Commit

Pages/About.py

@@ -1,28 +0,0 @@
-import streamlit as st
-
-
-# About page
-def about_page():
-    style_for_page = """
-    <style>
-    div.css-nahz7x.e16nr0p34>p {
-        font-family: Poppins, sans-serif;
-        font-size: 1.07rem;
-    }
-    </style>
-    """
-    st.markdown(style_for_page, unsafe_allow_html=True)
-    st.title("About")
-    st.divider()
-    st.subheader(
-        "This is a content based recommender system that recommends animes similar to the animes you like."
-    )
-    st.write("\n")
-    st.write("\n")
-    st.write(
-        "This Anime Recommender App is made by [Harshit Singh](https://Harsh502s.github.io/). :ninja:"
-    )
-
-
-if __name__ == "__main__":
-    about_page()

Pages/Recommender App.py

@@ -1,149 +0,0 @@
-import streamlit as st
-import pandas as pd
-import pickle
-
-
-# Importing the dataset
-@st.cache_data
-def load_data():
-    try:
-        anime_data = pd.read_csv(r"rec_data.csv")
-    except:
-        st.error("Dataset Not Found")
-    return anime_data
-
-
-# Uncomment this if you want to load the model
-# @st.cache_resource
-# def load_model():
-#     try:
-#         similarity = pickle.load(open(r"similarity.pkl", "rb"))
-#     except:
-#         st.error("Model Not Found")
-#     return similarity
-
-
-# similarity = load_model()
-anime_data = load_data()
-
-
-# Fetching the poster and url of the anime
-def fetch_anime_url(anime_id):
-    url = anime_data[anime_data["anime_id"] == anime_id].urls.values[0]
-    return url
-
-
-def fetch_poster(anime_id):
-    poster = anime_data[anime_data["anime_id"] == anime_id].poster.values[0]
-    return poster
-
-
-# Recommender System
-def recommend(anime):
-    index = anime_data[anime_data["title"] == anime].index[0]
-    distances = sorted(
-        list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1]
-    )
-    recommended_anime_names = []
-    recommended_anime_posters = []
-    recommended_anime_urls = []
-
-    for i in distances[1:9]:
-        # fetch the anime poster
-        anime_id = anime_data.iloc[i[0]].anime_id
-        recommended_anime_posters.append(fetch_poster(anime_id))
-        recommended_anime_names.append(anime_data.iloc[i[0]].title)
-        recommended_anime_urls.append(fetch_anime_url(anime_id))
-
-    return recommended_anime_names, recommended_anime_posters, recommended_anime_urls
-
-
-# Recommender Page
-def recommender_page():
-    style_for_page = """
-    <style>
-    div.css-1v0mbdj.etr89bj1>img {
-    width: 100%;
-    height: 100%;
-    overflow: hidden;
-    box-shadow: 0 0 0 1px rgba(0,0,0,.1);
-    border-radius: 1rem;
-    }
-    </style>
-    """
-    st.markdown(style_for_page, unsafe_allow_html=True)
-
-    st.title("Anime Recommendation System")
-
-    anime_list = anime_data["title"].tolist()
-    anime_list.sort()
-    anime_list.insert(0, "Top 8 Animes")
-    anime_select = st.selectbox("Select an Anime", anime_list)
-
-    if st.button("Recommendation"):
-        if anime_select == "Top 8 Animes":
-            top8 = anime_data.sort_values("score", ascending=False).head(8)
-            col1, col2, col3, col4 = st.columns(4)
-            with col1:
-                st.write(f"[{top8.iloc[0].title}]({top8.iloc[0].anime_url})")
-                st.image(top8.iloc[0].poster)
-            with col2:
-                st.write(f"[{top8.iloc[1].title}]({top8.iloc[1].anime_url})")
-                st.image(top8.iloc[1].poster)
-            with col3:
-                st.write(f"[{top8.iloc[2].title}]({top8.iloc[2].anime_url})")
-                st.image(top8.iloc[2].poster)
-            with col4:
-                st.write(f"[{top8.iloc[3].title}]({top8.iloc[3].anime_url})")
-                st.image(top8.iloc[3].poster)
-
-            col5, col6, col7, col8 = st.columns(4)
-            with col5:
-                st.write(f"[{top8.iloc[4].title}]({top8.iloc[4].anime_url})")
-                st.image(top8.iloc[4].poster)
-            with col6:
-                st.write(f"[{top8.iloc[5].title}]({top8.iloc[5].anime_url})")
-                st.image(top8.iloc[5].poster)
-            with col7:
-                st.write(f"[{top8.iloc[6].title}]({top8.iloc[6].anime_url})")
-                st.image(top8.iloc[6].poster)
-            with col8:
-                st.write(f"[{top8.iloc[7].title}]({top8.iloc[7].anime_url})")
-                st.image(top8.iloc[7].poster)
-        else:
-            (
-                recommended_anime_names,
-                recommended_anime_posters,
-                recommended_anime_urls,
-            ) = recommend(anime_select)
-            col1, col2, col3, col4 = st.columns(4)
-            with col1:
-                st.write(f"[{recommended_anime_names[0]}]({recommended_anime_urls[0]})")
-                st.image(recommended_anime_posters[0])
-            with col2:
-                st.write(f"[{recommended_anime_names[1]}]({recommended_anime_urls[1]})")
-                st.image(recommended_anime_posters[1])
-            with col3:
-                st.write(f"[{recommended_anime_names[2]}]({recommended_anime_urls[2]})")
-                st.image(recommended_anime_posters[2])
-            with col4:
-                st.write(f"[{recommended_anime_names[3]}]({recommended_anime_urls[3]})")
-                st.image(recommended_anime_posters[3])
-
-            col5, col6, col7, col8 = st.columns(4)
-            with col5:
-                st.write(f"[{recommended_anime_names[4]}]({recommended_anime_urls[4]})")
-                st.image(recommended_anime_posters[4])
-            with col6:
-                st.write(f"[{recommended_anime_names[5]}]({recommended_anime_urls[5]})")
-                st.image(recommended_anime_posters[5])
-            with col7:
-                st.write(f"[{recommended_anime_names[6]}]({recommended_anime_urls[6]})")
-                st.image(recommended_anime_posters[6])
-            with col8:
-                st.write(f"[{recommended_anime_names[7]}]({recommended_anime_urls[7]})")
-                st.image(recommended_anime_posters[7])
-
-
-if __name__ == "__main__":
-    recommender_page()

