Update home.py

home.py

@@ -1,6 +1,8 @@
 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):
@@ -33,19 +35,11 @@ st.markdown("""
             --text-muted: #a3a3a3;
         }
     }
-    body {
-        margin: 0;
-        padding: 0;
-        font-family: 'Roboto', sans-serif;
-        background-color: var(--background-color) !important;
-        color: var(--text-color) !important;
-    }
+    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; }
+    .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); }
@@ -55,7 +49,7 @@ st.markdown("""
 """, unsafe_allow_html=True)
 
 # Sidebar Navigation
-episodes = [
+chapters = [
     "Foundation",
     "ML Project Lifecycle",
     "Core Algorithms",
@@ -65,11 +59,10 @@ episodes = [
     "Natural Language Processing (NLP)",
     "Deployment & Tools"
 ]
-chapter = st.sidebar.radio("Chapter", episodes)
+chapter = st.sidebar.radio("Chapter", chapters)
 
-# Initialize page variables
+# 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":
@@ -102,8 +95,26 @@ elif chapter == "Natural Language Processing (NLP)":
 elif chapter == "Deployment & Tools":
     page = st.sidebar.radio("Page", ["Model Deployment", "Working with Excel/CSV", "SQL for Data Science"])
 
-# Content rendering based on selection
+# 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")
@@ -142,7 +153,21 @@ if page == "Home":
     </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.")
 
@@ -162,6 +187,156 @@ else:
 #     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>