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49 min read

WordPress Enlighter Plugin – Theme Options

Merhaba bu yazımda WordPress üzerinde kodları görselleştirmek için kullanılan bir plugin olan Enlighter – Customizable Syntax Highlighter‘ın stil seçeneklerini gösteriyorum.

Blog’um içerisinde kod satırlarını göstermek için kendi stilimi oluşturduğum ve kullanacağım son temayı da en son kısımda görebilirsiniz.

Englighter

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Godzilla

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Beyond

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Classic

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

MowTwo

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Eclipse

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Droide

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Minimal

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Atomic

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Rowhammer

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Bootsrap4

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Dracula

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Monokai

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])

Custom

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='🧠 Miull, Skills of tomorrow!', width=350)

with col3:
    st.markdown("[![Foo](https://img.icons8.com/material-outlined/96/000000/github.png)](https://github.com/oguzerdo)")

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])
Oğuz
Oğuz
http://www.oguzerdogan.com
Data Delivery Guy

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