app.py

"""
Indian Content Recommender
A content-based recommendation app for Indian movies, web series and
documentaries across languages, genres and OTT platforms.
"""

import streamlit as st
from recommender import load_data, ContentRecommender

DATA_PATH = "data/indian_content.csv"

st.set_page_config(page_title="Indian Content Recommender", page_icon="🎬", layout="centered")


@st.cache_resource
def get_recommender():
    df = load_data(DATA_PATH)
    return ContentRecommender(df), df


recommender, df = get_recommender()

st.title("🎬 Indian Content Recommender")
st.caption(
    "Content-based recommendations across Hindi, Tamil, Telugu, Malayalam, "
    "Kannada and Marathi movies, series and documentaries."
)

with st.sidebar:
    st.header("Filters")
    content_type = st.selectbox("Content type", ["All"] + sorted(df["type"].unique()))
    top_n = st.slider("Number of recommendations", 3, 15, 8)
    st.markdown("---")
    st.markdown(
        "**Dataset:** starter set of 50 titles. Swap in a larger dataset "
        "(e.g. Kaggle's *Indian Movies* or *OTT Platforms* datasets) by "
        "replacing `data/indian_content.csv` — same column format."
    )

query = st.text_input("Enter a movie, series or documentary you like", placeholder="e.g. Drishyam")

if query:
    matched_title, results = recommender.recommend(query, top_n=top_n, content_type=content_type)

    if matched_title is None:
        st.warning("No close match found. Try another title.")
    else:
        if matched_title.lower() != query.strip().lower():
            st.info(f"Showing recommendations based on closest match: **{matched_title}**")

        if results.empty:
            st.warning("No recommendations match the selected filter.")
        else:
            st.subheader(f"Because you liked {matched_title}")
            for _, row in results.iterrows():
                with st.container(border=True):
                    col1, col2 = st.columns([3, 1])
                    with col1:
                        st.markdown(f"**{row['title']}**  ·  {row['type']}  ·  {row['language']}")
                        st.caption(row["genre"])
                        st.write(row["description"])
                    with col2:
                        st.metric("Match", f"{row['match_score']}%")
                        st.caption(row.get("platform", ""))
else:
    st.markdown("### Browse the catalog")
    st.dataframe(
        df[["title", "type", "language", "genre", "platform", "year"]],
        use_container_width=True,
        hide_index=True,
    )