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import streamlit as st
from streamlit_lottie import st_lottie
import requests
import importlib.util
import os

# Function to load Lottie animation from a URL
def load_lottieurl(url: str):
    """Fetch Lottie animation JSON from a URL."""
    try:
        response = requests.get(url)
        if response.status_code == 200:
            return response.json()
    except requests.exceptions.RequestException:
        return None

# CSS Styling for adaptive light/dark modes
st.markdown("""
    <style>
    :root {
        --background-color: #f8f9fa;
        --text-color: #212529;
        --primary-color: #007acc;
        --secondary-color: #005b96;
        --card-bg: #ffffff;
        --text-muted: #6c757d;
    }
    @media (prefers-color-scheme: dark) {
        :root {
            --background-color: #0e1117;
            --text-color: #e5e5e5;
            --primary-color: #29b6f6;
            --secondary-color: #90caf9;
            --card-bg: #1f2937;
            --text-muted: #a3a3a3;
        }
    }
    body { margin: 0; padding: 0; font-family: 'Roboto', sans-serif; background-color: var(--background-color) !important; color: var(--text-color) !important; }
    h1 { font-size: 3rem; color: var(--primary-color) !important; text-align: center; margin-bottom: 15px; }
    h2, h3 { font-size: 1.5rem; color: var(--secondary-color) !important; text-align: center; margin-top: 20px; }
    p { font-family: 'Georgia', serif; color: var(--text-color) !important; line-height: 1.6; text-align: justify; }
    .about-author { background-color: var(--card-bg) !important; border-radius: 10px; padding: 25px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); margin: 20px auto; max-width: 700px; text-align: center; color: var(--text-color) !important; }
    .social-icons { display: flex; justify-content: center; gap: 20px; margin-top: 15px; }
    .social-icons a img { width: 40px; height: 40px; transition: transform 0.3s ease-in-out; }
    .social-icons a img:hover { transform: scale(1.2); }
    footer { margin-top: 50px; text-align: center; font-family: 'Georgia', serif; color: var(--text-muted) !important; }
    .css-1d391kg, .css-1d391kg * { background-color: var(--card-bg) !important; color: var(--text-color) !important; }
    </style>
""", unsafe_allow_html=True)

# Sidebar Navigation
chapters = [
    "Foundation",
    "ML Project Lifecycle",
    "Core Algorithms",
    "Model Evaluation",
    "Data Handling",
    "Computer Vision Basics",
    "Natural Language Processing (NLP)",
    "Deployment & Tools"
]
chapter = st.sidebar.radio("Chapter", chapters)

# Nested page selection based on chapter
page = None
if chapter == "Foundation":
    page = st.sidebar.radio("Page", ["Home", "Introduction to Data Science", "Machine Learning vs Deep Learning"])
elif chapter == "ML Project Lifecycle":
    section = st.sidebar.radio("Section", ["Life Cycle of ML Project"])
    if section:
        page = st.sidebar.radio("Subtopic", [
            "Problem Statement", "Data Collection", "Data Preprocessing",
            "Exploratory Data Analysis (EDA)", "Feature Engineering", "Model Selection",
            "Model Training", "Model Evaluation & Tuning", "Model Deployment", "Monitoring"
        ])
elif chapter == "Core Algorithms":
    page = st.sidebar.radio("Page", [
        "Linear Regression", "Logistic Regression", "k-Nearest Neighbors (kNN)",
        "Decision Trees", "Support Vector Machines (SVM)", "Ensemble Techniques"
    ])
elif chapter == "Model Evaluation":
    page = st.sidebar.radio("Page", ["Performance Metrics"])
    if page == "Performance Metrics":
        page = st.sidebar.radio("Metric", ["Accuracy, Precision, Recall", "Confusion Matrix", "ROC-AUC"])
elif chapter == "Data Handling":
    page = st.sidebar.radio("Page", ["Data Types", "Data Cleaning", "Feature Engineering"])
    if page == "Data Types":
        page = st.sidebar.radio("Type", ["Structured Data (SQL, Excel)", "Semi-Structured Data (JSON, XML)", "Unstructured Data (Images, Text)"])
elif chapter == "Computer Vision Basics":
    page = st.sidebar.radio("Page", ["Image Processing", "OpenCV Basics"])
    if page == "Image Processing":
        page = st.sidebar.radio("Topic", ["Color Spaces", "Image Augmentation", "Splitting/Merging Images"])
elif chapter == "Natural Language Processing (NLP)":
    page = st.sidebar.radio("Page", ["NLP Introduction", "Text Preprocessing"])
elif chapter == "Deployment & Tools":
    page = st.sidebar.radio("Page", ["Model Deployment", "Working with Excel/CSV", "SQL for Data Science"])

