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f2ee240 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | import asyncio
import os
import pickle
import aiohttp
import pandas as pd
import streamlit as st
from dotenv import load_dotenv
# Load the environment variable from .env file
load_dotenv()
# Configure page
st.set_page_config(
page_title="Movie Recommender System",
page_icon="🎬🍿",
layout="wide"
)
# Cache the data loading
@st.cache_data
def load_data():
with open("movies.pkl", "rb") as file:
movies = pickle.load(file)
movies = pd.DataFrame(movies)
with open("similarity.pkl", "rb") as file:
similarity = pickle.load(file)
return movies, similarity
def my_api_key():
api_key = os.environ["MOVIES_API_KEY"]
if not api_key:
st.error("API Key not found. Please check your .env file.")
st.stop()
return api_key
async def fetch_poster(session, movie_id):
url = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key={my_api_key()}&language=en-US"
try:
async with session.get(url) as response:
data = await response.json()
poster_path = data.get("poster_path")
if poster_path:
return "https://image.tmdb.org/t/p/w500/" + poster_path
return "https://via.placeholder.com/500x750?text=No+Poster+Available"
except Exception as e:
st.error(f"Error fetching poster for movie ID {movie_id}: {str(e)}")
return "https://via.placeholder.com/500x750?text=Error+Loading+Poster"
async def get_recommendation(movie, movies, similarity):
movie_index = movies[movies["title"] == movie].index[0]
distances = similarity[movie_index]
movies_list = sorted((enumerate(distances)), key=lambda x: x[1], reverse=True)
async with aiohttp.ClientSession() as session:
tasks = []
recommended_movies_names = []
for i in movies_list:
movie_id = movies.loc[i[0]].movie_id
recommended_movies_names.append(movies.loc[i[0]].title)
tasks.append(fetch_poster(session, movie_id))
recommended_movies_posters = await asyncio.gather(*tasks)
return recommended_movies_names, recommended_movies_posters
def main():
st.header("🎬🍿 Movie Recommender System")
# Load Data
with st.spinner("Loading movie data..."):
movies, similarity = load_data()
print(movies)
# Create a sidebar for additional Information
with st.sidebar:
st.header("About")
st.write("""
This movie recommender system suggests similar movies based on your selection.
It uses content-based filtering to make recommendations.
""")
st.metric("Total Movies", len(movies))
# Main Content
movies_list = movies["title"].values
selected_movie_name = st.selectbox("Select a movie you like:",
movies_list,
help="Choose a movie to get similar recommendation")
if st.button("Get Recommendations"):
with st.spinner("Fetching recommendations..."):
# Run async recommendation function
recommended_movies_names, recommended_movies_posters = asyncio.run(get_recommendation(selected_movie_name, movies, similarity))
# Display recommendations in a grid
st.subheader("Recommended Movies")
cols = st.columns(5)
for index, (col, name, poster) in enumerate(zip(cols,
recommended_movies_names, recommended_movies_posters)):
with col:
st.image(poster, caption=name, use_container_width=True)
st.button("More Info", key=f"info_{index}", help=f"Click here to learn more")
if __name__ == "__main__":
main()
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