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app.py
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app.py
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import pickle
import streamlit as st
import pandas as pd
import requests
def fetch_poster(movie_id):
reponse = requests.get(
"//use your API to fetch image from tmdb".format(
movie_id))
data = reponse.json()
return "https://image.tmdb.org/t/p/w500/" + data['poster_path']
def recommend(movie):
movie_index = movies[movies['title'] == movie].index[0]
distances = similarity[movie_index]
movies_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
recommended_movie_names = []
recommended_movie_posters = []
for i in movies_list:
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 Recommender System')
movies_dict = pickle.load(open('movies_dicct.pkl', 'rb'))
movies = pd.DataFrame(movies_dict)
similarity = pickle.load(open('similarity.pkl', 'rb'))
movie_list = movies['title'].values
selected_movie = st.selectbox(
"Type or select a movie from the dropdown",
movie_list
)
if st.button('Show Recommendation'):
names, posters = recommend(selected_movie)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.text(names[0])
st.image(posters[0])
with col2:
st.text(names[1])
st.image(posters[1])
with col3:
st.text(names[2])
st.image(posters[2])
with col4:
st.text(names[3])
st.image(posters[3])
with col5:
st.text(names[4])
st.image(posters[4])