# Map pages to external scripts
page_to_script = {
    "Introduction to Data Science": "01_introduction.py",
    "Machine Learning vs Deep Learning": "02_ml_vs_dl.py",
    "Life Cycle of ML Project": "03_life_cycle_of_ml_project.py",
    "Data Handling": "04_data.py"
}

# Helper to load and execute a module from path
def run_script(path, func_name=None, *args):
    full_path = os.path.join(os.path.dirname(__file__), path)
    spec = importlib.util.spec_from_file_location(path, full_path)
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)
    if func_name:
        getattr(module, func_name)(*args)

# Content rendering
if page == "Home":
    # existing Home content
    st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
    st.markdown("## Your Gateway To Become Master In Data Science")
    anim = load_lottieurl("https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json")
    if anim:
        st_lottie(anim, height=200)
    st.subheader("About This Application")
    st.markdown("""
    This platform serves as a **comprehensive guide to Machine Learning and Data Science**.
    From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
    - **Problem Definition**: Understand context and objectives.
    - **Data Handling**: Collect, clean, explore.
    - **Model Development**: Build and optimize.
    - **Model Deployment**: Deliver and monitor solutions.
    """)
    st.subheader("What You'll Learn Here")
    st.markdown("""
    1. **Roadmaps**: Navigate challenges step-by-step.
    2. **Hands-on Projects**: Applied examples.
    3. **Visualizations**: Intuitive graphs.
    4. **Insights**: Lessons from experience.
    """)
    st.markdown("""
    <div class='about-author'>
        <h2>About the Author</h2>
        <p>Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist passionate about ML.</p>
    </div>
    """, unsafe_allow_html=True)
    st.markdown("""
    <div class='social-icons'>
        <a href='https://www.linkedin.com/in/yash-harish-gupta-71b011189/' target='_blank'>
            <img src='https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png' alt='LinkedIn'>
        </a>
        <a href='https://github.com/YashGupta018' target='_blank'>
            <img src='https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png' alt='GitHub'>
        </a>
    </div>
    <footer>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</footer>
    """, unsafe_allow_html=True)

elif page in page_to_script:
    script = page_to_script[page]
    # call appropriate function if needed
    if page == "Introduction to Data Science":
        run_script(script, "show_content", "Introduction")
    elif page == "Machine Learning vs Deep Learning":
        run_script(script)  # top-level execution handles its own nav
    elif page == "Life Cycle of ML Project":
        run_script(script, "draw_lifecycle_diagram")
    elif page == "Data Handling":
        run_script(script, "main")

else:
    # default placeholder for other pages
    st.header(page or chapter)
    st.write("Content for this page will go here.")

# ----------------------------------------------------------------------------------------------------------------------------------------------------------

# import streamlit as st
# from streamlit_lottie import st_lottie
# import requests

# # Function to load Lottie animation from a URL
# def load_lottieurl(url: str):
#     """Fetch Lottie animation JSON from a URL."""
#     try:
#         response = requests.get(url)
#         if response.status_code == 200:
#             return response.json()
#     except requests.exceptions.RequestException:
#         return None

# # CSS Styling for adaptive light/dark modes
# st.markdown("""
#     <style>
#     :root {
#         --background-color: #f8f9fa;
#         --text-color: #212529;
#         --primary-color: #007acc;
#         --secondary-color: #005b96;
#         --card-bg: #ffffff;
#         --text-muted: #6c757d;
#     }
#     @media (prefers-color-scheme: dark) {
#         :root {
#             --background-color: #0e1117;
#             --text-color: #e5e5e5;
#             --primary-color: #29b6f6;
#             --secondary-color: #90caf9;
#             --card-bg: #1f2937;
#             --text-muted: #a3a3a3;
#         }
#     }
#     body {
#         margin: 0;
#         padding: 0;
#         font-family: 'Roboto', sans-serif;
#         background-color: var(--background-color) !important;
#         color: var(--text-color) !important;
#     }
#     h1 { font-size: 3rem; color: var(--primary-color) !important; text-align: center; margin-bottom: 15px; }
#     h2, h3 { font-size: 1.5rem; color: var(--secondary-color) !important; text-align: center; margin-top: 20px; }
#     p { font-family: 'Georgia', serif; color: var(--text-color) !important; line-height: 1.6; text-align: justify; }
#     .about-author { background-color: var(--card-bg) !important; border-radius: 10px; padding: 25px;
#         box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); margin: 20px auto; max-width: 700px; text-align: center;
#         color: var(--text-color) !important; }
#     .social-icons { display: flex; justify-content: center; gap: 20px; margin-top: 15px; }
#     .social-icons a img { width: 40px; height: 40px; transition: transform 0.3s ease-in-out; }
#     .social-icons a img:hover { transform: scale(1.2); }
#     footer { margin-top: 50px; text-align: center; font-family: 'Georgia', serif; color: var(--text-muted) !important; }
#     .css-1d391kg, .css-1d391kg * { background-color: var(--card-bg) !important; color: var(--text-color) !important; }
#     </style>
# """, unsafe_allow_html=True)

