| import requests | |
| import streamlit as st | |
| import pickle | |
| import pandas as pd | |
| import numpy as np | |
| st.title('Movie Recommendation System') | |
| movies_dict = pickle.load(open('movies_data.pkl', 'rb')) | |
| movies=pd.DataFrame(movies_dict) | |
| similarity=pickle.load(open('similarity.pkl', 'rb')) | |
| def recommend(movie): | |
| index=movies[movies['Title']==movie].index[0] | |
| distances=similarity[index] | |
| movie_list=sorted(list(enumerate(distances)),reverse=True,key=lambda x:x[1]) | |
| recommended_movie_names = [] | |
| for i in movie_list[1:6]: | |
| recommended_movie_names.append(movies.iloc[i[0]].Title) | |
| return recommended_movie_names | |
| movie_list = movies['Title'].values | |
| selected_movie = st.selectbox( | |
| "Type or select a movie from the dropdown", | |
| movie_list | |
| ) | |
| if st.button('Show Recommendation'): | |
| recommended_movie_names= recommend(selected_movie) | |
| col1, col2, col3, col4, col5 = st.columns(5) | |
| with col1: | |
| st.text(recommended_movie_names[0]) | |
| with col2: | |
| st.text(recommended_movie_names[1]) | |
| with col3: | |
| st.text(recommended_movie_names[2]) | |
| with col4: | |
| st.text(recommended_movie_names[3]) | |
| with col5: | |
| st.text(recommended_movie_names[4]) | |