Merge branch 'main' into frontend_AI_communication

Dockerfile

@@ -1,25 +1,15 @@
-# Use an official Python runtime as a parent image
+FROM node:latest
-FROM python:3.10-slim
 
-# Set the working directory to /app
+# Create app directory
-WORKDIR /app
+WORKDIR /usr/src/app
 
-# Install necessary OS packages
+# Install app dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential \
-    libpq-dev \
-    && rm -rf /var/lib/apt/lists/*
 
-# Copy the Python script, requirements, and constants.ini file into the container at /app
+# If you also need http-server globally
-COPY connector/Include/frontend_AI_connector.py /app/
+RUN npm install -g http-server
-COPY connector/Include/requirements.txt /app/
-COPY connector/Include/init_scripts/constants.ini /app/init_scripts/
 
-# Modify requirements.txt to use psycopg2-binary
+# Bundle app source
-RUN sed -i 's/psycopg2==2.9.9/psycopg2-binary==2.9.9/' requirements.txt
+COPY . .
 
-# Install any needed packages specified in requirements.txt
+EXPOSE 8080
-RUN pip install --no-cache-dir -r requirements.txt
+CMD ["http-server", "-p 8080"]
-
-# Run frontend_AI_connector.py when the container launches
-CMD ["python", "./frontend_AI_connector.py"]

Dockerfile_backend

@@ -0,0 +1,25 @@
+# Use an official Python runtime as a parent image
+FROM python:3.10-slim
+
+# Set the working directory to /app
+WORKDIR /app
+
+# Install necessary OS packages
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    libpq-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy the Python script, requirements, and constants.ini file into the container at /app
+COPY connector/Include/frontend_AI_connector.py /app/
+COPY connector/Include/requirements.txt /app/
+COPY connector/Include/init_scripts/constants.ini /app/init_scripts/
+
+# Modify requirements.txt to use psycopg2-binary
+RUN sed -i 's/psycopg2==2.9.9/psycopg2-binary==2.9.9/' requirements.txt
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Run frontend_AI_connector.py when the container launches
+CMD ["python", "./frontend_AI_connector.py"]

docs/IndividualServices/analytics_service.txt

@@ -0,0 +1,12 @@
+1. Receive data from database
+2. Calculate data:    
+    a) User Count 
+    b) Movies Count 
+    c) Rating Count 
+    d) Total data size 
+    e) "Hot" movies -> movies that received most ratings during last 
+        week 
+    f) System logs (keeps tracks of all messages exchanged by all services)
+        1. timestamp (when was a message send)
+        2. Message raw data 
+3. Send requests to frondend upon request 

docs/toDos/deployment_script.txt

@@ -0,0 +1,25 @@
+One script upon being ran should deploy the entire solution to some 
+cloud service
+
+It should:
+1. Build everything 
+2. Connect to cloud service (Azure?)
+3. Send the data 
+
+After it will be run website should be accessible under some address 
+    (cloud service should provide this address?)
+
+Decide:
+    What cloud service? (Azure?) Requirements:
+        a. Free (https://github.com/cloudcommunity/Cloud-Free-Tier-Comparison)
+        b. Popular 
+            AWS:
+                +   Most popular 
+                +   "Always" free
+                +   AWS CDK available
+            Azure 
+                -Microsoft
+            Google Cloud 
+                +We have google accounts anyway
+    What technology for script (Ansible?)
+

docs/toDos/monitoring_service.txt

@@ -0,0 +1,12 @@
+Monitoring service 
+Monitoring service should keep track of all communication 
+send between all services 
+Store logs with:
+1. timestamp (when was a message send)
+2. Message raw data 
+
+To decide: 
+    What to use for Monitoring service? 
+    Maybe cloud service will provide us with functioning one?
+    Maybe deployment tool already has one?
+

frontend/.eslintrc.cjs

@@ -0,0 +1,19 @@
+module.exports = {
+  root: true,
+  env: { browser: true, es2020: true },
+  extends: [
+    'eslint:recommended',
+    'plugin:@typescript-eslint/recommended',
+    'plugin:react-hooks/recommended',
+    'prettier',
+  ],
+  ignorePatterns: ['dist', '.eslintrc.cjs'],
+  parser: '@typescript-eslint/parser',
+  plugins: ['react-refresh'],
+  rules: {
+    'react-refresh/only-export-components': [
+      'warn',
+      { allowConstantExport: true },
+    ],
+  },
+};

