app.py

import streamlit as st
import pickle
import pandas as pd
import requests

def fetch_poster(movie_id):
 response=requests.get('https://api.themoviedb.org/3/movie/{}?api_key=e091268b493d63975342491246748399&language=en-US'.format(movie_id))
 data=response.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_poster_path=[]
 st.write('Recommended Movies:')
 for i in movies_list:
  movie_id=movies.iloc[i[0]].movie_id
  #fetch poster from API
  path=fetch_poster(movie_id)
  recommended_movies_poster_path.append(path)
  recommended_movies.append(movies.iloc[i[0]].title)

 return recommended_movies, recommended_movies_poster_path

movies_list=pickle.load(open('movie_dict.pkl','rb'))
movies=pd.DataFrame(movies_list)

similarity=pickle.load(open('similarity.pkl','rb'))

st.title('Movie Recommender System')

selected_movie_name = st.selectbox(
'Select a movie:',
 movies['title'].values)

if st.button('Recommend'):
 st.write('You selected:', selected_movie_name)
 st.write('\n')
 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])