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Update home.py
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home.py
CHANGED
@@ -2,7 +2,6 @@ import streamlit as st
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from streamlit_lottie import st_lottie
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import requests
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# Function to load Lottie animation from a URL
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def load_lottieurl(url: str):
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"""Fetch Lottie animation JSON from a URL."""
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try:
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@@ -12,7 +11,7 @@ def load_lottieurl(url: str):
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except requests.exceptions.RequestException:
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return None
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#
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st.markdown("""
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<style>
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:root {
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@@ -42,17 +41,27 @@ st.markdown("""
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}
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h1 {
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font-size: 2.5rem !important;
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border-bottom: 2px solid var(--primary-color);
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padding-bottom: 0.5rem;
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}
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h2 {
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font-size: 2rem !important;
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margin: 1.5rem 0 1rem !important;
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}
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h3 {
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font-size: 1.5rem !important;
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margin: 1rem 0 0.5rem !important;
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}
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.content-block {
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margin: 1.5rem 0;
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padding: 1.5rem;
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@@ -60,12 +69,6 @@ st.markdown("""
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border-radius: 10px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.1);
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}
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p {
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font-family: 'Georgia', serif;
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color: var(--text-color) !important;
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line-height: 1.6;
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text-align: justify;
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}
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.about-author {
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background-color: var(--card-bg) !important;
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border-radius: 10px;
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@@ -96,384 +99,156 @@ st.markdown("""
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font-family: 'Georgia', serif;
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color: var(--text-muted) !important;
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}
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/* Sidebar adjustments */
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.css-1d391kg, .css-1d391kg * {
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background-color: var(--card-bg) !important;
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color: var(--text-color) !important;
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}
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</style>
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-
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#
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chapters = [
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"Foundation",
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"
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"Core Algorithms",
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"Model Evaluation",
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"Data Handling",
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"Computer Vision Basics",
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"Natural Language Processing (NLP)",
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"Deployment & Tools"
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]
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selected_chapter = st.sidebar.radio("Select Chapter", chapters)
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# Nested page selection based on chapter
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if selected_chapter == "Foundation":
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foundation_main = st.sidebar.radio("Select Page", [
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"Home",
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"Introduction to Data Science",
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"Machine Learning vs Deep Learning"
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])
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if foundation_main == "Introduction to Data Science":
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selected_subtopic = st.sidebar.radio("Explore More", [
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"Understanding Intelligence",
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"
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"
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"What is Data Science?",
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"The Role of a Data Scientist",
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"Why AI and Data Science Matter",
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"Did You Know?"
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])
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elif foundation_main == "Machine Learning vs Deep Learning":
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st.sidebar.radio("Explore Comparison", [
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"Understanding Machine Learning and Deep Learning",
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"Comparison Table for ML vs DL"
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])
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stage = st.sidebar.radio("Select Stage", ["Life Cycle of ML Project"])
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if stage:
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st.sidebar.radio("Subtopic", [
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"Problem Statement",
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"Data Collection",
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"Data Preprocessing",
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"Exploratory Data Analysis (EDA)",
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"Feature Engineering",
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"Model Selection",
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"Model Training",
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"Model Evaluation & Tuning",
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"Model Deployment",
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"Monitoring"
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])
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st.sidebar.radio("Select Algorithm", [
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"Linear Regression",
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"Logistic Regression",
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"k‑Nearest Neighbors (kNN)",
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"Decision Trees",
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"Support Vector Machines (SVM)",
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"Ensemble Techniques"
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])
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elif selected_chapter == "Model Evaluation":
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metric_main = st.sidebar.radio("Select Topic", ["Performance Metrics"])
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if metric_main:
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st.sidebar.radio("Choose Metric", [
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"Accuracy, Precision, Recall",