app.py

@@ -1,72 +0,0 @@
-import streamlit as st
-from st_pages import Page, show_pages
-from PIL import Image
-
-# Configuring Pages
-
-show_pages(
-    [
-        Page(r"app.py", "Homepage", "🏠"),
-        Page(r"Pages/Recommender App.py", "Anime Recommender", "📺"),
-        Page(r"Pages/About.py", "About", "👋"),
-    ]
-)
-
-
-# Make the page full width
-im = Image.open(r"ninja.png")
-st.set_page_config(
-    page_title="Anime Recommender App",
-    page_icon=im,
-    layout="wide",
-    initial_sidebar_state="expanded",
-    menu_items={"About": "This Anime Recommender App is made by Harshit Singh."},
-)
-
-
-# Home Page
-def home_page():
-    style_for_page = """
-    <style>
-    div.css-1v0mbdj.etr89bj1>img {
-    width: 100%;
-    height: 100%;
-    box-shadow: 0 0 0 1px rgba(0,0,0,.1);
-    border-radius: 5rem;
-    padding: 4rem;
-    justify-content: left;}
-
-    div.css-k7vsyb.e16nr0p31>h1 {
-    font-family: Poppins, sans-serif;
-    }
-
-    div.css-14xtw13.e8zbici0 {
-        margin-right: 2rem;
-        scale: 1.15;
-    }
-
-    div.css-nahz7x.e16nr0p34>p {
-        font-family: Poppins, sans-serif;
-        font-size: 1.05rem;
-    }
-    </style>
-    """
-    st.markdown(style_for_page, unsafe_allow_html=True)
-
-    st.title("Welcome to Anime Recommender! :ninja:")
-    st.subheader("Discover Your Next Favorite Anime")
-
-    # Add unique content to the home page
-    st.write(
-        "Explore a world of anime and find personalized recommendations based on your anime preferences."
-    )
-    img = Image.open(r"animes.jpg")
-    st.image(img, use_column_width=True, caption="Anime Characters")
-    st.write(
-        "Get started by selecting your favorite anime and let the recommendation system do the rest!"
-    )
-
-
-# Web Application
-if __name__ == "__main__":
-    home_page()

