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import streamlit as st
import pickle
import requests
import pandas as pd
st.set_page_config(page_title=None, page_icon=None, layout="wide", initial_sidebar_state="auto", menu_items=None)
movies = pickle.load(open("movies_list.pkl",'rb'))
similarity = pickle.load(open("similarity.pkl",'rb'))
def fetch_poster(movie_id):
url = "https://api.themoviedb.org/3/movie/{}?api_key=8735d1698444eb906584226ecb77307b&language=en-US".format(movie_id)
data = requests.get(url)
data = data.json()
poster_path = data['poster_path']
full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
return full_path
def recommend(movie):
index = movies[movies['title'] == movie].index[0]
distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
recommended_movie_names = []
recommended_movie_posters = []
for i in distances[1:6]:
# fetch the movie poster
movie_id = movies.iloc[i[0]].movie_id
recommended_movie_posters.append(fetch_poster(movie_id))
recommended_movie_names.append(movies.iloc[i[0]].title)
return recommended_movie_names,recommended_movie_posters
st.header('Movie Recommendation System (Content Based)')
st.text("A movie recommendation system, or a movie recommender system, is an ML-based approach to filtering or predicting the users' film preferences ")
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,recommended_movie_posters = recommend(selected_movie)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.text(recommended_movie_names[0])
st.image(recommended_movie_posters[0])
with col2:
st.text(recommended_movie_names[1])
st.image(recommended_movie_posters[1])
with col3:
st.text(recommended_movie_names[2])
st.image(recommended_movie_posters[2])
with col4:
st.text(recommended_movie_names[3])
st.image(recommended_movie_posters[3])
with col5:
st.text(recommended_movie_names[4])
st.image(recommended_movie_posters[4])
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