import streamlit as st import pandas as pd import requests import pickle # Load the processed data and similarity matrix with open('movie_data.pkl', 'rb') as file: movies, cosine_sim = pickle.load(file) # Function to get movie recommendations def get_recommendations(title, cosine_sim=cosine_sim): idx = movies[movies['title'] == title].index[0] sim_scores = list(enumerate(cosine_sim[idx])) sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True) sim_scores = sim_scores[1:11] # Get top 10 similar movies movie_indices = [i[0] for i in sim_scores] return movies[['title', 'movie_id']].iloc[movie_indices] # Fetch movie poster from TMDB API def fetch_poster(movie_id): api_key = "d52d86933103ae578bbf057ec39d012e" # Your TMDB API key url = f'https://api.themoviedb.org/3/movie/{movie_id}?api_key={api_key}' response = requests.get(url) # Check if the request was successful if response.status_code != 200: return "https://via.placeholder.com/500x750?text=No+Image+Available" # Placeholder for errors data = response.json() # Safely access 'poster_path' poster_path = data.get('poster_path') if poster_path: return f"https://image.tmdb.org/t/p/w500{poster_path}" # Return placeholder if 'poster_path' is missing return "https://via.placeholder.com/500x750?text=No+Image+Available" # Streamlit UI st.title("Movie Recommendation System") selected_movie = st.selectbox("Select a movie:", movies['title'].values) if st.button('Recommend'): recommendations = get_recommendations(selected_movie) st.write("Top 10 recommended movies:") # Create a 2x5 grid layout for i in range(0, 10, 5): # Loop over rows (2 rows, 5 movies each) cols = st.columns(5) # Create 5 columns for each row for col, j in zip(cols, range(i, i+5)): if j < len(recommendations): movie_title = recommendations.iloc[j]['title'] movie_id = recommendations.iloc[j]['movie_id'] poster_url = fetch_poster(movie_id) with col: st.image(poster_url, width=130) st.write(movie_title)