# # Sidebar Navigation
# episodes = [
#     "Foundation",
#     "ML Project Lifecycle",
#     "Core Algorithms",
#     "Model Evaluation",
#     "Data Handling",
#     "Computer Vision Basics",
#     "Natural Language Processing (NLP)",
#     "Deployment & Tools"
# ]
# chapter = st.sidebar.radio("Chapter", episodes)

# # Initialize page variables
# page = None

# if chapter == "Foundation":
#     page = st.sidebar.radio("Page", ["Home", "Introduction to Data Science", "Machine Learning vs Deep Learning"])
# elif chapter == "ML Project Lifecycle":
#     section = st.sidebar.radio("Section", ["Life Cycle of ML Project"])
#     if section:
#         page = st.sidebar.radio("Subtopic", [
#             "Problem Statement", "Data Collection", "Data Preprocessing",
#             "Exploratory Data Analysis (EDA)", "Feature Engineering", "Model Selection",
#             "Model Training", "Model Evaluation & Tuning", "Model Deployment", "Monitoring"
#         ])
# elif chapter == "Core Algorithms":
#     page = st.sidebar.radio("Page", [
#         "Linear Regression", "Logistic Regression", "k-Nearest Neighbors (kNN)",
#         "Decision Trees", "Support Vector Machines (SVM)", "Ensemble Techniques"
#     ])
# elif chapter == "Model Evaluation":
#     page = st.sidebar.radio("Page", ["Performance Metrics"])
#     if page == "Performance Metrics":
#         page = st.sidebar.radio("Metric", ["Accuracy, Precision, Recall", "Confusion Matrix", "ROC-AUC"])
# elif chapter == "Data Handling":
#     page = st.sidebar.radio("Page", ["Data Types", "Data Cleaning", "Feature Engineering"])
#     if page == "Data Types":
#         page = st.sidebar.radio("Type", ["Structured Data (SQL, Excel)", "Semi-Structured Data (JSON, XML)", "Unstructured Data (Images, Text)"])
# elif chapter == "Computer Vision Basics":
#     page = st.sidebar.radio("Page", ["Image Processing", "OpenCV Basics"])
#     if page == "Image Processing":
#         page = st.sidebar.radio("Topic", ["Color Spaces", "Image Augmentation", "Splitting/Merging Images"])
# elif chapter == "Natural Language Processing (NLP)":
#     page = st.sidebar.radio("Page", ["NLP Introduction", "Text Preprocessing"])
# elif chapter == "Deployment & Tools":
#     page = st.sidebar.radio("Page", ["Model Deployment", "Working with Excel/CSV", "SQL for Data Science"])

# # Content rendering based on selection
# if page == "Home":
#     st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
#     st.markdown("## Your Gateway To Become Master In Data Science")
#     anim = load_lottieurl("https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json")
#     if anim:
#         st_lottie(anim, height=200)
#     st.subheader("About This Application")
#     st.markdown("""
#     This platform serves as a **comprehensive guide to Machine Learning and Data Science**.
#     From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
#     - **Problem Definition**: Understand context and objectives.
#     - **Data Handling**: Collect, clean, explore.
#     - **Model Development**: Build and optimize.
#     - **Model Deployment**: Deliver and monitor solutions.
#     """)
#     st.subheader("What You'll Learn Here")
#     st.markdown("""
#     1. **Roadmaps**: Navigate challenges step-by-step.
#     2. **Hands-on Projects**: Applied examples.
#     3. **Visualizations**: Intuitive graphs.
#     4. **Insights**: Lessons from experience.
#     """)
#     st.markdown("""
#     <div class='about-author'>
#         <h2>About the Author</h2>
#         <p>Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist passionate about ML.</p>
#     </div>
#     """, unsafe_allow_html=True)
#     st.markdown("""
#     <div class='social-icons'>
#         <a href='https://www.linkedin.com/in/yash-harish-gupta-71b011189/' target='_blank'>
#             <img src='https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png' alt='LinkedIn'>
#         </a>
#         <a href='https://github.com/YashGupta018' target='_blank'>
#             <img src='https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png' alt='GitHub'>
#         </a>
#     </div>
#     <footer>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</footer>
#     """, unsafe_allow_html=True)
# else:
#     st.header(page or chapter)
#     st.write("Content for this page will go here.")