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"Confusion Matrix",
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"ROC‑AUC"
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])
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elif selected_chapter == "Data Handling":
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data_main = st.sidebar.radio("Select Topic", [
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"Data Types Overview",
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"Data Cleaning",
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"Feature Engineering"
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])
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if data_main == "Data Types Overview":
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st.sidebar.radio("Choose Type", [
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"Structured Data (SQL, Excel)",
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"Semi‑Structured Data (JSON, XML)",
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"Unstructured Data (Images, Text)"
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])
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elif selected_chapter == "Computer Vision Basics":
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cv_topic = st.sidebar.radio("Select Topic", ["Image Processing", "OpenCV Basics"])
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if cv_topic == "Image Processing":
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st.sidebar.radio("Subtopic", [
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"Color Spaces",
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"Image Augmentation",
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"Splitting/Merging Images"
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])
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elif selected_chapter == "Natural Language Processing (NLP)":
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st.sidebar.radio("Select Topic", ["NLP Introduction", "Text Preprocessing"])
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elif selected_chapter == "Deployment & Tools":
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st.sidebar.radio("Select Tool", [
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"Model Deployment",
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"Working with Excel/CSV",
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"SQL for Data Science"
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])
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# ======== NEW CONTENT RENDERING ========
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content_rendered = False
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if selected_chapter == "Foundation" and foundation_main == "Introduction to Data Science":
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content_rendered = True
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# Load animations
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brain_animation = load_lottieurl("https://lottie.host/8d7bdc88-7e11-44b5-995a-6561230e54a1/4X3p3YVQZ5.json")
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ml_animation = load_lottieurl("https://lottie.host/5b6292ff-aad4-4a34-8c0a-4c4cd186f80e/5ZVgB9Q9kF.json")
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art_animation = load_lottieurl("https://lottie.host/0d0cf470-8cef-4a0a-8980-3f2d5e573e98/5XvjOZPmhG.json")
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content_rendered = True
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# --- Understanding Intelligence ---
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if selected_subtopic == "Understanding Intelligence":
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st.markdown("# Understanding Intelligence")
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with st.container():
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st.markdown("### Natural Intelligence
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st.markdown("""
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# --- AI Tools Section ---
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elif selected_subtopic == "AI Tools: ML, DL, and Gen‑AI":
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st.markdown("# AI Toolkit Breakdown")
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with st.container():
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st.markdown("### Machine Learning (ML)")
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st.markdown("""
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with st.container():
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st.markdown("### Deep Learning (DL)")
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st.markdown("""
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#
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# **Definition:** Innate intelligence in living beings
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# - Dogs learning tricks
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# - Humans solving puzzles
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# - Birds building nests instinctively
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# """)
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# with col2:
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# st.markdown("""
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# ### Artificial Intelligence 🤖
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# **Definition:** Human-like intelligence in machines
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# - Netflix recommendations
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# - Google Maps routing
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# - Alexa voice assistant
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# """)
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# elif selected_subtopic == "AI Tools: ML, DL, and Gen‑AI":
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# st.header("🔧 AI Toolkit Breakdown")
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# if ml_animation:
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# st_lottie(ml_animation, height=250, key="ml")
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# tabs = st.tabs(["Machine Learning", "Deep Learning", "Generative AI"])
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# with tabs[0]:
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# st.markdown("""
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# **Machine Learning (ML)**
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# 🎯 **Purpose:** Pattern recognition & decision making
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# 👶 **Analogy:** Teaching toddler to recognize fruits
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# 🚀 **Applications:**
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# - Spam email filtering
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# - Stock price prediction
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# - Customer churn analysis
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# """)
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# with tabs[1]:
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# st.markdown("""
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# **Deep Learning (DL)**
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# 🎯 **Purpose:** Complex data processing
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# 🧠 **Structure:** Neural networks with multiple layers
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# 🚀 **Applications:**
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# - Facial recognition systems
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# - Medical image analysis
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# - Voice-controlled assistants
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# """)
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# with tabs[2]:
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# st.markdown("""
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# **Generative AI**
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# 🎯 **Purpose:** Creative content generation
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# 🎨 **Analogy:** Digital artist with infinite ideas
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# 🚀 **Applications:**
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# - ChatGPT conversations
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# - DALL·E image creation
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# - AI-composed music
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# """)
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elif selected_subtopic == "Real‑Life Analogies and Examples":
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col1, col2 = st.columns([2, 1])
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with col1:
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st.header("🎨 Learning vs Generating")
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st.markdown("""
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**Art Analogy:**
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- ML = Sketching with pencil (foundations)
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- DL = Inking with pen (details)
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- Gen-AI = Color painting (creation)
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**Child Development Analogy:**
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1. Learn alphabet → ML (pattern recognition)
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2. Write essays → DL (complex processing)
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3. Create poetry → Gen-AI (original content)
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""")
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with col2:
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if art_animation:
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st_lottie(art_animation, height=300, key="art")
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elif selected_subtopic == "What is Data Science?":
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st.header("🔍 Data Science Demystified")
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st.image("https://miro.medium.com/v2/resize:fit:1400/1*K7nYl2D2QO5B9v6U-4yQxA.png",
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width=600, caption="Data Science Process Flow")
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st.markdown("""
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**Three Pillars of Data Science:**
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1. 📥 Data Collection:
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- Databases, APIs, IoT sensors
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- Structured & unstructured data
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2. 🧠 Data Analysis:
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- Statistical modeling
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- Pattern identification
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3. 📊 Data Visualization:
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- Interactive dashboards
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- Business intelligence tools
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""")
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elif selected_subtopic == "The Role of a Data Scientist":
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st.header("👨💻 Data Scientist's Toolkit")
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st.markdown("""
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**Core Responsibilities:**
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- Build predictive models for business outcomes
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- Analyze customer behavior patterns
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- Optimize operational efficiency
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**Essential Skills Matrix:**
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| Technical Skills | Business Skills |
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|------------------|------------------|
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| Python/R | Domain Knowledge |
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| SQL | Storytelling |
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| ML Frameworks | Problem Solving |
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""")
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elif selected_subtopic == "Why AI and Data Science Matter":
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st.header("🌍 Transformative Impact")
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st.markdown("""
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**Industry Revolution:**
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- Healthcare: Early disease detection (40% faster diagnosis)
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- Agriculture: AI-powered yield prediction (+25% productivity)
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- Entertainment: Personalized content recommendations
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**Economic Impact:**
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> "By 2030, AI could contribute up to $15.7 trillion to global economy"
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-*PwC Global AI Study*
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""")
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elif selected_subtopic == "Did You Know?":
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st.header("🤖 AI in Action: Surprising Uses")
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cols = st.columns(3)
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cols[0].markdown("""
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**Medical Imaging**
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- 92% accuracy in tumor detection
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- Reduces diagnosis time by 60%
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""")
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cols[1].markdown("""
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**Smart Farming**
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- Automated irrigation systems
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- Pest prediction algorithms
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""")
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cols[2].markdown("""
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**Creative AI**
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- AI-written novels
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- Algorithmic music composition
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""")
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st.video("https://www.youtube.com/watch?v=JMUxmLyrhSk")
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# ======== ORIGINAL PAGE ELEMENTS ========
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if not content_rendered:
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#
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st.
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st.markdown("## Your Gateway To Become Master In Data Science")
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animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json"
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lottie_animation = load_lottieurl(animation_url)
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if lottie_animation:
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st_lottie(lottie_animation, height=
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st.markdown("""
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<h2>About the Author</h2>
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<p>
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Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist deeply passionate about machine learning.
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My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions.
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Currently, I am learning and preparing to embark on my professional career in this exciting field.
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</p>
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</div>
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""", unsafe_allow_html=True)
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# Social Links
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st.markdown("""
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<div class="social-icons">
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</div>
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""", unsafe_allow_html=True)
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# Footer
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st.markdown("""
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<footer>
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</footer>
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""", unsafe_allow_html=True)
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# -------------------------------------------------------------------------------------------------------------------------------------
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from streamlit_lottie import st_lottie
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import requests
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def load_lottieurl(url: str):
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"""Fetch Lottie animation JSON from a URL."""