scrape_anime.py

@@ -1,150 +0,0 @@
-import requests
-from bs4 import BeautifulSoup
-import pandas as pd
-from concurrent.futures import ThreadPoolExecutor
-import requests
-from tqdm import tqdm
-import warnings as w
-
-w.filterwarnings("ignore")
-
-
-no_of_page = int(
-    BeautifulSoup(requests.get("https://aniwatch.to/az-list").content, "lxml")
-    .find("nav", attrs={"aria-label": "Page navigation"})
-    .find_all("li")[-1]
-    .find("a")["href"]
-    .split("=")[1]
-)
-landing_page_url = "https://aniwatch.to/az-list"
-page_urls = [
-    f"{landing_page_url}/?page={i}" if i != 1 else landing_page_url
-    for i in range(1, no_of_page + 1)
-]
-
-# Scraping the data from all the pages
-
-anime_urls = []
-
-for url in tqdm(page_urls):
-    page = requests.get(url)
-    soup = BeautifulSoup(page.content, "html.parser")
-
-    # Getting the url for the anime page
-
-    for anime in soup.find_all("div", class_="film-poster"):
-        anime = anime.find("a")["href"]
-        page = "https://aniwatch.to" + anime
-        anime_urls.append(page)
-        pass
-    pass
-
-anime_url = pd.DataFrame(anime_urls, columns=["anime_url"])
-anime_url.to_csv("anime_url.csv", index=False)
-
-
-# def process_url(url):
-#     soup = BeautifulSoup(requests.get(url).content, "html.parser")
-
-#     anime_poster = soup.find("div", class_="film-poster").find("img")["src"]
-
-#     # Getting the name of the anime
-
-#     anime_title = soup.find("h2", class_="film-name dynamic-name").text
-
-#     # Getting the overview of the anime
-
-#     anime_overview = anime_overview = (
-#         soup.find("div", class_="item item-title w-hide")
-#         .find("div", class_="text")
-#         .text
-#     )
-
-#     # Creating an object of the div containing all the details of the anime
-
-#     soup = soup.find("div", class_="anisc-info")
-
-#     # Extract MAL Score
-#     mal_score_element = soup.find("span", {"class": "item-head"}, text="MAL Score:")
-#     anime_mal_score = (
-#         mal_score_element.find_next_sibling("span", {"class": "name"}).text.strip()
-#         if mal_score_element
-#         else "NA"
-#     )
-
-#     # Extract Studios
-#     studios_element = soup.find("span", {"class": "item-head"}, text="Studios:")
-#     anime_studio = (
-#         studios_element.find_next("a", {"class": "name"}).text.strip()
-#         if studios_element
-#         else "NA"
-#     )
-
-#     # Extract Producers
-#     producers_element = soup.find("span", {"class": "item-head"}, text="Producers:")
-#     anime_producer = (
-#         [
-#             producer.text.strip()
-#             for producer in producers_element.find_next_siblings("a")
-#         ]
-#         if producers_element
-#         else ["NA"]
-#     )
-
-#     # Extract Genres
-#     genres_element = soup.find("span", {"class": "item-head"}, text="Genres:")
-#     anime_genres = (
-#         [genre.text.strip() for genre in genres_element.find_next_siblings("a")]
-#         if genres_element
-#         else ["NA"]
-#     )
-
-#     return (
-#         anime_poster,
-#         anime_title,
-#         anime_overview,
-#         anime_mal_score,
-#         anime_studio,
-#         anime_producer,
-#         anime_genres,
-#     )
-
-
-# def create_df_parallel(anime_urls, num_threads=4):
-#     anime_poster_list = []
-#     anime_title_list = []
-#     anime_overview_list = []
-#     anime_mal_score_list = []
-#     anime_studio_list = []
-#     anime_producer_list = []
-#     anime_genres_list = []
-
-#     with ThreadPoolExecutor(max_workers=num_threads) as executor:
-#         results = executor.map(process_url, anime_urls)
-
-#         for result in results:
-#             anime_poster_list.append(result[0])
-#             anime_title_list.append(result[1])
-#             anime_overview_list.append(result[2])
-#             anime_mal_score_list.append(result[3])
-#             anime_studio_list.append(result[4])
-#             anime_producer_list.append(result[5])
-#             anime_genres_list.append(result[6])
-
-#     anime_dict = {
-#         "anime_poster": anime_poster_list,
-#         "anime_title": anime_title_list,
-#         "anime_overview": anime_overview_list,
-#         "anime_mal_score": anime_mal_score_list,
-#         "anime_studio": anime_studio_list,
-#         "anime_producer": anime_producer_list,
-#         "anime_genres": anime_genres_list,
-#     }
-
-#     anime_df = pd.DataFrame(anime_dict)
-#     return anime_df
-
-
-# anime_df = create_df_parallel(anime_urls)
-# anime_df.head()
-# anime_df.to_csv("anime_data.csv", index=False)