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Recom.py
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Recom.py
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import streamlit as st
import pickle
import requests
import pandas as pd
def fetch_poster(id):
response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=8fe08ca3bb37030ce26bc59bfbecf201&language=en-US'.format(id))
data = response.json()
#st.write(data)
#st.write(response)
return "https://image.tmdb.org/t/p/w500/" + data['poster_path']
def recom(m):
m_index = movies[movies['title'] == m].index[0]
dist = similarity[m_index]
l = sorted(list(enumerate(dist)), reverse=True, key=lambda x: x[1])[1:6]
recom_movies = []
recom_posters = []
for i in l:
m_id = movies.iloc[i[0]].movie_id
recom_movies.append(movies.iloc[i[0]].title)
recom_posters.append(fetch_poster(m_id))
return recom_movies, recom_posters
movie_dict = pickle.load(open('movies_dict.pkl','rb'))
movies = pd.DataFrame(movie_dict)
#movies_list = pickle.load(open('movies.pkl', 'rb'))
#movies = movies_list['title'].values
similarity = pickle.load(open('similarity.pkl','rb'))
st.title('Movie Recommend System')
selected_movie = st.selectbox( 'What to recommend :', movies['title'].values)
if st.button('Show Recommendation'):
names, posters = recom(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])