
+
+    except Exception as e:
+        print(f"Error: {e}")
+        db_con.rollback()
+    finally:
+        # Close the cursor and connection
+        cursor.close()
+        db_con.close()