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57ba871 0e94fa4 93fd6a2 57ba871 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | import pickle
import numpy as np
import pandas as pd
import streamlit as st
def recommend(movie):
index = movies[movies["title"]== movie].index[0]
distances = sorted(list(enumerate(similarity[index])), reverse = True, key = lambda x: x[1])
recommended_movies_names =[]
#recommended_movies_poster =[]
for i in distances[1:6]:
movie_id = movies.iloc[i[0]].movie_id
# recommended_movies_poster.append(fetch_poster(movie_id))
recommended_movies_names.append(movies.iloc[i[0]].title)
return recommended_movies_names
st.header("Movie Recommendation System Project ")
movies = pickle.load(open(r"movielist.pkl",'rb'))
similarity = pickle.load(open(r"similarity.pkl",'rb'))
movie_list = movies['title'].values
selected_movie = st.selectbox(
"Type or select a movie to get recommendations",
movie_list
)
if st.button("Show Recommendation"):
recommended_movies_names = recommend(selected_movie)
st.text(recommended_movies_names[0])
st.text(recommended_movies_names[1])
st.text(recommended_movies_names[2])
st.text(recommended_movies_names[3])
st.text(recommended_movies_names[4])
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