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app.py
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import streamlit as st
import pickle
import pandas as pd
import requests
def fetch_poster(movie_id):
response=requests.get('https://api.themoviedb.org/3/movie/{}?api_key=b06f5ab31c81a66c57e1ab13303f0cc8'.format(movie_id))
data=response.json()
return "https://image.tmdb.org/t/p/w200/"+data['poster_path']
def fetch_vote_average(movie_id):
response=requests.get('https://api.themoviedb.org/3/movie/{}?api_key=b06f5ab31c81a66c57e1ab13303f0cc8'.format(movie_id))
data=response.json()
return data['vote_average']
st.title("BEST Movies For You!")
movies_list=pickle.load(open('movies.pkl','rb'))
movies=pd.DataFrame(movies_list)
selected_movie_name=st.selectbox('Come on Lets Enjoy ! Tell your Movie',movies['title'].values)
st.image(fetch_poster(movies.iloc[movies[movies['title']==selected_movie_name].index[0]].id))
st.text("Rating:"+str(round(fetch_vote_average(movies.iloc[movies[movies['title']==selected_movie_name].index[0]].id)*10,1))+"%")
similarity=pickle.load(open('similarity.pkl','rb'))
def recommend(movie):
movie_index=movies[movies['title']==movie].index[0]
distances=similarity[movie_index]
movies_listk=sorted(list(enumerate(distances)),reverse=True,key=lambda x:x[1])[1:6]
l=[]
l1=[]
l2=[]
for i in movies_listk:
movie_id=movies.iloc[i[0]].id
l.append(movies.iloc[i[0]].title)
l1.append(fetch_poster(movie_id))
l2.append(fetch_vote_average(movie_id))
return l,l1,l2
if (st.button('Recommend')):
names,posters,popularity=recommend(selected_movie_name)
col1,col2,col3,col4,col5=st.columns(5)
with col1:
st.text(names[0])
st.image(posters[0])
st.text("Rating:"+str(round(popularity[0]*10,1))+"%")
with col2:
st.text(names[1])
st.image(posters[1])
st.text("Rating:"+str(round(popularity[1]*10,1))+"%")
with col3:
st.text(names[2])
st.image(posters[2])
st.text("Rating:"+str(round(popularity[2]*10,1))+"%")
with col4:
st.text(names[3])
st.image(posters[3])
st.text("Rating:"+str(round(popularity[3]*10,1))+"%")
with col5:
st.text(names[4])
st.image(posters[4])
st.text("Rating:"+str(round(popularity[4]*10,1))+"%")