# ----------------------------------------------------------------------------------------------------------------------------------------------------------

# import streamlit as st
# from streamlit_lottie import st_lottie
# import requests

# # Function to load Lottie animation from a URL
# def load_lottieurl(url: str):
#     """Fetch Lottie animation JSON from a URL."""
#     try:
#         response = requests.get(url)
#         if response.status_code == 200:
#             return response.json()
#     except requests.exceptions.RequestException:
#         return None

# # CSS Styling for light and dark modes with adaptive theme support
# st.markdown("""
#     <style>
#     :root {
#         --background-color: #f8f9fa;
#         --text-color: #212529;
#         --primary-color: #007acc;
#         --secondary-color: #005b96;
#         --card-bg: #ffffff;
#         --text-muted: #6c757d;
#     }
#     @media (prefers-color-scheme: dark) {
#         :root {
#             --background-color: #0e1117;
#             --text-color: #e5e5e5;
#             --primary-color: #29b6f6;
#             --secondary-color: #90caf9;
#             --card-bg: #1f2937;
#             --text-muted: #a3a3a3;
#         }
#     }
#     body {
#         margin: 0;
#         padding: 0;
#         font-family: 'Roboto', sans-serif;
#         background-color: var(--background-color) !important;
#         color: var(--text-color) !important;
#     }
#     h1 {
#         font-size: 3rem;
#         color: var(--primary-color) !important;
#         text-align: center;
#         margin-bottom: 15px;
#     }
#     h2, h3 {
#         font-size: 1.5rem;
#         color: var(--secondary-color) !important;
#         text-align: center;
#         margin-top: 20px;
#     }
#     p {
#         font-family: 'Georgia', serif;
#         color: var(--text-color) !important;
#         line-height: 1.6;
#         text-align: justify;
#     }
#     .about-author {
#         background-color: var(--card-bg) !important;
#         border-radius: 10px;
#         padding: 25px;
#         box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
#         margin: 20px auto;
#         max-width: 700px;
#         text-align: center;
#         color: var(--text-color) !important;
#     }
#     .social-icons {
#         display: flex;
#         justify-content: center;
#         gap: 20px;
#         margin-top: 15px;
#     }
#     .social-icons a img {
#         width: 40px;
#         height: 40px;
#         transition: transform 0.3s ease-in-out;
#     }
#     .social-icons a img:hover {
#         transform: scale(1.2);
#     }
#     footer {
#         margin-top: 50px;
#         text-align: center;
#         font-family: 'Georgia', serif;
#         color: var(--text-muted) !important;
#     }
#     /* Sidebar adjustments */
#     .css-1d391kg, .css-1d391kg * {
#         background-color: var(--card-bg) !important;
#         color: var(--text-color) !important;
#     }
#     </style>
#     """, unsafe_allow_html=True)

# # Sidebar Navigation
# chapters = [
#     "Foundation",
#     "ML Project Lifecycle",
#     "Core Algorithms",
#     "Model Evaluation",
#     "Data Handling",
#     "Computer Vision Basics",
#     "Natural Language Processing (NLP)",
#     "Deployment & Tools"
# ]
# selected_chapter = st.sidebar.radio("Select Chapter", chapters)

# # Nested page selection based on chapter
# if selected_chapter == "Foundation":
#     page = st.sidebar.radio("Select Page", [
#         "Home",
#         "Introduction to Data Science",
#         "Machine Learning vs Deep Learning"
#     ])
# elif selected_chapter == "ML Project Lifecycle":
#     section = st.sidebar.radio("Select Section", ["Life Cycle of ML Project"])
#     if section:
#         page = st.sidebar.radio("Select Subtopic", [
#             "Problem Statement",
#             "Data Collection",
#             "Data Preprocessing",
#             "Exploratory Data Analysis (EDA)",
#             "Feature Engineering",
#             "Model Selection",
#             "Model Training",
#             "Model Evaluation & Tuning",
#             "Model Deployment",
#             "Monitoring"
#         ])
# elif selected_chapter == "Core Algorithms":
#     page = st.sidebar.radio("Select Page", [
#         "Linear Regression",
#         "Logistic Regression",
#         "k-Nearest Neighbors (kNN)",
#         "Decision Trees",
#         "Support Vector Machines (SVM)",
#         "Ensemble Techniques"
#     ])
# elif selected_chapter == "Model Evaluation":
#     page = st.sidebar.radio("Select Page", ["Performance Metrics"])
#     if page == "Performance Metrics":
#         metric = st.sidebar.radio("Choose Metric", [
#             "Accuracy, Precision, Recall",
#             "Confusion Matrix",
#             "ROC-AUC"
#         ])
# elif selected_chapter == "Data Handling":
#     page = st.sidebar.radio("Select Page", [
#         "Data Types",
#         "Data Cleaning",
#         "Feature Engineering"
#     ])
#     if page == "Data Types":
#         dtype = st.sidebar.radio("Select Type", [
#             "Structured Data (SQL, Excel)",
#             "Semi-Structured Data (JSON, XML)",
#             "Unstructured Data (Images, Text)"
#         ])
# elif selected_chapter == "Computer Vision Basics":
#     page = st.sidebar.radio("Select Page", ["Image Processing", "OpenCV Basics"])
#     if page == "Image Processing":
#         iproc = st.sidebar.radio("Select Topic", [
#             "Color Spaces",
#             "Image Augmentation",
#             "Splitting/Merging Images"
#         ])
# elif selected_chapter == "Natural Language Processing (NLP)":
#     page = st.sidebar.radio("Select Page", [
#         "NLP Introduction",
#         "Text Preprocessing"
#     ])
# elif selected_chapter == "Deployment & Tools":
#     page = st.sidebar.radio("Select Page", [
#         "Model Deployment",
#         "Working with Excel/CSV",
#         "SQL for Data Science"
#     ])

