File size: 2,020 Bytes
9188ee5 |
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 49 50 51 52 53 54 55 56 57 58 59 60 61 |
import streamlit as st
import pickle
import requests
# from sklearn.feature_extraction.text import CountVectorizer
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):
index = movies[movies['title'] == movie].index[0]
distances = sorted(list(enumerate(similarity[index])),reverse=True,key = lambda x: x[1])
recommend_Movies=[]
recommended_movie_posters = []
for i in distances[1:6]:
movie_id = movies.iloc[i[0]].movie_id
recommended_movie_posters.append(fetch_poster(movie_id))
recommend_Movies.append(movies.iloc[i[0]].title)
return recommended_movie_posters,recommend_Movies
movies=pickle.load(open('movie_list.pkl','rb'))
similarity=pickle.load(open("similarity.pkl",'rb'))
# cv = CountVectorizer(max_features=10000,stop_words='english')
# from sklearn.metrics.pairwise import cosine_similarity
# vector = cv.fit_transform(movies['tags']).toarray()
# similarity = cosine_similarity(vector)
st.title("Movie Recommandor System")
Movies_Name = st.selectbox('How would you like to recomend movies?',movies['title'].values)
if st.button('recommend'):
recommended_movie_posters,recommend_Movies=recommend(Movies_Name)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.text(recommend_Movies[0])
st.image(recommended_movie_posters[0])
with col2:
st.text(recommend_Movies[1])
st.image(recommended_movie_posters[1])
with col3:
st.text(recommend_Movies[2])
st.image(recommended_movie_posters[2])
with col4:
st.text(recommend_Movies[3])
st.image(recommended_movie_posters[3])
with col5:
st.text(recommend_Movies[4])
st.image(recommended_movie_posters[4])
|