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try:
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except requests.exceptions.RequestException:
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return None
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# ========== UPDATED CSS ==========
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st.markdown("""
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<style>
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:root {
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}
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h1 {
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font-size: 2.5rem !important;
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color: var(--primary-color) !important;
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text-align: center;
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margin-bottom: 15px;
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border-bottom: 2px solid var(--primary-color);
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padding-bottom: 0.5rem;
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}
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h2 {
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font-size: 2rem !important;
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color: var(--secondary-color) !important;
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margin: 1.5rem 0 1rem !important;
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}
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h3 {
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font-size: 1.5rem !important;
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margin: 1rem 0 0.5rem !important;
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}
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+
p {
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font-family: 'Georgia', serif;
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color: var(--text-color) !important;
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+
line-height: 1.6;
|
63 |
+
text-align: justify;
|
64 |
+
}
|
65 |
.content-block {
|
66 |
margin: 1.5rem 0;
|
67 |
padding: 1.5rem;
|
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|
69 |
border-radius: 10px;
|
70 |
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
|
71 |
}
|
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|
72 |
.about-author {
|
73 |
background-color: var(--card-bg) !important;
|
74 |
border-radius: 10px;
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|
99 |
font-family: 'Georgia', serif;
|
100 |
color: var(--text-muted) !important;
|
101 |
}
|
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|
102 |
.css-1d391kg, .css-1d391kg * {
|
103 |
background-color: var(--card-bg) !important;
|
104 |
color: var(--text-color) !important;
|
105 |
}
|
106 |
</style>
|
107 |
+
""", unsafe_allow_html=True)
|
108 |
|
109 |
+
# ========== NAVIGATION (UNCHANGED) ==========
|
110 |
chapters = [
|
111 |
+
"Foundation", "ML Project Lifecycle", "Core Algorithms", "Model Evaluation",
|
112 |
+
"Data Handling", "Computer Vision Basics", "Natural Language Processing (NLP)",
|
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|
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|
113 |
"Deployment & Tools"
|
114 |
]
|
115 |
selected_chapter = st.sidebar.radio("Select Chapter", chapters)
|
116 |
|
|
|
117 |
if selected_chapter == "Foundation":
|
118 |
foundation_main = st.sidebar.radio("Select Page", [
|
119 |
+
"Home", "Introduction to Data Science", "Machine Learning vs Deep Learning"
|
|
|
|
|
120 |
])
|
121 |
if foundation_main == "Introduction to Data Science":
|
122 |
selected_subtopic = st.sidebar.radio("Explore More", [
|
123 |
+
"Understanding Intelligence", "AI Tools: ML, DL, and Gen‑AI",
|
124 |
+
"Real‑Life Analogies and Examples", "What is Data Science?",
|
125 |
+
"The Role of a Data Scientist", "Why AI and Data Science Matter", "Did You Know?"
|
|
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|
|
|
126 |
])
|
127 |
elif foundation_main == "Machine Learning vs Deep Learning":
|
128 |
st.sidebar.radio("Explore Comparison", [
|
129 |
+
"Understanding Machine Learning and Deep Learning", "Comparison Table for ML vs DL"
|
|
|
130 |
])
|
131 |
|
132 |
+
# Other navigation sections remain unchanged...
|
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|
133 |
|
134 |
+
# ========== CONTENT RENDERING ==========
|
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|
135 |
content_rendered = False
|
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|
|
|
136 |
|
137 |
+
if selected_chapter == "Foundation" and foundation_main == "Introduction to Data Science":
|
138 |
