app.py

import streamlit as st
import pickle
import pandas as pd
import requests
def fetch_poster(movie_id):
    respone=requests.get("https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US".format(movie_id))
    data=respone.json()
    return "https://image.tmdb.org/t/p/w500/"+data['poster_path']
def recommend(movie):
    movie_index=movies[movies['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_posters=[]
    for i in movies_list:
        movie_id=movies.iloc[i[0]].movie_id
        recommended_movies.append(movies.iloc[i[0]].title)
        # fetch posters from API 
        recommended_movies_posters.append(fetch_poster(movie_id))
    
    return recommended_movies,recommended_movies_posters

movies_dict=pickle.load(open("movies_dict.pkl","rb"))
movies=pd.DataFrame(movies_dict)
similarity=pickle.load(open("similarity1.pkl","rb"))
st.title("Movie Recommender System")
selected_movie_name = st.selectbox(
"Which movie to recommend?",
movies['title'].values)
if st.button("Recommend"):
    names,posters=recommend(selected_movie_name)
    col1,col2,col3,col4,col5=st.columns(5)
    with col1:
        st.text(names[0])
        st.image(posters[0])
    with col2:
        st.text(names[1])
        st.image(posters[1])
    with col3:
        st.text(names[2])
        st.image(posters[2])
    with col4:
        st.text(names[3])
        st.image(posters[3])
    with col5:
        st.text(names[4])
        st.image(posters[4])
    for i in names,posters:
        st.write(i)