# # Title and Tagline
# st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
# st.markdown("## Your Gateway To Become Master In Data Science")

# # Display Lottie animation
# animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json"
# lottie_animation = load_lottieurl(animation_url)
# if lottie_animation:
#     st_lottie(lottie_animation, height=200, key="animation")

# # About the App Section
# st.subheader("About This Application")
# st.markdown("""
# This platform serves as a **comprehensive guide to Machine Learning and Data Science**.  
# From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
# - **Problem Definition**: Understand the business context and set clear objectives.  
# - **Data Handling**: Collect, clean, and explore datasets to uncover insights.  
# - **Model Development**: Build and optimize machine learning models.  
# - **Model Deployment**: Deliver real-world solutions and monitor performance.  
# Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples.
# """)

# # Key Takeaways Section
# st.subheader("What You'll Learn Here")
# st.markdown("""
# 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges.  
# 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning.  
# 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts.  
# 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls.  
# """)

# # Author Section
# st.markdown("""
# <div class="about-author">
#     <h2>About the Author</h2>
#     <p>
#         Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist deeply passionate about machine learning.  
#         My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions.  
#         Currently, I am learning and preparing to embark on my professional career in this exciting field.
#     </p>
# </div>
# """, unsafe_allow_html=True)

# # Social Links Section
# st.markdown("""
# <div class="social-icons">
#     <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank">
#         <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn">
#     </a>
#     <a href="https://github.com/YashGupta018" target="_blank">
#         <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub">
#     </a>
# </div>
# """, unsafe_allow_html=True)

# # Footer Section
# st.markdown("""
# <footer>
#     <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p>
# </footer>
# """, unsafe_allow_html=True)

# -----------------------------------------------------------------------------------------------------------------------------------------------------

# import streamlit as st
# from streamlit_lottie import st_lottie
# import requests

# # Function to load Lottie animation from a URL
# def load_lottieurl(url: str):
#     """Fetch Lottie animation JSON from a URL."""
#     try:
#         response = requests.get(url)
#         if response.status_code == 200:
#             return response.json()
#     except requests.exceptions.RequestException:
#         return None

# # CSS Styling for light and dark modes
# st.markdown("""
#     <style>
#     body {
#         margin: 0;
#         padding: 0;
#         font-family: 'Roboto', sans-serif;
#         background-color: var(--background-color, #f8f9fa);
#         color: var(--text-color, #212529);
#     }
#     h1 {
#         font-size: 3rem;
#         color: var(--primary-color, #007acc);
#         text-align: center;
#         margin-bottom: 15px;
#     }
#     h2, h3 {
#         font-size: 1.5rem;
#         color: var(--secondary-color, #005b96);
#         text-align: center;
#         margin-top: 20px;
#     }
#     p {
#         font-family: 'Georgia', serif;
#         color: var(--text-color, #212529);
#         line-height: 1.6;
#         text-align: justify;
#     }
#     .about-author {
#         background-color: var(--card-bg, #ffffff);
#         border-radius: 10px;
#         padding: 25px;
#         box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
#         margin: 20px auto;
#         max-width: 700px;
#         text-align: center;
#         color: var(--text-color, #212529);
#     }
#     .social-icons {
#         display: flex;
#         justify-content: center;
#         gap: 20px;
#         margin-top: 15px;
#     }
#     .social-icons a img {
#         width: 40px;
#         height: 40px;
#         transition: transform 0.3s ease-in-out;
#     }
#     .social-icons a img:hover {
#         transform: scale(1.2);
#     }
#     footer {
#         margin-top: 50px;
#         text-align: center;
#         font-family: 'Georgia', serif;
#         color: var(--text-muted, #6c757d);
#     }
#     </style>
#     """, unsafe_allow_html=True)