frontend/.gitignore

@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

frontend/.prettierrc.json

@@ -0,0 +1,3 @@
+{
+  "singleQuote": true
+}

frontend/Dockerfile

@@ -0,0 +1,19 @@
+FROM node:18-alpine AS build-stage
+
+WORKDIR /app
+
+COPY package.json .
+
+RUN npm install
+
+COPY . .
+
+RUN npm run build
+
+FROM nginx:stable-alpine
+
+COPY --from=build-stage /app/dist /usr/share/nginx/html
+
+EXPOSE 80
+
+CMD ["nginx", "-g", "daemon off;"]

frontend/index.html

@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Movie Recommendation</title>
+  </head>
+  <body>
+    <div id="root" class="flex min-h-dvh flex-col bg-gray-800 text-white"></div>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
+</html>

frontend/package.json

@@ -0,0 +1,37 @@
+{
+  "name": "movie-recommendation-client",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "tsc && vite build",
+    "lint": "eslint . --ext ts,tsx --report-unused-disable-directives --max-warnings 0",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "firebase": "^10.11.1",
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0",
+    "react-firebase-hooks": "^5.1.1",
+    "react-loader-spinner": "^6.1.6",
+    "react-router-dom": "^6.23.1"
+  },
+  "devDependencies": {
+    "@types/react": "^18.2.66",
+    "@types/react-dom": "^18.2.22",
+    "@typescript-eslint/eslint-plugin": "^7.2.0",
+    "@typescript-eslint/parser": "^7.2.0",
+    "@vitejs/plugin-react-swc": "^3.5.0",
+    "autoprefixer": "^10.4.19",
+    "eslint": "^8.57.0",
+    "eslint-config-prettier": "^9.1.0",
+    "eslint-plugin-react-hooks": "^4.6.0",
+    "eslint-plugin-react-refresh": "^0.4.6",
+    "postcss": "^8.4.38",
+    "prettier": "^3.2.5",
+    "tailwindcss": "^3.4.3",
+    "typescript": "^5.2.2",
+    "vite": "^5.2.0"
+  }
+}

frontend/postcss.config.js

@@ -0,0 +1,6 @@
+export default {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+}

frontend/public/vite.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>

frontend/src/App.tsx

@@ -0,0 +1,33 @@
+import { Outlet, RouterProvider, createBrowserRouter } from 'react-router-dom';
+import Navbar from './components/Navbar';
+import Home from './pages/Home';
+import Recommendations from './pages/Recommendations';
+import Rate from './pages/Rate';
+import Analytics from './pages/Analytics';
+
+function App() {
+  const Layout = (
+    <>
+      <Navbar />
+      <Outlet />
+    </>
+  );
+
+  const router = createBrowserRouter([
+    {
+      path: '/',
+      element: Layout,
+      children: [
+        { path: '/', element: <Home /> },
+        { path: '/recommendations', element: <Recommendations /> },
+        { path: '/rate', element: <Rate /> },
+        { path: '/analytics', element: <Analytics /> },
+        { path: '*', element: <code>404</code> },
+      ],
+    },
+  ]);
+
+  return <RouterProvider router={router} />;
+}
+
+export default App;

frontend/src/assets/google.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 128 128"><path fill="#fff" d="M44.59 4.21a63.28 63.28 0 004.33 120.9 67.6 67.6 0 0032.36.35 57.13 57.13 0 0025.9-13.46 57.44 57.44 0 0016-26.26 74.33 74.33 0 001.61-33.58H65.27v24.69h34.47a29.72 29.72 0 01-12.66 19.52 36.16 36.16 0 01-13.93 5.5 41.29 41.29 0 01-15.1 0A37.16 37.16 0 0144 95.74a39.3 39.3 0 01-14.5-19.42 38.31 38.31 0 010-24.63 39.25 39.25 0 019.18-14.91A37.17 37.17 0 0176.13 27a34.28 34.28 0 0113.64 8q5.83-5.8 11.64-11.63c2-2.09 4.18-4.08 6.15-6.22A61.22 61.22 0 0087.2 4.59a64 64 0 00-42.61-.38z"/><path fill="#e33629" d="M44.59 4.21a64 64 0 0142.61.37 61.22 61.22 0 0120.35 12.62c-2 2.14-4.11 4.14-6.15 6.22Q95.58 29.23 89.77 35a34.28 34.28 0 00-13.64-8 37.17 37.17 0 00-37.46 9.74 39.25 39.25 0 00-9.18 14.91L8.76 35.6A63.53 63.53 0 0144.59 4.21z"/><path fill="#f8bd00" d="M3.26 51.5a62.93 62.93 0 015.5-15.9l20.73 16.09a38.31 38.31 0 000 24.63q-10.36 8-20.73 16.08a63.33 63.33 0 01-5.5-40.9z"/><path fill="#587dbd" d="M65.27 52.15h59.52a74.33 74.33 0 01-1.61 33.58 57.44 57.44 0 01-16 26.26c-6.69-5.22-13.41-10.4-20.1-15.62a29.72 29.72 0 0012.66-19.54H65.27c-.01-8.22 0-16.45 0-24.68z"/><path fill="#319f43" d="M8.75 92.4q10.37-8 20.73-16.08A39.3 39.3 0 0044 95.74a37.16 37.16 0 0014.08 6.08 41.29 41.29 0 0015.1 0 36.16 36.16 0 0013.93-5.5c6.69 5.22 13.41 10.4 20.1 15.62a57.13 57.13 0 01-25.9 13.47 67.6 67.6 0 01-32.36-.35 63 63 0 01-23-11.59A63.73 63.73 0 018.75 92.4z"/></svg>

