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app.py
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app.py
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import pandas as pd
import streamlit as st
import pickle
import requests
st.set_page_config(
page_title="Your App Title",
page_icon=":smiley:",
layout="wide",
initial_sidebar_state="expanded"
#bgcolor="blue", # You can use any valid CSS color here
)
st.markdown('<style>body{background-color: Blue;}</style>',unsafe_allow_html=True)
def fetch_poster(movie_id):
url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&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
def recommend(movie):
movie_index = movies[movies['title'] == movie].index[0]
distances = similarity[movie_index]
movie_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
L=[]
poster=[]
for i in movie_list:
movie_id=movies.iloc[i[0]].movie_id
L.append(movies.iloc[i[0]].title)
poster.append(fetch_poster(movie_id))
return L,poster
movie_dict=pickle.load(open('movies_dict.pkl','rb'))
movies=pd.DataFrame(movie_dict)
similarity=pickle.load(open('similarity.pkl','rb'))
st.title('Movie Recommender System')
option=st.selectbox('Select a movie',movies['title'].values)
if st.button('Recommend'):
recommended_movie_names, corresponding_posters = recommend(option)
# Displaying movie name and poster in 6 columns
column_1, column_2, column_3, column_4, column_5, column_6 = st.columns(6)
with column_1:
st.text(recommended_movie_names[0])
st.image(corresponding_posters[0])
with column_2:
st.text(recommended_movie_names[1])
st.image(corresponding_posters[1])
with column_3:
st.text(recommended_movie_names[2])
st.image(corresponding_posters[2])
with column_4:
st.text(recommended_movie_names[3])
st.image(corresponding_posters[3])
with column_5:
st.text(recommended_movie_names[4])
st.image(corresponding_posters[4])