# # Sidebar Navigation
# chapters = [
#     "Foundation",
#     "ML Project Lifecycle",
#     "Core Algorithms",
#     "Model Evaluation",
#     "Data Handling",
#     "Computer Vision Basics",
#     "Natural Language Processing (NLP)",
#     "Deployment & Tools"
# ]
# selected_chapter = st.sidebar.radio("Select Chapter", chapters)

# # Nested page selection based on chapter
# if selected_chapter == "Foundation":
#     page = st.sidebar.radio("Select Page", [
#         "Home",
#         "Introduction to Data Science",
#         "Machine Learning vs Deep Learning"
#     ])
# elif selected_chapter == "ML Project Lifecycle":
#     section = st.sidebar.radio("Select Section", ["Life Cycle of ML Project"])
#     if section:
#         page = st.sidebar.radio("Select Subtopic", [
#             "Problem Statement",
#             "Data Collection",
#             "Data Preprocessing",
#             "Exploratory Data Analysis (EDA)",
#             "Feature Engineering",
#             "Model Selection",
#             "Model Training",
#             "Model Evaluation & Tuning",
#             "Model Deployment",
#             "Monitoring"
#         ])
# elif selected_chapter == "Core Algorithms":
#     page = st.sidebar.radio("Select Page", [
#         "Linear Regression",
#         "Logistic Regression",
#         "k-Nearest Neighbors (kNN)",
#         "Decision Trees",
#         "Support Vector Machines (SVM)",
#         "Ensemble Techniques"
#     ])
# elif selected_chapter == "Model Evaluation":
#     page = st.sidebar.radio("Select Page", ["Performance Metrics"])
#     if page == "Performance Metrics":
#         metric = st.sidebar.radio("Choose Metric", [
#             "Accuracy, Precision, Recall",
#             "Confusion Matrix",
#             "ROC-AUC"
#         ])
# elif selected_chapter == "Data Handling":
#     page = st.sidebar.radio("Select Page", [
#         "Data Types",
#         "Data Cleaning",
#         "Feature Engineering"
#     ])
#     if page == "Data Types":
#         dtype = st.sidebar.radio("Select Type", [
#             "Structured Data (SQL, Excel)",
#             "Semi-Structured Data (JSON, XML)",
#             "Unstructured Data (Images, Text)"
#         ])
# elif selected_chapter == "Computer Vision Basics":
#     page = st.sidebar.radio("Select Page", ["Image Processing", "OpenCV Basics"])
#     if page == "Image Processing":
#         iproc = st.sidebar.radio("Select Topic", [
#             "Color Spaces",
#             "Image Augmentation",
#             "Splitting/Merging Images"
#         ])
# elif selected_chapter == "Natural Language Processing (NLP)":
#     page = st.sidebar.radio("Select Page", [
#         "NLP Introduction",
#         "Text Preprocessing"
#     ])
# elif selected_chapter == "Deployment & Tools":
#     page = st.sidebar.radio("Select Page", [
#         "Model Deployment",
#         "Working with Excel/CSV",
#         "SQL for Data Science"
#     ])

# # Title and Tagline
# st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
# st.markdown("## Your Gateway To Become Master In Data Science")

# # Display Lottie animation
# animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json"
# lottie_animation = load_lottieurl(animation_url)
# if lottie_animation:
#     st_lottie(lottie_animation, height=200, key="animation")

# # About the App Section
# st.subheader("About This Application")
# st.markdown("""
# This platform serves as a **comprehensive guide to Machine Learning and Data Science**.  
# From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
# - **Problem Definition**: Understand the business context and set clear objectives.  
# - **Data Handling**: Collect, clean, and explore datasets to uncover insights.  
# - **Model Development**: Build and optimize machine learning models.  
# - **Model Deployment**: Deliver real-world solutions and monitor performance.  
# Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples.
# """)

# # Key Takeaways Section
# st.subheader("What You'll Learn Here")
# st.markdown("""
# 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges.  
# 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning.  
# 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts.  
# 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls.  
# """)

# # Author Section
# st.markdown("""
# <div class="about-author">
#     <h2>About the Author</h2>
#     <p>
#         Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist deeply passionate about machine learning.  
#         My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions.  
#         Currently, I am learning and preparing to embark on my professional career in this exciting field.
#     </p>
# </div>
# """, unsafe_allow_html=True)

# # Social Links Section
# st.markdown("""
# <div class="social-icons">
#     <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank">
#         <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn">
#     </a>
#     <a href="https://github.com/YashGupta018" target="_blank">
#         <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub">
#     </a>
# </div>
# """, unsafe_allow_html=True)