frontend/src/components/Navbar.tsx

@@ -0,0 +1,55 @@
+import { useAuthState, useSignOut } from 'react-firebase-hooks/auth';
+import { Link, NavLink } from 'react-router-dom';
+import { auth } from '../firebase';
+
+function Navbar() {
+  const [user, userLoading] = useAuthState(auth);
+  const [signOut] = useSignOut(auth);
+
+  return (
+    <header className="px-6 py-4 flex justify-between items-center">
+      <Link to="/">
+        <h1 className="text-xl sm:text-2xl">Movie Recommendation</h1>
+      </Link>
+      {userLoading && <p>Loading user info</p>}
+      {!userLoading && !user && (
+        <p className="text-gray-300 text-lg">Logged out</p>
+      )}
+      {!!user && (
+        <div className="flex gap-8">
+          <nav>
+            <ul className="flex flex-col text-center gap-4 md:flex-row">
+              <li>
+                <NavLink
+                  to="/recommendations"
+                  className={({ isActive }) => (isActive ? 'font-medium' : '')}
+                >
+                  Recommendations
+                </NavLink>
+              </li>
+              <li>
+                <NavLink
+                  to="/rate"
+                  className={({ isActive }) => (isActive ? 'font-medium' : '')}
+                >
+                  Rate
+                </NavLink>
+              </li>
+              <li>
+                <NavLink
+                  to="/analytics"
+                  className={({ isActive }) => (isActive ? 'font-medium' : '')}
+                >
+                  Analytics
+                </NavLink>
+              </li>
+            </ul>
+          </nav>
+          <button onClick={() => signOut()}>Log out</button>
+        </div>
+      )}
+    </header>
+  );
+}
+
+export default Navbar;

frontend/src/components/Welcome.tsx

@@ -0,0 +1,55 @@
+import {
+  collection,
+  getCountFromServer,
+  query,
+  where,
+} from 'firebase/firestore';
+import { useEffect, useState } from 'react';
+import { useAuthState } from 'react-firebase-hooks/auth';
+import { Link } from 'react-router-dom';
+import { auth, db } from '../firebase';
+
+const ratingsRef = collection(db, 'ratings');
+
+function Welcome() {
+  const [user] = useAuthState(auth);
+  const [ratingsCount, setRatingsCount] = useState<number | null>(null);
+
+  if (!user) throw new Error('No user');
+
+  useEffect(() => {
+    (async () => {
+      const ratingsQuery = query(ratingsRef, where('userId', '==', user.uid));
+      const snapshot = await getCountFromServer(ratingsQuery);
+
+      setRatingsCount(snapshot.data().count);
+    })();
+  }, [user.uid]);
+
+  return (
+    <main>
+      <p className="text-lg mb-8">Hello {user.displayName}!</p>
+      {ratingsCount === null && <p>Loading your data</p>}
+      {Number.isInteger(ratingsCount) &&
+        (ratingsCount === 0 ? (
+          <p>
+            <span>You don't have any ratings yet. Please </span>
+            <Link to="/rate" className="underline">
+              rate
+            </Link>
+            <span> your first movie.</span>
+          </p>
+        ) : (
+          <p>
+            <span>Go to </span>
+            <Link to="/recommendations" className="underline">
+              recommendations
+            </Link>
+            <span> to get inspired.</span>
+          </p>
+        ))}
+    </main>
+  );
+}
+
+export default Welcome;

