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f1c9005 | 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 37 38 39 40 41 42 43 44 45 46 47 48 | # streamlit run app.py use for run this web
import streamlit as st
import pickle
import pandas as pd
import requests
def fechposter(movies_id):
response = requests.get(f"https://api.themoviedb.org/3/movie/{movies_id}?api_key=175454cec5e81a00151981dfb22a69d4&language=en-US")
data=response.json()
# st.write(data)
return 'https://image.tmdb.org/t/p/w500/'+data['poster_path']
def recommend(movie):
m_index=movies[movies['title']==movie].index[0]
distance=similer[m_index]
movies_list=sorted(list(enumerate(distance)), reverse=True, key=lambda x : x[1])[1:6]
recommended=[]
recom_movie_post=[]
for m in movies_list:
recommended.append(movies.iloc[m[0]].title)
recom_movie_post.append(fechposter(movies.iloc[m[0]].movie_id))
return recommended,recom_movie_post
movies_l=pickle.load(open('movie_dict.pkl','rb'))
similer=pickle.load(open('similer_m.pkl','rb'))
movies=pd.DataFrame(movies_l)
st.title("w_one_n.cm")
selected_movie = st.selectbox(
'Select the movie',
movies['title'].values)
if st.button('Recommend'):
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])
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