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#Import required Libraries
import streamlit as st
import pickle
import pandas as pd
import requests
#Hide Streamlit Menu and Default Footer
hide_menu_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_menu_style, unsafe_allow_html=True)
#Fetch posters from TMDb Database
def fetch_poster(movie_id):
response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=[TMDb-API-KEY]&language=en-US'.format(movie_id))
data = response.json()
return "https://image.tmdb.org/t/p/w500/" + data['poster_path']
#Recommend movies based on content
def recommend(movie):
movie_index = movies[movies['original_title'] == movie].index[0]
distances = similarity[movie_index]
movies_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
recommended_movies = []
recommended_movies_poster = []
for i in movies_list:
movie_id = movies.iloc[i[0]].id
recommended_movies.append(movies.iloc[i[0]].original_title)
recommended_movies_poster.append(fetch_poster(movie_id))
return recommended_movies,recommended_movies_poster
#Frontend Design for StreamLit WebApp Sidebar
st.sidebar.subheader(" ")
st.sidebar.subheader("Technology:")
st.sidebar.text("Natural Language Processing")
st.sidebar.subheader("Developed By")
st.sidebar.text("Ashwin Raj, Student at UCEK")
movies_dict = pickle.load(open('pickle/movie_dict.pkl','rb'))
movies = pd.DataFrame(movies_dict)
similarity = pickle.load(open('pickle/similarity.pkl','rb'))
#Frontend Hero Section
st.title("Movie Recommender System")
selected_movie_name = st.selectbox(
'Select a movie to recommend',
movies['original_title'].values)
#Output Recommendations with Posters
if st.button('Recommend'):
name, posters = recommend(selected_movie_name)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.text(name[0])
st.image(posters[0])
with col2:
st.text(name[1])
st.image(posters[1])
with col3:
st.text(name[2])
st.image(posters[2])
with col4:
st.text(name[3])
st.image(posters[3])
with col5:
st.text(name[4])
st.image(posters[4])
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