frontend/src/firebase.ts

@@ -0,0 +1,18 @@
+import { initializeApp } from 'firebase/app';
+import { getAuth } from 'firebase/auth';
+import { getFirestore } from 'firebase/firestore';
+
+const firebaseConfig = {
+  apiKey: 'AIzaSyAF34ovW0qaDlV68sbpCjylz4TQ0he_XEM',
+  authDomain: 'movie-recommendation-2024.firebaseapp.com',
+  projectId: 'movie-recommendation-2024',
+  storageBucket: 'movie-recommendation-2024.appspot.com',
+  messagingSenderId: '777010585410',
+  appId: '1:777010585410:web:7db1436b72879d512032db',
+};
+
+const app = initializeApp(firebaseConfig);
+const auth = getAuth(app);
+const db = getFirestore(app);
+
+export { auth, db };

frontend/src/index.css

@@ -0,0 +1,3 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;

frontend/src/main.tsx

@@ -0,0 +1,10 @@
+import React from 'react';
+import ReactDOM from 'react-dom/client';
+import App from './App.tsx';
+import './index.css';
+
+ReactDOM.createRoot(document.getElementById('root')!).render(
+  <React.StrictMode>
+    <App />
+  </React.StrictMode>,
+);

frontend/src/pages/Analytics.tsx

@@ -0,0 +1,5 @@
+function Analytics() {
+  return <div>Analytics</div>;
+}
+
+export default Analytics;

frontend/src/pages/Home.tsx

@@ -0,0 +1,35 @@
+import { useAuthState, useSignInWithGoogle } from 'react-firebase-hooks/auth';
+import { Triangle } from 'react-loader-spinner';
+import Welcome from '../components/Welcome';
+import { auth } from '../firebase';
+import googleLogo from '../assets/google.svg';
+
+function Home() {
+  const [user, userLoading] = useAuthState(auth);
+  const [signInWithGoogle] = useSignInWithGoogle(auth);
+
+  return (
+    <main className="flex flex-col items-center mt-16 mx-4 text-center">
+      <h2 className="text-2xl font-medium mb-8">
+        Find the perfect movie to watch tonight.
+      </h2>
+      {userLoading && <Triangle />}
+      {!userLoading && !user && (
+        <>
+          <p className="mt-4">But first you need to log in:</p>
+          <button
+            type="button"
+            onClick={() => signInWithGoogle()}
+            className="mt-10 flex items-center gap-2 border rounded-full py-2 px-4 border-gray-400 hover:bg-gray-700 duration-100"
+          >
+            <img src={googleLogo} alt="Google" className="size-8" />
+            <span>Log in with Google</span>
+          </button>
+        </>
+      )}
+      {!!user && <Welcome />}
+    </main>
+  );
+}
+
+export default Home;

frontend/src/pages/Rate.tsx

@@ -0,0 +1,5 @@
+function Rate() {
+  return <div>Rate</div>;
+}
+
+export default Rate;

frontend/src/pages/Recommendations.tsx

@@ -0,0 +1,5 @@
+function Recommendations() {
+  return <div>Recommendations</div>;
+}
+
+export default Recommendations;

frontend/src/vite-env.d.ts

@@ -0,0 +1 @@
+/// <reference types="vite/client" />

frontend/tailwind.config.ts

@@ -0,0 +1,9 @@
+import type { Config } from 'tailwindcss';
+
+export default {
+  content: ['./index.html', './src/**/*.{js,ts,jsx,tsx}'],
+  theme: {
+    extend: {},
+  },
+  plugins: [],
+} satisfies Config;

frontend/tsconfig.json

@@ -0,0 +1,25 @@
+{
+  "compilerOptions": {
+    "target": "ES2020",
+    "useDefineForClassFields": true,
+    "lib": ["ES2020", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "noEmit": true,
+    "jsx": "react-jsx",
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "noFallthroughCasesInSwitch": true
+  },
+  "include": ["src"],
+  "references": [{ "path": "./tsconfig.node.json" }]
+}

frontend/tsconfig.node.json

@@ -0,0 +1,11 @@
+{
+  "compilerOptions": {
+    "composite": true,
+    "skipLibCheck": true,
+    "module": "ESNext",
+    "moduleResolution": "bundler",
+    "allowSyntheticDefaultImports": true,
+    "strict": true
+  },
+  "include": ["vite.config.ts"]
+}

