Create app.py

7474794 verified about 2 years ago

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

# Function to fetch movie poster from API
def fetch_poster(movie_id):
    response = requests.get(
        f'https://api.themoviedb.org/3/movie/{movie_id}?api_key=9b955595d7ffef24254513d6a66503fe&language=en-US'
    )
    data = response.json()
    return "http://image.tmdb.org/t/p/w500" + data['poster_path']

# Function to recommend movies based on selected movie
def recommend(movie):
    movie_index = movies[movies['title'] == movie].index[0]
    distances = similarity[movie_index]
    movie_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
    
    recommended_movies = []
    recommended_posters = []
    
    for i in movie_list:
        movie_id = movies.iloc[i[0]].movie_id
        recommended_movies.append(movies.iloc[i[0]].title)
        recommended_posters.append(fetch_poster(movie_id))
    
    return recommended_movies, recommended_posters

# Load data (movies and similarity matrix)
movies_dict = pickle.load(open('movie_dict2.pkl', 'rb'))
movies = pd.DataFrame(movies_dict)
similarity = pickle.load(open('similarity.pkl', 'rb'))

# App title
st.title('🎬 Movie Recommender System')

# Movie selection section
st.subheader("Select a movie to get recommendations:")
selected_movie_name = st.selectbox('Choose a movie:', movies['title'].values)

# Recommendation button and display
if st.button('Recommend'):
    recommended_names, recommended_posters = recommend(selected_movie_name)
    
    # Displaying recommendations in a more visually appealing way
    st.subheader(f"Movies recommended based on '{selected_movie_name}':")
    cols = st.columns(5)  # Dividing the page into 5 columns
    for idx, col in enumerate(cols):
        with col:
            st.text(recommended_names[idx])
            st.image(recommended_posters[idx])
            # Adding a clickable link that redirects to Google search for the movie
            search_url = f"https://www.google.com/search?q={recommended_names[idx].replace(' ', '+')}+movie"
            st.markdown(f"[Search '{recommended_names[idx]}' on Google]({search_url})", unsafe_allow_html=True)

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

	# Function to fetch movie poster from API
	def fetch_poster(movie_id):
	response = requests.get(
	f'https://api.themoviedb.org/3/movie/{movie_id}?api_key=9b955595d7ffef24254513d6a66503fe&language=en-US'
	)
	data = response.json()
	return "http://image.tmdb.org/t/p/w500" + data['poster_path']

	# Function to recommend movies based on selected movie
	def recommend(movie):
	movie_index = movies[movies['title'] == movie].index[0]
	distances = similarity[movie_index]
	movie_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]

	recommended_movies = []
	recommended_posters = []

	for i in movie_list:
	movie_id = movies.iloc[i[0]].movie_id
	recommended_movies.append(movies.iloc[i[0]].title)
	recommended_posters.append(fetch_poster(movie_id))

	return recommended_movies, recommended_posters

	# Load data (movies and similarity matrix)
	movies_dict = pickle.load(open('movie_dict2.pkl', 'rb'))
	movies = pd.DataFrame(movies_dict)
	similarity = pickle.load(open('similarity.pkl', 'rb'))

	# App title
	st.title('🎬 Movie Recommender System')

	# Movie selection section
	st.subheader("Select a movie to get recommendations:")
	selected_movie_name = st.selectbox('Choose a movie:', movies['title'].values)

	# Recommendation button and display
	if st.button('Recommend'):
	recommended_names, recommended_posters = recommend(selected_movie_name)

	# Displaying recommendations in a more visually appealing way
	st.subheader(f"Movies recommended based on '{selected_movie_name}':")
	cols = st.columns(5) # Dividing the page into 5 columns
	for idx, col in enumerate(cols):
	with col:
	st.text(recommended_names[idx])
	st.image(recommended_posters[idx])
	# Adding a clickable link that redirects to Google search for the movie
	search_url = f"https://www.google.com/search?q={recommended_names[idx].replace(' ', '+')}+movie"
	st.markdown(f"[Search '{recommended_names[idx]}' on Google]({search_url})", unsafe_allow_html=True)