Commit

Pages/About.py

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+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

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+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
+
+
+anime_data = load_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()
+
+
+# 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

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+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()

similarity.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fe2a6562734859d7b4cd2faa02b48cc75b30f08466bcdeedcf43f0cb41ea0428
+size 328397355