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| import asyncio | |
| import os | |
| import pickle | |
| import aiohttp | |
| import pandas as pd | |
| import streamlit as st | |
| from dotenv import load_dotenv | |
| # Load the environment variable from .env file | |
| load_dotenv() | |
| # Configure page | |
| st.set_page_config( | |
| page_title="Movie Recommender System", | |
| page_icon="🎬🍿", | |
| layout="wide" | |
| ) | |
| # Cache the data loading | |
| def load_data(): | |
| with open("movies.pkl", "rb") as file: | |
| movies = pickle.load(file) | |
| movies = pd.DataFrame(movies) | |
| with open("similarity.pkl", "rb") as file: | |
| similarity = pickle.load(file) | |
| return movies, similarity | |
| def my_api_key(): | |
| api_key = os.environ["MOVIES_API_KEY"] | |
| if not api_key: | |
| st.error("API Key not found. Please check your .env file.") | |
| st.stop() | |
| return api_key | |
| async def fetch_poster(session, movie_id): | |
| url = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key={my_api_key()}&language=en-US" | |
| try: | |
| async with session.get(url) as response: | |
| data = await response.json() | |
| poster_path = data.get("poster_path") | |
| if poster_path: | |
| return "https://image.tmdb.org/t/p/w500/" + poster_path | |
| return "https://via.placeholder.com/500x750?text=No+Poster+Available" | |
| except Exception as e: | |
| st.error(f"Error fetching poster for movie ID {movie_id}: {str(e)}") | |
| return "https://via.placeholder.com/500x750?text=Error+Loading+Poster" | |
| async def get_recommendation(movie, movies, similarity): | |
| movie_index = movies[movies["title"] == movie].index[0] | |
| distances = similarity[movie_index] | |
| movies_list = sorted((enumerate(distances)), key=lambda x: x[1], reverse=True) | |
| async with aiohttp.ClientSession() as session: | |
| tasks = [] | |
| recommended_movies_names = [] | |
| for i in movies_list: | |
| movie_id = movies.loc[i[0]].movie_id | |
| recommended_movies_names.append(movies.loc[i[0]].title) | |
| tasks.append(fetch_poster(session, movie_id)) | |
| recommended_movies_posters = await asyncio.gather(*tasks) | |
| return recommended_movies_names, recommended_movies_posters | |
| def main(): | |
| st.header("🎬🍿 Movie Recommender System") | |
| # Load Data | |
| with st.spinner("Loading movie data..."): | |
| movies, similarity = load_data() | |
| print(movies) | |
| # Create a sidebar for additional Information | |
| with st.sidebar: | |
| st.header("About") | |
| st.write(""" | |
| This movie recommender system suggests similar movies based on your selection. | |
| It uses content-based filtering to make recommendations. | |
| """) | |
| st.metric("Total Movies", len(movies)) | |
| # Main Content | |
| movies_list = movies["title"].values | |
| selected_movie_name = st.selectbox("Select a movie you like:", | |
| movies_list, | |
| help="Choose a movie to get similar recommendation") | |
| if st.button("Get Recommendations"): | |
| with st.spinner("Fetching recommendations..."): | |
| # Run async recommendation function | |
| recommended_movies_names, recommended_movies_posters = asyncio.run(get_recommendation(selected_movie_name, movies, similarity)) | |
| # Display recommendations in a grid | |
| st.subheader("Recommended Movies") | |
| cols = st.columns(5) | |
| for index, (col, name, poster) in enumerate(zip(cols, | |
| recommended_movies_names, recommended_movies_posters)): | |
| with col: | |
| st.image(poster, caption=name, use_container_width=True) | |
| st.button("More Info", key=f"info_{index}", help=f"Click here to learn more") | |
| if __name__ == "__main__": | |
| main() | |