app.py

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

def fetch_poster(movie_id):
    response = requests.get(f'https://api.themoviedb.org/3/movie/{movie_id}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US')
    data = response.json()
    poster_path = data['poster_path']
    return f"https://image.tmdb.org/t/p/w500/{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)
        recommended_movies_posters.append(fetch_poster(movie_id))
    return recommended_movies, recommended_movies_posters

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

st.header("CineCloud - Find Movies You'll Love!")

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

if st.button('Recommend'):
    names, posters = recommend(selected_movie_name)

    cols = st.columns(5)
    for col, name, poster in zip(cols, names, posters):
        with col:
            st.markdown(f"<div style='text-align: center;'><img src='{poster}' style='width:100%;'><p style='margin-top: 10px;'><strong>{name}</strong></p></div>", unsafe_allow_html=True)