app.py

import streamlit as st
import requests
import pandas as pd
import pickle
import gdown
import re
import os
from Helpers import get_user_recommendation , train_model , get_user_recommendation_XGBoost , seen_movies


# Set page configuration
st.set_page_config(page_title="Movie Recommendation", page_icon="🎬", layout="wide")

st.markdown(
        """
        <style>
        body {
            background-image: url("https://repository-images.githubusercontent.com/275336521/20d38e00-6634-11eb-9d1f-6a5232d0f84f");
            color: #FFFFFF;
            font-family: 'Arial', sans-serif;
        }
        .stApp {
            background: rgba(0, 0, 0, 0.7);
            border-radius: 15px;
            padding: 20px;
        }
        .title {
            font-size: 3em;
            text-align: center;
            margin-bottom: 20px;
            font-weight: bold;
            color: #FF0000;
        }
        .section-title {
            font-size: 2em;
            margin-top: 30px;
            margin-bottom: 20px;
            text-align: center;
            color: #FFD700;
        }
        .recommendation {
            border: 1px solid #FFD700;
            padding: 20px;
            margin-bottom: 20px;
            border-radius: 15px;
            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.3);
            transition: transform 0.2s, box-shadow 0.2s;
            background-color: rgba(0, 0, 0, 0.8);
            overflow: hidden;
        }
        .recommendation:hover {
            transform: translateY(-10px);
            box-shadow: 0 8px 16px rgba(0, 0, 0, 0.5);
        }
        .recommendation img {
            width: 100%;
            height: 200px;
            object-fit: cover;
            border-radius: 10px;
            margin-bottom: 10px;
        }
        .movie-details-container {
            display: flex;
            align-items: center;
            margin-bottom: 20px;
        }
        .movie-details-container .movie-poster {
            flex: 0 0 auto;
            width: 30%;
            margin-right: 20px;
        }
        .movie-details-container .movie-poster img {
            width: 100%;
            border-radius: 10px;
        }
        .movie-details-container .movie-details {
            flex: 1 1 auto;
        }
        .movie-details-container .movie-details p {
            margin: 5px 0;
        }
        a {
            color: #FFD700;
            text-decoration: none;
        }
        a:hover {
            text-decoration: underline;
        }
        .stSidebar .element-container {
            background: rgba(0, 0, 0, 0.7);
            border-radius: 15px;
            padding: 15px;
        }
        .stSidebar .stButton button {
            background-color: #FFD700;
            color: #000;
            border: none;
            border-radius: 10px;
            padding: 10px;
            transition: background-color 0.2s, transform 0.2s;
        }
        .stSidebar .stButton button:hover {
            background-color: #FFAA00;
            transform: scale(1.05);
        }
        </style>
        """,
        unsafe_allow_html=True
    )



# CSV files URLs as raw data from GitHub repository
moviesCSV = "Data/movies.csv" 
ratingsCSV = "Data/ratings.csv"
linksCSV = "Data/links.csv"




# the folloing code is used to download the similarity matrix from google drive if not exist

# the folloing code is used to download the similarity matrix from google drive if not exist
file_url = 'https://drive.google.com/uc?id=1-1bpusE96_Hh0rUxU7YmBo6RiwYLQGVy'
DataBaseCSV = "https://drive.google.com/uc?id=11Soimwc1uKS5VGy_QROifwkdIzl8MZaV"
output_path = 'Models/similarity_matrix.pkl'
output_path_DataBase = 'Data/XGBoost_database.csv'


user_matrix_path = 'Models/User_based_matrix.pkl'

@st.cache_data
def download_model_from_google_drive(file_url, output_path):
    gdown.download(file_url, output_path, quiet=False)
    

# # Check if the file already exists
if not os.path.exists(output_path):
    print("Downloading the similarity matrix from Googlr Drive...")
    # change file permission
    # os.chmod('Models/', 0o777)
    download_model_from_google_drive(file_url, output_path)
    download_model_from_google_drive(DataBaseCSV, output_path_DataBase)

    print("Download completed......")

