Spaces:
Sleeping
Sleeping
File size: 3,082 Bytes
8af8121 36dc14c 8af8121 36dc14c 8af8121 36dc14c 8af8121 36dc14c 8af8121 36dc14c 8af8121 36dc14c 8af8121 36dc14c 8af8121 36dc14c 8af8121 36dc14c 8af8121 36dc14c |
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 |
import os
import gdown
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import re
import streamlit as st
# Google Drive file IDs
movies_file_id = "1HWlVK-nXM5JG4GfSDHyR-x8T1AlfQQYw"
ratings_file_id = "1V2s1rpu4Gfjbt8z2a1Xml9IJr5KSozK1"
# Download the files if they don't exist
def download_file_from_google_drive(file_id, output):
url = f"https://drive.google.com/uc?id={file_id}"
gdown.download(url, output, quiet=False)
if not os.path.exists("movies.csv"):
download_file_from_google_drive(movies_file_id, "movies.csv")
if not os.path.exists("ratings.csv"):
download_file_from_google_drive(ratings_file_id, "ratings.csv")
# Load the data
movies = pd.read_csv("movies.csv")
ratings = pd.read_csv("ratings.csv")
# Clean movie titles
def clean_title(title):
title = re.sub("[^a-zA-Z0-9 ]", "", title)
return title
movies["clean_title"] = movies["title"].apply(clean_title)
# Vectorize the titles
vectorizer = TfidfVectorizer(ngram_range=(1, 2))
tfidf = vectorizer.fit_transform(movies["clean_title"])
# Function to search for movies
def search(title):
title = clean_title(title)
query_vec = vectorizer.transform([title])
similarity = cosine_similarity(query_vec, tfidf).flatten()
indices = np.argpartition(similarity, -5)[-5:]
results = movies.iloc[indices].iloc[::-1]
return results
# Function to find similar movies
def find_similar_movies(movie_id):
similar_users = ratings[(ratings["movieId"] == movie_id) & (ratings["rating"] > 4)]["userId"].unique()
similar_user_recs = ratings[(ratings["userId"].isin(similar_users)) & (ratings["rating"] > 4)]["movieId"]
similar_user_recs = similar_user_recs.value_counts() / len(similar_users)
similar_user_recs = similar_user_recs[similar_user_recs > .10]
all_users = ratings[(ratings["movieId"].isin(similar_user_recs.index)) & (ratings["rating"] > 4)]
all_user_recs = all_users["movieId"].value_counts() / len(all_users["userId"].unique())
rec_percentages = pd.concat([similar_user_recs, all_user_recs], axis=1)
rec_percentages.columns = ["similar", "all"]
rec_percentages["score"] = rec_percentages["similar"] / rec_percentages["all"]
rec_percentages = rec_percentages.sort_values("score", ascending=False)
return rec_percentages.head(10).merge(movies, left_index=True, right_on="movieId")[["score", "title", "genres"]]
# Streamlit UI
st.title("Movie Recommendation System")
movie_name = st.text_input("Enter a movie title", "Toy Story")
if len(movie_name) > 5:
results = search(movie_name)
if not results.empty:
movie_id = results.iloc[0]["movieId"]
st.write(f"Top recommendations based on '{results.iloc[0]['title']}':")
recommendations = find_similar_movies(movie_id)
for index, row in recommendations.iterrows():
st.write(f"{row['title']} ({row['genres']}) - Score: {row['score']:.2f}")
else:
st.write("No movies found. Please try a different title.") |