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# main code
import streamlit as st
import pickle
import requests
movies = pickle.load(open("movies_list.pkl", 'rb'))
similarity = pickle.load(open("similarity.pkl",'rb'))
movies_list = movies['title'].values
st.header("Movie Recommender System")
selectvalue = st.selectbox("Select movie from dropdown", movies_list)
def fetch_poster(movie_id):
try:
url = "https://api.themoviedb.org/3/movie/{}?api_key=8cfe8dff1a6fff88fe27b573ee65c035&language=en-US".format(movie_id)
# Disablinb SSL verification for development
data = requests.get(url, verify=False)
data = data.json()
poster_path = data['poster_path']
if poster_path:
full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
return full_path
else:
st.warning(f"No poster found for movie ID {movie_id}")
return None
except Exception as e:
st.error(f"Error fetching poster: {str(e)}")
return None
def recommend(movie):
index = movies[movies['title']==movie].index[0]
distance = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda vector:vector[1])
recommend_movie = []
recommend_poster = []
for i in distance[1:6]:
movies_id = movies.iloc[i[0]].id
recommend_movie.append(movies.iloc[i[0]].title)
poster = fetch_poster(movies_id)
recommend_poster.append(poster)
return recommend_movie, recommend_poster
if st.button("Show Recommend"):
movie_name, movie_poster = recommend(selectvalue)
cols = st.columns(5)
for idx, (col, name, poster) in enumerate(zip(cols, movie_name, movie_poster)):
with col:
st.text(name)
if poster: # showing image if url exists
st.image(poster)
else:
st.write("No poster available") |