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app.py
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app.py
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import streamlit as st
import pickle
import requests
def fetch_poster(movie_id):
url = "https://api.themoviedb.org/3/movie/{}?api_key=c7ec19ffdd3279641fb606d19ceb9bb1&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
movies = pickle.load(open("movies_list.pkl", 'rb'))
similarity = pickle.load(open("similarity.pkl", 'rb'))
movies_list=movies['title'].values
st.header("Movie Recommender System")
import streamlit.components.v1 as components
imageCarouselComponent = components.declare_component("image-carousel-component", path="frontend/public")
imageUrls = [
fetch_poster(1632),
fetch_poster(299536),
fetch_poster(17455),
fetch_poster(2830),
fetch_poster(429422),
fetch_poster(9722),
fetch_poster(13972),
fetch_poster(240),
fetch_poster(155),
fetch_poster(598),
fetch_poster(914),
fetch_poster(255709),
fetch_poster(572154)
]
imageCarouselComponent(imageUrls=imageUrls, height=200)
selectvalue=st.selectbox("Select movie from dropdown", movies_list)
def recommend(movie):
index=movies[movies['title']==movie].index[0]
distance = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda vector:vector[1])
recommend_movie=[]
recommend_poster=[]
for i in distance[1:6]:
movies_id=movies.iloc[i[0]].id
recommend_movie.append(movies.iloc[i[0]].title)
recommend_poster.append(fetch_poster(movies_id))
return recommend_movie, recommend_poster
if st.button("Show Recommend"):
movie_name, movie_poster = recommend(selectvalue)
col1,col2,col3,col4,col5=st.columns(5)
with col1:
st.text(movie_name[0])
st.image(movie_poster[0])
with col2:
st.text(movie_name[1])
st.image(movie_poster[1])
with col3:
st.text(movie_name[2])
st.image(movie_poster[2])
with col4:
st.text(movie_name[3])
st.image(movie_poster[3])
with col5:
st.text(movie_name[4])
st.image(movie_poster[4])