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import pandas as pd
import streamlit as st
import pickle
import requests
def fetch_image(movie_id):
response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US'.format(movie_id))
data = response.json()
return "https://image.tmdb.org/t/p/w500/" + data['poster_path']
def recommand(movie):
movie_index = movies[movies['title'] == movie].index[0]
distences = similarity[movie_index]
movies_list = sorted(list(enumerate(distences)),reverse=True,key = lambda x : x[1])[1:6]
recommanded_movie = []
recommaned_poster=[]
for i in movies_list:
movie_id = movies.iloc[i[0]].movie_id
recommanded_movie.append(movies.iloc[i[0]].title)
recommaned_poster.append(fetch_image(movie_id)) # Fetch poster from API
return recommanded_movie,recommaned_poster
movies_dict = pickle.load(open('movie_dict.pkl','rb'))
movies = pd.DataFrame(movies_dict)
similarity = pickle.load(open('similarity.pkl','rb'))
st.title('Movie Recommender System')
selected_movie = st.selectbox(
'What is your taste in movie ? ',
movies['title'].values)
st.write('You selected:', selected_movie)
if st.button('Recommend movie'):
name,poster =recommand(selected_movie)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.text(name[0])
st.image(poster[0])
with col2:
st.text(name[1])
st.image(poster[1])
with col3:
st.text(name[2])
st.image(poster[2])
with col4:
st.text(name[3])
st.image(poster[3])
with col5:
st.text(name[4])
st.image(poster[4])
st.header('', divider='rainbow')
st.markdown('''
Developed by KARTAVYA MASTER :8ball:
''')
link='PORTFOLIO : [CLICK ME](https://mydawjbhdas.my.canva.site/aiwithkartavya)'
st.markdown(link,unsafe_allow_html=True) |