| import streamlit as st |
| import pandas as pd |
| import requests |
| import pickle |
|
|
| |
| with open('movie_data.pkl', 'rb') as file: |
| movies, cosine_sim = pickle.load(file) |
|
|
| |
| 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] |
| movie_indices = [i[0] for i in sim_scores] |
| return movies[['title', 'movie_id']].iloc[movie_indices] |
|
|
| |
| def fetch_poster(movie_id): |
| api_key = "d52d86933103ae578bbf057ec39d012e" |
| url = f'https://api.themoviedb.org/3/movie/{movie_id}?api_key={api_key}' |
| response = requests.get(url) |
| |
| |
| if response.status_code != 200: |
| return "https://via.placeholder.com/500x750?text=No+Image+Available" |
|
|
| data = response.json() |
| |
| |
| poster_path = data.get('poster_path') |
| if poster_path: |
| return f"https://image.tmdb.org/t/p/w500{poster_path}" |
| |
| |
| return "https://via.placeholder.com/500x750?text=No+Image+Available" |
|
|
| |
| 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:") |
|
|
| |
| for i in range(0, 10, 5): |
| cols = st.columns(5) |
| 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) |
|
|