# # Footer Section
# st.markdown("""
# <footer>
#     <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p>
# </footer>
# """, unsafe_allow_html=True)

# -------------------------------------------------------------------------------------------------------------------------------------------

# import streamlit as st
# from streamlit_lottie import st_lottie
# import requests

# # Function to load Lottie animation from a URL
# def load_lottieurl(url: str):
#     """Fetch Lottie animation JSON from a URL."""
#     try:
#         response = requests.get(url)
#         if response.status_code == 200:
#             return response.json()
#     except requests.exceptions.RequestException:
#         return None

# # CSS Styling for light and dark modes
# st.markdown("""
#     <style>
#     body {
#         margin: 0;
#         padding: 0;
#         font-family: 'Roboto', sans-serif;
#         background-color: var(--background-color, #f8f9fa);
#         color: var(--text-color, #212529);
#     }
#     h1 {
#         font-size: 3rem;
#         color: var(--primary-color, #007acc);
#         text-align: center;
#         margin-bottom: 15px;
#     }
#     h2, h3 {
#         font-size: 1.5rem;
#         color: var(--secondary-color, #005b96);
#         text-align: center;
#         margin-top: 20px;
#     }
#     p {
#         font-family: 'Georgia', serif;
#         color: var(--text-color, #212529);
#         line-height: 1.6;
#         text-align: justify;
#     }
#     .about-author {
#         background-color: var(--card-bg, #ffffff);
#         border-radius: 10px;
#         padding: 25px;
#         box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
#         margin: 20px auto;
#         max-width: 700px;
#         text-align: center;
#         color: var(--text-color, #212529);
#     }
#     .social-icons {
#         display: flex;
#         justify-content: center;
#         gap: 20px;
#         margin-top: 15px;
#     }
#     .social-icons a img {
#         width: 40px;
#         height: 40px;
#         transition: transform 0.3s ease-in-out;
#     }
#     .social-icons a img:hover {
#         transform: scale(1.2);
#     }
#     footer {
#         margin-top: 50px;
#         text-align: center;
#         font-family: 'Georgia', serif;
#         color: var(--text-muted, #6c757d);
#     }
#     </style>
#     """, unsafe_allow_html=True)

# # Navigation Bar (Sidebar) - Inserted with the exact list as requested
# st.sidebar.markdown(
#     """
#     # Navigation

#     1. **Foundation**

#         - Home  
#         - Introduction to Data Science  
#         - Machine Learning vs Deep Learning

#     2. **ML Project Lifecycle**

#         - Life Cycle of ML Project  
#             - Problem Statement  
#             - Data Collection  
#             - Data Preprocessing  
#             - Exploratory Data Analysis (EDA)  
#             - Feature Engineering  
#             - Model Selection  
#             - Model Training  
#             - Model Evaluation & Tuning  
#             - Model Deployment  
#             - Monitoring

#     3. **Core Algorithms**

#         - Linear Regression  
#         - Logistic Regression  
#         - k-Nearest Neighbors (kNN)  
#         - Decision Trees  
#         - Support Vector Machines (SVM)  
#         - Ensemble Techniques

#     4. **Model Evaluation**

#         - Performance Metrics  
#             - Accuracy, Precision, Recall  
#             - Confusion Matrix  
#             - ROC-AUC

#     5. **Data Handling**

#         - Data Types:  
#             - Structured Data (SQL, Excel)  
#             - Semi-Structured Data (JSON, XML)  
#             - Unstructured Data (Images, Text)  
#         - Data Cleaning  
#         - Feature Engineering

#     6. **Computer Vision Basics**

#         - Image Processing  
#             - Color Spaces  
#             - Image Augmentation  
#             - Splitting/Merging Images  
#         - OpenCV Basics

#     7. **Natural Language Processing (NLP)**

#         - NLP Introduction  
#         - Text Preprocessing

#     8. **Deployment & Tools**

#         - Model Deployment  
#         - Working with Excel/CSV  
#         - SQL for Data Science
#     """, unsafe_allow_html=True)

# # Title and Tagline
# st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
# st.markdown("## Your Gateway To Become Master In Data Science")

# # Display Lottie animation
# animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json"
# lottie_animation = load_lottieurl(animation_url)
# if lottie_animation:
#     st_lottie(lottie_animation, height=200, key="animation")

# # About the App Section
# st.subheader("About This Application")
# st.markdown("""
# This platform serves as a **comprehensive guide to Machine Learning and Data Science**.  
# From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
# - **Problem Definition**: Understand the business context and set clear objectives.  
# - **Data Handling**: Collect, clean, and explore datasets to uncover insights.  
# - **Model Development**: Build and optimize machine learning models.  
# - **Model Deployment**: Deliver real-world solutions and monitor performance.  
# Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples.
# """)