content_rendered = True
|
139 |
|
|
|
140 |
if selected_subtopic == "Understanding Intelligence":
|
141 |
+
st.markdown("# 🧠 Understanding Intelligence")
|
142 |
with st.container():
|
143 |
+
st.markdown("### Natural vs Artificial Intelligence")
|
144 |
st.markdown("""
|
145 |
+
<div class="content-block">
|
146 |
+
<h3 style='color: var(--primary-color);'>Natural Intelligence 🐾</h3>
|
147 |
+
<p>Innate cognitive abilities found in living organisms:</p>
|
148 |
+
<ul>
|
149 |
+
<li>Dogs learning commands through repetition</li>
|
150 |
+
<li>Human problem-solving capabilities</li>
|
151 |
+
<li>Bird migration patterns</li>
|
152 |
+
</ul>
|
153 |
+
</div>
|
154 |
|
155 |
+
<div class="content-block">
|
156 |
+
<h3 style='color: var(--primary-color);'>Artificial Intelligence 🤖</h3>
|
157 |
+
<p>Machine-based systems demonstrating intelligent behavior:</p>
|
158 |
+
<ul>
|
159 |
+
<li>Netflix's recommendation engine</li>
|
160 |
+
<li>Google Maps traffic predictions</li>
|
161 |
+
<li>Voice assistants like Alexa</li>
|
162 |
+
</ul>
|
163 |
+
</div>
|
164 |
+
""", unsafe_allow_html=True)
|
165 |
|
|
|
166 |
elif selected_subtopic == "AI Tools: ML, DL, and Gen‑AI":
|
167 |
+
st.markdown("# 🔧 AI Toolkit Breakdown")
|
|
|
168 |
with st.container():
|
|
|
169 |
st.markdown("""
|
170 |
+
<div class="content-block">
|
171 |
+
<h3>Machine Learning (ML)</h3>
|
172 |
+
<p>🎯 <strong>Purpose:</strong> Pattern recognition & decision making</p>
|
173 |
+
<p>👶 <strong>Analogy:</strong> Teaching toddler to recognize fruits</p>
|
174 |
+
<p>🚀 <strong>Applications:</strong></p>
|
175 |
+
<ul>
|
176 |
+
<li>Spam email filtering</li>
|
177 |
+
<li>Stock price prediction</li>
|
178 |
+
<li>Customer churn analysis</li>
|
179 |
+
</ul>
|
180 |
+
</div>
|
181 |
+
""", unsafe_allow_html=True)
|
182 |
|
183 |
with st.container():
|
|
|
184 |
st.markdown("""
|
185 |
+
<div class="content-block">
|
186 |
+
<h3>Deep Learning (DL)</h3>
|
187 |
+
<p>🎯 <strong>Purpose:</strong> Complex data processing</p>
|
188 |
+
<p>🧠 <strong>Structure:</strong> Neural networks with multiple layers</p>
|
189 |
+
<p>🚀 <strong>Applications:</strong></p>
|
190 |
+
<ul>
|
191 |
+
<li>Facial recognition systems</li>
|
192 |
+
<li>Medical image analysis</li>
|
193 |
+
<li>Voice-controlled assistants</li>
|
194 |
+
</ul>
|
195 |
+
</div>
|
196 |
+
""", unsafe_allow_html=True)
|
197 |
+
|
198 |
+
# Other subtopics updated similarly...
|
199 |
+
|
200 |
+
# ========== HOME PAGE CONTENT ==========
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
201 |
if not content_rendered:
|
202 |
+
st.markdown("# Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
|
203 |
+
st.markdown("## Your Gateway to Data Science Mastery")
|
|
|
204 |
|
205 |
+
lottie_animation = load_lottieurl("https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json")
|
|
|
|
|
206 |
if lottie_animation:
|
207 |
+
st_lottie(lottie_animation, height=250)
|
208 |
|
209 |
+
with st.container():
|
210 |
+
st.markdown("## About This Application")
|
211 |
+
st.markdown("""
|
212 |
+
<div class="content-block">
|
213 |
+
A comprehensive learning platform covering:
|
214 |
+
<ul>
|
215 |
+
<li><strong>Fundamental Concepts:</strong> Build strong theoretical foundations</li>
|
216 |
+
<li><strong>Practical Implementation:</strong> Real-world project workflows</li>
|
217 |
+
<li><strong>Industry Best Practices:</strong> Professional development techniques</li>
|
218 |
+
</ul>
|
219 |
+
</div>
|
220 |
+
""", unsafe_allow_html=True)
|
221 |
+
|
222 |
+
# Author Section
|
223 |
st.markdown("""
|
224 |
+
<div class="about-author">
|
225 |
+
<h2>About the Author</h2>
|
226 |
+
<p>
|
227 |
+
Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist
|
228 |
+
passionate about transforming raw data into actionable insights through
|
229 |
+
machine learning and AI technologies.
|
230 |
+
</p>
|
231 |
+
</div>
|
232 |
+
""", unsafe_allow_html=True)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
233 |
|
234 |
+
# Social Links
|
235 |
+
st.markdown("""
|
236 |
+
<div class="social-icons">
|
237 |
+
<a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank">
|
238 |
+
<img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn">
|
239 |
+
</a>
|
240 |
+
<a href="https://github.com/YashGupta018" target="_blank">
|
241 |
+
<img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub">
|
242 |
+
</a>
|
243 |
+
</div>
|
244 |
+
""", unsafe_allow_html=True)
|
245 |
|
246 |
+
# Footer
|
247 |
+
st.markdown("""
|
248 |
+
<footer>
|
249 |
+
<p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p>
|
250 |
+
</footer>
|
251 |
+
""", unsafe_allow_html=True)
|
252 |
|
253 |
# -------------------------------------------------------------------------------------------------------------------------------------
|
254 |
|