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import streamlit as st
import pandas as pd
import requests
import pickle
import os
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Load the processed data and similarity matrix
with open('movie_data.pkl', 'rb') as file:
movies, cosine_sim = pickle.load(file)
# Function to get movie recommendations
def get_recommendations(title, cosine_sim=cosine_sim):
idx = movies[movies['title'] == title].index[0]
sim_scores = list(enumerate(cosine_sim[idx]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
sim_scores = sim_scores[1:11] # Get top 10 similar movies
movie_indices = [i[0] for i in sim_scores]
return movies[['title', 'movie_id']].iloc[movie_indices]
# Fetch movie poster from TMDB API
def fetch_poster(movie_id):
api_key = os.getenv("API_KEY") # Replace with your TMDB API key
url = f'https://api.themoviedb.org/3/movie/{movie_id}?api_key={api_key}'
response = requests.get(url)
data = response.json()
poster_path = data['poster_path']
full_path = f"https://image.tmdb.org/t/p/w500{poster_path}"
return full_path
# Streamlit UI
st.title("Movie Recommendation System")
selected_movie = st.selectbox("Select a movie:", movies['title'].values)
if st.button('Recommend'):
recommendations = get_recommendations(selected_movie)
st.write("Top 10 recommended movies:")
# Create a 2x5 grid layout
for i in range(0, 10, 5): # Loop over rows (2 rows, 5 movies each)
cols = st.columns(5) # Create 5 columns for each row
for col, j in zip(cols, range(i, i+5)):
if j < len(recommendations):
movie_title = recommendations.iloc[j]['title']
movie_id = recommendations.iloc[j]['movie_id']
poster_url = fetch_poster(movie_id)
with col:
st.image(poster_url, width=130)
st.write(movie_title)
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