# # Key Takeaways Section
# st.subheader("What You'll Learn Here")
# st.markdown("""
# 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges.  
# 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning.  
# 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts.  
# 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls.  
# """)

# # Author Section
# st.markdown("""
# <div class="about-author">
#     <h2>About the Author</h2>
#     <p>
#         Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist deeply passionate about machine learning.  
#         My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions.  
#         Currently, I am learning and preparing to embark on my professional career in this exciting field.
#     </p>
# </div>
# """, unsafe_allow_html=True)

# # Social Links Section
# st.markdown("""
# <div class="social-icons">
#     <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank">
#         <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn">
#     </a>
#     <a href="https://github.com/YashGupta018" target="_blank">
#         <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub">
#     </a>
# </div>
# """, unsafe_allow_html=True)

# # Footer Section
# st.markdown("""
# <footer>
#     <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p>
# </footer>
# """, unsafe_allow_html=True)

# -------------------------------------------------------------------------------------------------------------------------------------------------------

# import streamlit as st
# from streamlit_lottie import st_lottie
# import requests

# # Function to load Lottie animation from a URL
# def load_lottieurl(url: str):
#     """Fetch Lottie animation JSON from a URL."""
#     try:
#         response = requests.get(url)
#         if response.status_code == 200:
#             return response.json()
#     except requests.exceptions.RequestException:
#         return None

# # CSS Styling for light and dark modes
# st.markdown("""
#     <style>
#     body {
#         margin: 0;
#         padding: 0;
#         font-family: 'Roboto', sans-serif;
#         background-color: var(--background-color, #f8f9fa);
#         color: var(--text-color, #212529);
#     }
#     h1 {
#         font-size: 3rem;
#         color: var(--primary-color, #007acc);
#         text-align: center;
#         margin-bottom: 15px;
#     }
#     h2, h3 {
#         font-size: 1.5rem;
#         color: var(--secondary-color, #005b96);
#         text-align: center;
#         margin-top: 20px;
#     }
#     p {
#         font-family: 'Georgia', serif;
#         color: var(--text-color, #212529);
#         line-height: 1.6;
#         text-align: justify;
#     }
#     .about-author {
#         background-color: var(--card-bg, #ffffff);
#         border-radius: 10px;
#         padding: 25px;
#         box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
#         margin: 20px auto;
#         max-width: 700px;
#         text-align: center;
#         color: var(--text-color, #212529);
#     }
#     .social-icons {
#         display: flex;
#         justify-content: center;
#         gap: 20px;
#         margin-top: 15px;
#     }
#     .social-icons a img {
#         width: 40px;
#         height: 40px;
#         transition: transform 0.3s ease-in-out;
#     }
#     .social-icons a img:hover {
#         transform: scale(1.2);
#     }
#     footer {
#         margin-top: 50px;
#         text-align: center;
#         font-family: 'Georgia', serif;
#         color: var(--text-muted, #6c757d);
#     }
#     </style>
#     """, unsafe_allow_html=True)

# # Title and Tagline
# st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
# st.markdown("## Your Gateway To Become Master In Data Science")

# # Display Lottie animation
# animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json"
# lottie_animation = load_lottieurl(animation_url)
# if lottie_animation:
#     st_lottie(lottie_animation, height=200, key="animation")

# # About the App Section
# st.subheader("About This Application")
# st.markdown("""
# This platform serves as a **comprehensive guide to Machine Learning and Data Science**.  
# From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
# - **Problem Definition**: Understand the business context and set clear objectives.  
# - **Data Handling**: Collect, clean, and explore datasets to uncover insights.  
# - **Model Development**: Build and optimize machine learning models.  
# - **Model Deployment**: Deliver real-world solutions and monitor performance.  

# Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples.
# """)

# # Key Takeaways Section
# st.subheader("What You'll Learn Here")
# st.markdown("""
# 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges.  
# 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning.  
# 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts.  
# 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls.  
# """)

# # Author Section
# st.markdown("""
# <div class="about-author">
#     <h2>About the Author</h2>
#     <p>
#         Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist deeply passionate about machine learning.  
#         My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions.  
#         Currently, I am learning and preparing to embark on my professional career in this exciting field.
#     </p>
# </div>
# """, unsafe_allow_html=True)

# # Social Links Section
# st.markdown("""
# <div class="social-icons">
#     <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank">
#         <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn">
#     </a>
#     <a href="https://github.com/YashGupta018" target="_blank">
#         <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub">
#     </a>
# </div>
# """, unsafe_allow_html=True)

# # Footer Section
# st.markdown("""
# <footer>
#     <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p>
# </footer>
# """, unsafe_allow_html=True)