about_me.py

import streamlit as st

#===============================
import pandas as pd
from pathlib import Path
import os
import sys

# Define the data directory outside the function
data_dir = Path(__file__).parent / 'data'

# 1. Use st.cache_data to run this function only once
@st.cache_data
def load_ratings_data():
    chunks = []
    # Using nullable integers ('Int32') is correct
    dtype_ratings = {'userId': 'Int32', 'movieId': 'Int32', 'rating': 'float32'}

    # Add a spinner to show the user the file is loading
    with st.spinner('Loading ratings data (please bear with me)...'):
        for chunk in pd.read_csv(
            data_dir / 'ratings.csv', 
            dtype=dtype_ratings, 
            chunksize=1_000_000
        ):
            # Drop rows where 'userId' or 'movieId' is NaN (as requested)
            chunk = chunk.dropna(subset=['userId', 'movieId'])
            chunks.append(chunk)

    # Concatenate all chunks into the final dataframe
    ratings = pd.concat(chunks, ignore_index=True)
    return ratings

# 2. Call the function to load and cache the data. 
#    This DataFrame is now available globally or can be passed to functions.
ratings_df = load_ratings_data() 
st.session_state['ratings_df'] = ratings_df # Use session_state to share it easily

#==============================



# Basic page styling
st.markdown(
    """
    <style>
        .stApp {
            background-color: white;
            color: #116A91;
        }
        .title {
            text-align: center;
            color: #116A91;
            font-size: 36px;
            margin-bottom: 10px;
        }
        .subtitle {
            text-align: center;
            color: #444444;
            font-size: 18px;
            margin-bottom: 30px;
        }
        .section {
            background-color: #f9f9f9;
            padding: 20px;
            margin-bottom: 20px;
            border-radius: 6px;
        }
        .header {
            color: #116A91;
            font-size: 20px;
            margin-bottom: 10px;
        }
        .content {
            color: #333333;
            font-size: 16px;
            line-height: 1.6;
        }
    </style>
    """, unsafe_allow_html=True
)

# Page Title and Subtitle
st.markdown("<div class='title'>Movie Recommender System</div>", unsafe_allow_html=True)
st.markdown("<div class='subtitle'>Discover movies you'll love using different recommendation techniques 🎬</div>", unsafe_allow_html=True)

# Creator Section
st.markdown("""
<div class='section'>
    <div class='header'>👤 About the Creator</div>
    <div class='content'>
        <strong>SJ</strong> is the creator of this Movie Recommender System.
        <br><br>
        Currently working as a <strong>Product Manager</strong>, SJ specializes in leading cross-functional teams to build and launch user-centric digital products. With a strong foundation in both business strategy and data, SJ is passionate about solving real-world problems through thoughtful product design.
        <br><br>
        Prior to becoming a Product Manager, SJ held roles as:
        <ul>
            <li><strong>Data Analyst</strong> – Experienced in SQL, Excel, Tableau, Python, Consumer Bureau data, and Microfinance analytics</li>
            <li><strong>Data Engineer</strong> – Worked with IBM DataStage, Informatica, Ataccama, and Collibra</li>
        </ul>
        This diverse background is what inspired SJ to build tools like this — combining the power of data with simple, intuitive products.
    </div>
</div>
""", unsafe_allow_html=True)

# Purpose Section
st.markdown("""
<div class='section'>
    <div class='header'>📌 Purpose of This App</div>
    <div class='content'>
        This Movie Recommender System helps users discover movies they might enjoy based on different recommendation techniques:
        <ul>
            <li>🔥 Popularity Based Filtering – Recommend trending movies</li>
            <li>🎬 Content-Based Filtering – Recommend movies similar to your favorites</li>
            <li>👥 Collaborative User-Based – Recommend movies liked by users similar to you</li>
            <li>🛍️ Collaborative Item-Based – Recommend movies similar to ones you've rated highly</li>
        </ul>
        The goal is to provide a simple, interactive way to explore movie recommendations tailored to your preferences.
    </div>
</div>
""", unsafe_allow_html=True)

# Technology Section
st.markdown("""
<div class='section'>
    <div class='header'>🛠️ Technology Used</div>
    <div class='content'>
        The application is developed using <strong>Streamlit</strong>, a lightweight and efficient Python framework for building data apps.
        <br><br>
        Key Python libraries used include <strong>Pandas</strong>, <strong>NumPy</strong>, <strong>Scikit-Learn</strong>, and <strong>Scipy</strong> for data processing and recommendation algorithms.
    </div>
</div>
""", unsafe_allow_html=True)

# Features Section
st.markdown("""
<div class='section'>
    <div class='header'>🔍 What You Can Do with It</div>
    <div class='content'>
        With this app, users can:
        <ul>
            <li>Explore movie recommendations based on popularity, content similarity, or collaborative filtering</li>
            <li>View detailed recommendations for a specific user</li>
            <li>Filter movies based on ratings and user activity</li>
            <li>Understand hidden patterns in user preferences using latent features</li>
        </ul>
        The system helps both casual viewers and movie enthusiasts find movies they are likely to enjoy.
    </div>
</div>
""", unsafe_allow_html=True)

 # Footer / Call-to-action
st.markdown("""
<div class='section'>
    <div class='header'>🎯 Ready to Discover Movies?</div>
    <div class='content'>
        Choose a recommendation technique from the sidebar and start exploring movies tailored to your tastes.
        <br><br>
        Enjoy discovering new favorites!
    </div>
</div>
""", unsafe_allow_html=True)