From 3f8c21105026eddd83d4e2ac77b11acee3a152ee Mon Sep 17 00:00:00 2001 From: Evaclaire Wamitu Date: Wed, 31 Jul 2024 01:06:40 +0300 Subject: [PATCH] Add resources --- .ipynb_checkpoints/app-checkpoint.py | 129 +++++++++++++++++++++++++ pickled_models/.DS_Store | Bin 6148 -> 0 bytes pickled_models/collaborative_model.pkl | 3 - pickled_models/contentbased_model.pkl | 3 - pickled_models/hybrid_model.pkl | 3 - streamflix_recommendation_system.pptx | Bin 5344130 -> 0 bytes ~$reamflix_data_report.docx | Bin 0 -> 162 bytes 7 files changed, 129 insertions(+), 9 deletions(-) create mode 100644 .ipynb_checkpoints/app-checkpoint.py delete mode 100644 pickled_models/.DS_Store delete mode 100644 pickled_models/collaborative_model.pkl delete mode 100644 pickled_models/contentbased_model.pkl delete mode 100644 pickled_models/hybrid_model.pkl delete mode 100644 streamflix_recommendation_system.pptx create mode 100644 ~$reamflix_data_report.docx diff --git a/.ipynb_checkpoints/app-checkpoint.py b/.ipynb_checkpoints/app-checkpoint.py new file mode 100644 index 0000000..d523071 --- /dev/null +++ b/.ipynb_checkpoints/app-checkpoint.py @@ -0,0 +1,129 @@ +import streamlit as st +import pandas as pd +import numpy as np +from surprise import Dataset, Reader, SVD +import requests +import pickle + +# Set page config +st.set_page_config(page_title='STREAMFLIX', page_icon="🎬", layout='wide') + +# Load your data +@st.cache_data +def load_data(): + df = pd.read_csv('movies_data/movies.csv') + ratings = pd.read_csv('movies_data/ratings.csv') + return df, ratings + +# Train your model +@st.cache_resource +def train_model(ratings): + reader = Reader(rating_scale=(1, 5)) + data = Dataset.load_from_df(ratings[['userId_x', 'movieId', 'rating']], reader) + model = SVD() + model.fit(data.build_full_trainset()) + return model + +# Get recommendations +def get_recommendations(model, df, user_ratings, n=5, genre=None): + new_user_id = df['userId_x'].max() + 1 + movies_to_predict = df[~df['movieId'].isin([x[0] for x in user_ratings])]['movieId'].unique() + + predictions = [] + for movie_id in movies_to_predict: + predicted_rating = model.predict(new_user_id, movie_id).est + predictions.append((movie_id, predicted_rating)) + + recommendations = sorted(predictions, key=lambda x: x[1], reverse=True) + + if genre: + genre_recommendations = [ + (movie_id, rating) for movie_id, rating in recommendations + if genre.lower() in df[df['movieId'] == movie_id]['genres'].iloc[0].lower() + ] + return genre_recommendations[:n] + else: + return recommendations[:n] + +# Fetch movie poster +@st.cache_data +def fetch_poster(movie_id): + url = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key=your_api_key" + response = requests.get(url) + data = response.json() + return "https://image.tmdb.org/t/p/w500/" + data.get('poster_path', '') + +# Main app +def main(): + st.title("🎬 Streamflix: Hybrid Movie Recommendation System") + + # Load data + df, ratings = load_data() + model = train_model(ratings) + + # Sidebar + st.sidebar.title('Navigation') + page = st.sidebar.radio('Go to', ['Home', 'Get Recommendations', 'Search Movie']) + + if page == 'Home': + st.header('🔥 Top Trending Movies') + top_movies = df.sort_values('popularity', ascending=False).head(10) + + for _, movie in top_movies.iterrows(): + col1, col2 = st.columns([1, 3]) + with col1: + poster_url = fetch_poster(movie['id']) + st.image(poster_url, width=150) + with col2: + st.subheader(movie['title']) + st.write(f"Genres: {movie['genres']}") + st.write(f"Average Rating: {movie['vote_average']:.1f}/10") + if st.button(f"Rate {movie['title']}", key=f"rate_{movie['id']}"): + rating = st.slider('Your rating', 0.5, 5.0, 3.0, 0.5, key=f"slider_{movie['id']}") + st.write(f"You rated {movie['title']} {rating} stars!") + st.write(''---'') + + elif page == 'Get Recommendations': + st.header('🎯 Get Personalized Recommendations') + user_id = st.number_input('Please enter your user ID', min_value=1, step=1) + genres = st.multiselect('Select genres', df['genres'].explode().unique()) + + if st.button('Get Recommendations'): + recommendations = get_recommendations(user_id, model, df, ratings) + if genres: + recommendations = recommendations[recommendations['genres'].apply(lambda x: any(genre in x for genre in genres))] + + st.subheader('Your Recommended Movies:') + for _, movie in recommendations.iterrows(): + col1, col2 = st.columns([1, 3]) + with col1: + poster_url = fetch_poster(movie['id']) + st.image(poster_url, width=150) + with col2: + st.write(f'**{movie['title']}**') + st.write(f'Genres: {movie['genres']}') + st.write(f'Average Rating: {movie['vote_average']:.1f}/10') + if st.button(f'Watch Trailer for {movie['title']}', key=f'trailer_{movie['id']}'): + # You would need to implement a function to fetch and display the trailer + st.video('https://www.youtube.com/watch?v=dQw4w9WgXcQ') # Placeholder + st.write('---') + + elif page == 'Search Movies': + st.header('🔍 Search Movies') + search_term = st.text_input('Enter a movie title') + if search_term: + results = df[df['title'].str.contains(search_term, case=False)] + for _, movie in results.iterrows(): + col1, col2 = st.columns([1, 3]) + with col1: + poster_url = fetch_poster(movie['id']) + st.image(poster_url, width=150) + with col2: + st.subheader(movie['title']) + st.write(f'Genres: {movie['genres']}') + st.write(f'Average Rating: {movie['vote_average']:.1f}/10') + st.write(f'Overview: {movie['overview'][:200]}...') + st.write('---') + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/pickled_models/.DS_Store b/pickled_models/.DS_Store deleted file mode 100644 index ef023e7c0bc3d5ac48ed94d0f1ecbf57e825c468..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHK%Sr?>5Ul7P7Cg8o@t7|V{DWax51#!1b#?|6M@M`dyyefeD&2~Ma}pGhZs=5} zJKf0?OdbHz`nfv>Rsfb%VUjT;a?gf#e6TRp8a?jtgcg^0oGJ7dU0VAAwixTmKd-+> zhjZLA(xJzIBi!Q#!|5jLfa`DNvqVTR5DWwZ!9XzZqYUuO 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