def display_user_history(history):
    st.write("Your Watch History:")
    container = st.container(height=300)
    for movie in history:
        with container:
            container.write(movie)


def get_user_history(dataBase, user_id):
    return dataBase[dataBase['userId'] == user_id]['title'].values



# Function to hash passwords
def hash_password(password):
    pass



# Login function
def login(username, password = None):
    if isinstance(username, int) and username > 0 and username < 610:
        return True
    return False



def fetch_movie_details(title, api_key_omdb="23f109b2", api_key_tmdb="b8c96e534866701532768a313b978c8b"):
    # First, try the OMDb API
    title = title[:-7]
    title = title.replace('+', '')
    url_omdb = f"http://www.omdbapi.com/?t={title}&apikey={api_key_omdb}"
    response_omdb = requests.get(url_omdb)
    movie = response_omdb.json()
    
    if movie['Response'] == 'True':
        return movie
    else:
        # If OMDb API doesn't find the movie, try the TMDb API
        url_tmdb_search = f"https://api.themoviedb.org/3/search/movie?api_key={api_key_tmdb}&query={title}"
        response_tmdb_search = requests.get(url_tmdb_search)
        search_results = response_tmdb_search.json()
        
        if search_results['total_results'] > 0:
            movie_id = search_results['results'][0]['id']
            url_tmdb_movie = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key={api_key_tmdb}"
            response_tmdb_movie = requests.get(url_tmdb_movie)
            tmdb_movie = response_tmdb_movie.json()
            
            # Convert TMDb response to a similar structure as OMDb response
            movie = {
                'Title': tmdb_movie['title'],
                'Year': tmdb_movie['release_date'].split('-')[0] if 'release_date' in tmdb_movie else 'N/A',
                'Rated': 'N/A',  # TMDb doesn't provide rating info in the same way
                'Genre': ', '.join([genre['name'] for genre in tmdb_movie['genres']]),
                'Plot': tmdb_movie['overview'],
                'Poster': f"https://image.tmdb.org/t/p/w500{tmdb_movie['poster_path']}" if 'poster_path' in tmdb_movie else '',
                'imdbRating': tmdb_movie['vote_average'],
                'imdbID': tmdb_movie['imdb_id'],
                'Response': 'True'
            }
            return movie
        else:
            return {'Response': 'False', 'Error': 'Movie not found'}

def display_movie_details(movie):
    if movie['Response'] == 'False':
        st.write(f"Movie not found: {movie['Error']}")
        return
    if movie['imdbRating'] == 'N/A':
        movie['imdbRating'] = 0
    imdb_rating = float(movie['imdbRating'])
    url = f"https://www.imdb.com/title/{movie['imdbID']}/"

    # Split the plot into lines based on . or ,
    plot_lines = re.split(r'[.,]', movie['Plot'])
    short_plot = '. '.join(plot_lines[:3]).strip() + '.'

    st.markdown(
        f"""
        <div style="
            background-color: #313131;
            border-radius: 20px;
            padding: 20px;
            margin: 25px 0;
            box-shadow: 0px 4px 12px rgba(0, 0, 0, 0.1);
        ">
            <div style="display: flex;">
                <div style="flex: 1;">
                <BR>
                    <a href="{url}" target="_blank" >
                    <img src="{movie['Poster']}" style="width: 100%; border-radius: 10px;" />
                    </a>
                </div>
                <div style="flex: 3; padding-left: 20px;">
                    <h3 style="margin: 0;" anchor="{url}">{movie['Title']}</h3>
                    <p style="color: gray;">
                        <b>Year:</b> {movie['Year']} Rated: {movie['Rated']} <br>
                        <b>Genre:</b> {movie['Genre'].replace(',', ' |')} <br>
                    </p>
                    <div>{short_plot}</div>
                    <div style="margin-top: 10px;">
                        <div style="background-color: #e0e0e0; border-radius: 5px; overflow: hidden;">
                            <div style="width: {imdb_rating * 10}%; background-color: #4caf50; padding: 5px 0; text-align: center; color: white;">
                                {imdb_rating}
                            </div>
                        </div>
                    </div>
                </div>
            </div>
        </div>
        """, unsafe_allow_html=True
    )






