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app.py
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import pickle
import streamlit as st
import requests
import os
from PIL import Image
from dotenv import load_dotenv
import pandas as pd
logo = Image.open('images/logo.png')
frame = Image.open('images/frame.png')
load_dotenv()
API = os.getenv('MOVIE_API')
st.set_page_config(
page_title="Film Tavsiye Sistemi",
page_icon="🤖",
layout="wide",
initial_sidebar_state="expanded",
)
def get_movie_poster(movie_id):
url = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key={API}&language=en-US"
data = requests.get(url)
data = data.json()
poster_path = data['poster_path']
full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
return full_path
def content_based_recommender(title, cosine_sim, dataframe):
# index'leri olusturma
indices = pd.Series(dataframe.index, index=dataframe['original_title'])
indices = indices[~indices.index.duplicated(keep='last')]
# title'ın index'ini yakalama
movie_index = indices[title]
# title'a gore benzerlik skorlarını hesapalama
similarity_scores = pd.DataFrame(cosine_sim[movie_index], columns=["score"])
# kendisi haric ilk 10 filmi getirme
movie_indices = similarity_scores.sort_values("score", ascending=False)[1:6].index
movie_names = []
movie_posters = []
for i in movie_indices[0:6]:
# fetch the movie poster
movie_id = dataframe.iloc[i].id
movie_posters.append(get_movie_poster(movie_id))
movie_names.append(movies.iloc[i]['original_title'])
return movie_names, movie_posters
col1, col2, col3 = st.columns(3)
with col1:
st.image(frame, width=125)
with col2:
st.image(logo, caption='🧠 Skills of tomorrow!', width=350)
with col3:
st.markdown("[](https://github.com/oguzerdo/Movie-Recommendation-System)")
st.header('Film Tavsiye Sistemi')
movies = pickle.load(open('model/movie_list.pkl', 'rb'))
cosine_sim = pickle.load(open('model/cosine_sim.pkl', 'rb'))
movie_list = movies['original_title'].values # Tüm film isimlerini alma
selected_movie = st.selectbox("Açılır menüden film seçiniz.", movie_list) # Açılır menüde film isimlerini gösterme.
if st.button('Tavsiyeleri gör'):
movie_names, movie_posters = content_based_recommender(selected_movie, cosine_sim, movies)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.text(movie_names[0])
st.image(movie_posters[0])
with col2:
st.text(movie_names[1])
st.image(movie_posters[1])
with col3:
st.text(movie_names[2])
st.image(movie_posters[2])
with col4:
st.text(movie_names[3])
st.image(movie_posters[3])
with col5:
st.text(movie_names[4])
st.image(movie_posters[4])
if st.button('Şansımı denemek istiyorum'):
random_movie = movies['original_title'].sample(1).values[0]
st.subheader(random_movie)
movie_names, movie_posters = content_based_recommender(random_movie, cosine_sim, movies)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.text(movie_names[0])
st.image(movie_posters[0])
with col2:
st.text(movie_names[1])
st.image(movie_posters[1])
with col3:
st.text(movie_names[2])
st.image(movie_posters[2])
with col4:
st.text(movie_names[3])
st.image(movie_posters[3])
with col5:
st.text(movie_names[4])
st.image(movie_posters[4])