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import streamlit as st
import pickle
import pandas as pd
import requests #to fetch images
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,verify=False)
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] # fetching the index of movie
distances = similarity[movie_index]
movie_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
recommended_movies=[]
recommended_movies_posters=[]
for i in movie_list:
movie_id = movies.iloc[i[0]].movie_id
recommended_movies.append(movies.iloc[i[0]].title)
recommended_movies_posters.append(fetch_poster(movie_id))
return recommended_movies,recommended_movies_posters
movies_dict=pickle.load(open('movie_dict.pkl','rb')) #read binary
movies=pd.DataFrame(movies_dict)
similarity=pickle.load(open('similarity.pkl','rb')) #read binary
st.title('Movie Nutz - A recommender')
selected_movie_name = st.selectbox(
'Type or select a movie from the dropdown menu of your choice:',
movies['title'].values
)
if st.button('Show Recommendation'):
recommended_movie_names,recommended_movie_posters = recommend(selected_movie_name)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.text(recommended_movie_names[0])
st.image(recommended_movie_posters[0])
with col2:
st.text(recommended_movie_names[1])
st.image(recommended_movie_posters[1])
with col3:
st.text(recommended_movie_names[2])
st.image(recommended_movie_posters[2])
with col4:
st.text(recommended_movie_names[3])
st.image(recommended_movie_posters[3])
with col5:
st.text(recommended_movie_names[4])
st.image(recommended_movie_posters[4])
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