frontend/vite.config.ts

@@ -0,0 +1,7 @@
+import { defineConfig } from 'vite';
+import react from '@vitejs/plugin-react-swc';
+
+// https://vitejs.dev/config/
+export default defineConfig({
+  plugins: [react()],
+});

movie_recommendations/movie_recommender.py

@@ -0,0 +1,119 @@
+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
+
+
+def get_director(x):
+    for i in x:
+        if i['job'] == 'Director':
+            return i['name']
+    return np.nan
+
+
+def get_list(x):
+    if isinstance(x, list):
+        names = [i['name'] for i in x]
+        if len(names) > 3:
+            names = names[:3]
+        return names
+    return []
+
+
+def clean_data(x):
+    if isinstance(x, list):
+        return [str.lower(i.replace(" ", "")) for i in x]
+    else:
+        if isinstance(x, str):
+            return str.lower(x.replace(" ", ""))
+        else:
+            return ''
+
+
+def create_soup(x):
+    return ' '.join(x['keywords']) + ' ' + ' '.join(x['cast']) + ' ' + x['director'] + ' ' + ' '.join(x['genres'])
+
+
+class MovieRecommender:
+    def __init__(self):
+        self.df = None
+        self.cosine_sim = None
+
+    def fit(self, credits_file, movies_file):
+        """
+        Fittuje AI do przekazanych danych
+        :param credits_file: csv z creditsami
+        :param movies_file: csv z filmami
+        :return: Nic
+        """
+        df1 = pd.read_csv(credits_file)
+        df2 = pd.read_csv(movies_file)
+        df1.columns = ['id', 'tittle', 'cast', 'crew']
+        df2 = df2.merge(df1, on='id')
+        df2['overview'] = df2['overview'].fillna('')
+        self.df = df2
+
+        features = ['cast', 'crew', 'keywords', 'genres']
+        for feature in features:
+            df2[feature] = df2[feature].apply(literal_eval)
+
+        df2['director'] = df2['crew'].apply(get_director)
+
+        features = ['cast', 'keywords', 'genres']
+        for feature in features:
+            df2[feature] = df2[feature].apply(get_list)
+
+        features = ['cast', 'keywords', 'director', 'genres']
+        for feature in features:
+            df2[feature] = df2[feature].apply(clean_data)
+
+        df2['soup'] = df2.apply(create_soup, axis=1)
+
+        count = CountVectorizer(stop_words='english')
+        count_matrix = count.fit_transform(df2['soup'])
+        self.cosine_sim = cosine_similarity(count_matrix, count_matrix)
+
+        self.df = df2.reset_index()
+
+    def _get_recommendations_one_input(self, movie_id):
+        """
+        Tworzy rekomendacje, bazując na jednym filmie
+        :param movie_id: id filmu, dla którego ma zrobić rekomendację
+        :return: Zwraca listę [movie_ids, similarity_scores] gdzie oba argumenty są np.array
+        """
+        indices = pd.Series(self.df.index, index=self.df['id']).drop_duplicates()
+        idx = indices[movie_id]
+        sim_scores = list(enumerate(self.cosine_sim[idx]))
+        sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
+        sim_scores = sim_scores[1:101]
+        movie_indices = [i[0] for i in sim_scores]
+        sim_scores = np.array([t[1] for t in sim_scores])
+        return [self.df['id'].iloc[movie_indices].values, sim_scores]
+
+    def get_recommendations(self, movie_ids: list) -> {}:
+        """
+        Tworzy listę rekomendacji bazującą na id podanych filmów
+        :param movie_ids: id filmów, na podstawie których ma wybrać rekomendowane filmy
+        :return: Zwraca dicta {movie_id: similarity_scores}
+        """
+        recommended_movies = {}
+        for movie_id in movie_ids:
+            recommended_ids, sim_scores = self._get_recommendations_one_input(movie_id)
+            for recommended_id, sim_score in zip(recommended_ids, sim_scores):
+                if recommended_id in movie_ids:
+                    continue
+
+                if recommended_movies.get(recommended_id) is None:
+                    recommended_movies[recommended_id] = sim_score / len(movie_ids)
+                else:
+                    recommended_movies[recommended_id] += sim_score / len(movie_ids)
+        return recommended_movies
+
+
+# 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)