def print_movie_details(movie):
    st.markdown(
        f"""
                                <div class="recommendation">
                                    <div style="display: flex;">
                                        <div style="flex: 1;">
                                         <a href="https://www.imdb.com/title/tt{movie['imdb_id']:07d}/" target="_blank">
                                            <img src="{movie['poster_url']}" />
                                            </a>
                                        </div>
                                        <div style="flex: 3; padding-left: 20px;">
                                            <h4 style="margin: 0;">{' '.join(movie['title'].split(" ")[:-1])}</h4>
                                            <p style="color: gray;">
                                                <b>Year:</b> {movie['title'].split(" ")[-1]}<br>
                                                <b>Genre:</b> {', '.join(movie['genres'])}<br>
                                                <b>Number of Ratings:</b> {movie['num_ratings']}<br>
                                                <b>IMDb Rating: </b>{round(movie["imdb_rating"],1)}<br>
                                            </p>
                                            <div style="margin-top: 10px;">
                                                <div style="background-color: #e0e0e0; border-radius: 5px; overflow: hidden;">
                                                    <div style="width: {movie['avg_rating'] * 20}%; background-color: #4caf50; padding: 5px 0; text-align: center; color: white;">
                                                        {movie['avg_rating']}
                                                    </div>
                                                </div>
                                            </div>
                                        </div>
                                    </div>
                                </div>
                                """,
                                unsafe_allow_html=True
                            )



# Function to load data
@st.cache_data
def load_data():
    movies_df = pd.read_csv(moviesCSV)
    ratings_df = pd.read_csv(ratingsCSV)
    links_df = pd.read_csv(linksCSV)
    DataBase = pd.read_csv(output_path_DataBase)
    return movies_df, ratings_df, links_df , DataBase

# Function to load similarity matrix
@st.cache_data
def load_similarity_matrix(path):
    with open(path, 'rb') as f:
        similarity_df = pickle.load(f)
    return similarity_df

# Function to get movie details
def get_movie_details(movie_id, df_movies, df_ratings, df_links):
    try:
        imdb_id = df_links[df_links['movieId'] == movie_id]['imdbId'].values[0]
        tmdb_id = df_links[df_links['movieId'] == movie_id]['tmdbId'].values[0]

        movie_data = df_movies[df_movies['movieId'] == movie_id].iloc[0]
        genres = movie_data['genres'].split('|') if 'genres' in movie_data else []

        avg_rating = df_ratings[df_ratings['movieId'] == movie_id]['rating'].mean()
        num_ratings = df_ratings[df_ratings['movieId'] == movie_id].shape[0]

        api_key = 'b8c96e534866701532768a313b978c8b'
        response = requests.get(f'https://api.themoviedb.org/3/movie/{tmdb_id}?api_key={api_key}' )
        poster_url = response.json().get('poster_path', '')
        full_poster_url = f'https://image.tmdb.org/t/p/w500{poster_url}' if poster_url else ''
        imdb_rating = response.json().get('vote_average', 0)

        return {
            "title": movie_data['title'],
            "genres": genres,
            "avg_rating": round(avg_rating, 2),
            "num_ratings": num_ratings,
            "imdb_id": imdb_id,
            "tmdb_id": tmdb_id,
            "poster_url": full_poster_url,
            "imdb_rating": imdb_rating  
        }
    except Exception as e:
        st.error(f"Error fetching details for movie ID {movie_id}: {e}")
        return None

# Function to recommend movies
def recommend(movie, similarity_df, movies_df, ratings_df, links_df, k=5):
    try:
        index = movies_df[movies_df['title'] == movie].index[0]

        distances = sorted(list(enumerate(similarity_df.iloc[index])), reverse=True, key=lambda x: x[1])

        recommended_movies = []
        for i in distances[1:]:
            movie_id = movies_df.iloc[i[0]]['movieId']
            num_ratings = ratings_df[ratings_df['movieId'] == movie_id].shape[0]
            
            if num_ratings > 100:
                movie_details = get_movie_details(movie_id, movies_df, ratings_df, links_df)
                if movie_details:
                    recommended_movies.append(movie_details)
                if len(recommended_movies) == k:
                    break
        return recommended_movies
    except Exception as e:
        st.error(f"Error generating recommendations: {e}")
        return []

# Main app

def main():
    movies_df, ratings_df, links_df, DB_df = load_data()
    print("Data loaded successfully")
    print("Loading similarity matrix...")
    similarity_df = load_similarity_matrix(output_path)
    
    st.sidebar.title("Navigation")
    menu = ["Login", "Movie Similarity"]
    choice = st.sidebar.selectbox("Select an option", menu)
    
    if choice == "Login":
        num_cols = 2
        cols = st.columns(num_cols)
        with cols[0]:
            st.title("Movie Recommendations")
            st.write("Welcome to the Movie Recommendation App!")
            st.write("Please login to get personalized movie recommendations. username between (1 and 609)")
            # model selection
            C = st.selectbox("Select the model", ["User Similarity Matrix", "XGBoost"])
            
        # Login form
        st.sidebar.header("Login")
        username = st.sidebar.text_input("Username")
        if username:
            username = int(username)
        if st.sidebar.button("Login"):
            if login(username):
                st.sidebar.success("Login successful!")

                # Fetch user history
                with cols[1 % num_cols]:
                    user_history = get_user_history(DB_df, username)
                    display_user_history(user_history)
                
                if C == "User Similarity Matrix":
                    user_matrix = load_similarity_matrix(user_matrix_path)
                    recommendations = get_user_recommendation(DB_df, user_matrix, username)
                elif C == "XGBoost":
                    model = train_model(DB_df, username)
                    recommendations, user_seen_movies = get_user_recommendation_XGBoost(DB_df, model, username)
                else:
                    pass

                st.write(f"Recommendations for user number {username}:")
                num_cols = 2
                cols = st.columns(num_cols)
                for i, movie_title in enumerate(recommendations):
                    movie = fetch_movie_details(movie_title)
                    if movie['Response'] == 'True':
                        with cols[i % num_cols]:
                            display_movie_details(movie)
                    else:
                        st.write(f"Movie details for '{movie_title}' not found.")
            else:
                st.sidebar.error("Invalid email or password")

    elif choice == "Movie Similarity":
        num_cols = 2
        cols = st.columns(num_cols)
        
        # Movie similarity search
        with cols[0]:
            st.title("Find Similar Movies")
            selected_movie = st.selectbox("Type or select a movie from the dropdown", movies_df['title'].unique())
            k = st.slider("Select the number of recommendations (k)", min_value=1, max_value=50, value=5)
            button = st.button("Find Similar Movies")
        with cols[1]:
            st.title("Choosen Movie Details:")
            if selected_movie:
                movie = fetch_movie_details(selected_movie)
                if movie['Response'] == 'True':
                    display_movie_details(movie)
                else:
                    st.write(f"Movie details for '{selected_movie}' not found.")
        if button:
            st.write("The rating bar here is token from our dataset and it's between 0 and 5.")
            if selected_movie:
                recommendations = recommend(selected_movie, similarity_df, movies_df, ratings_df, links_df, k)
                if recommendations:
                    st.write(f"Similar movies to '{selected_movie}':")
                    num_cols = 2
                    cols = st.columns(num_cols)
                    for i, movie in enumerate(recommendations):
                        with cols[i % num_cols]:
                            print_movie_details(movie)
                else:
                    st.write("No recommendations found.")
            else:
                st.write("Please select a movie.")

if __name__ == "__main__":
    main()