pages old commit

pages/01_introduction.py

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+import streamlit as st
+from streamlit_lottie import st_lottie
+import requests
+
+# Function to load Lottie animations
+def load_lottie_url(url: str):
+    r = requests.get(url)
+    if r.status_code == 200:
+        return r.json()
+    else:
+        return None
+
+# Function to display the content of each page
+def show_content(topic):
+    if topic == "Introduction":
+        st.title("Understanding Data Science and Artificial Intelligence 🌟")
+        st.subheader("Overview of AI and Data Science")
+        st.write("""
+        Artificial Intelligence (AI) and Data Science have become buzzwords in today's tech-driven world.
+        But what do they really mean, and why are they so significant? Let’s explore these fascinating concepts step by step!
+        """)
+        
+        # Load the Lottie animation
+        lottie_url = "https://assets4.lottiefiles.com/packages/lf20_tcbkqj.json"
+        animation_data = load_lottie_url(lottie_url)
+        if animation_data:
+            st_lottie(animation_data, speed=1, width=600, height=400)
+        else:
+            st.write("Unable to load animation.")
+        
+    elif topic == "Understanding Intelligence":
+        st.title("Understanding Intelligence")
+        st.subheader("What is Natural Intelligence? 🐾")
+        st.write("""
+        **Definition**: NI refers to the intelligence naturally present in living beings.
+        **Examples**:
+        - A dog learning a trick. 🐕
+        - Humans solving puzzles or making everyday decisions. 🧠
+        """)
+        st.subheader("What is Artificial Intelligence? 🤖")
+        st.write("""
+        **Definition**: Artificial intelligence (AI) is man-made intelligence where machines mimic human intelligence to perform tasks.
+        **Real-Life Examples**:
+        - Netflix recommending shows you’d love. 🎬
+        - Google Maps finding the fastest route. 🗺️
+        - Alexa answering your questions. 🎙️
+        """)
+
+    elif topic == "AI Tools: ML, DL, and Gen-AI":
+        st.title("AI Tools: ML, DL, and Gen-AI")
+        st.subheader("Machine Learning (ML) 🖥️")
+        st.write("""
+        - **What It Does**: ML enables machines to learn from patterns in data and make decisions.
+        - **How It Works**: Similar to teaching a toddler to recognize fruits, ML algorithms process large datasets to "learn" and predict outcomes.
+        - **Real-Life Applications**: Spam email detection 📧, Predicting stock prices 📈.
+        """)
+        st.subheader("Deep Learning (DL) 🤿")
+        st.write("""
+        - **What It Does**: DL uses neural networks to process and analyze complex data.
+        - **How It Works**: DL processes data in layers, enabling machines to perform sophisticated tasks like facial recognition and medical imaging.
+        - **Real-Life Applications**: Self-driving cars 🚗, Virtual assistants like Siri and Alexa. 🎙️
+        """)
+        st.subheader("Generative AI (Gen-AI) 🎨")
+        st.write("""
+        - **What It Does**: Gen-AI enables machines to generate new content like text, images, and music.
+        - **How It Works**: By learning patterns from data, Gen-AI creates outputs that feel original and human-like.
+        - **Real-Life Applications**: ChatGPT (text generation), DALL·E (image creation).
+        """)
+
+    elif topic == "Real-Life Analogies and Examples":
+        st.title("Real-Life Analogies and Examples")
+        st.subheader("Analogy: Tools Are Like Pens and Pencils")
+        st.write("""
+        - ML: Learns patterns (like sketching with a pencil).
+        - DL: Adds depth and detail (like using a pen).
+        - Gen-AI: Creates entirely new outputs (like turning sketches into colorful artwork).
+        """)
+        st.subheader("Learning vs. Generating: The Art Example 👩‍🎨")
+        st.write("""
+        Think of a child learning to draw:
+        - First, they learn the basics of drawing.
+        - Then they generate their own unique artwork.
+        
+        AI follows the same process:
+        - Learning: ML and DL handle this part.
+        - Generating: Gen-AI takes over to create new outputs.
+        """)
+
+    elif topic == "What is Data Science?":
+        st.title("What is Data Science? 📊")
+        st.write("""
+        Data Science is the art of extracting meaningful insights from raw data. It combines AI with statistics, computer science, and domain expertise to solve real-world problems.
+        **Key Components of Data Science**:
+        - **Data Collection**: Gathering information from various sources.
+        - **Data Analysis**: Using tools to find patterns and trends.
+        - **Data Visualization**: Presenting findings through charts and graphs.
+        """)
+
+    elif topic == "The Role of a Data Scientist":
+        st.title("The Role of a Data Scientist")
+        st.write("""
+        A Data Scientist plays a crucial role in:
+        - Building predictive models.
+        - Analyzing customer behavior.
+        - Designing solutions for business challenges.
+        
+        **Tools Used**: Python, R, SQL, Tableau, etc.
+        """)
+
+    elif topic == "Why AI and Data Science Matter":
+        st.title("Why AI and Data Science Matter")
+        st.write("""
+        AI and Data Science are transforming industries by:
+        - Automating tasks.
+        - Enhancing decision-making.
+        - Unlocking creative possibilities.
+
+        **Fun Fact**: By 2030, AI is expected to add $15.7 trillion to the global economy. 🚀
+        """)
+
+    elif topic == "Did You Know?":
+        st.title("Did You Know?")
+        st.write("""
+        **AI is already being used to**:
+        - Detect diseases in medical imaging.
+        - Automate farming for higher crop yields.
+        - Generate movie scripts and music albums.
+        """)
+
+# Set up sidebar navigation
+topics = [
+    "Introduction",
+    "Understanding Intelligence",
+    "AI Tools: ML, DL, and Gen-AI",
+    "Real-Life Analogies and Examples",
+    "What is Data Science?",
+    "The Role of a Data Scientist",
+    "Why AI and Data Science Matter",
+    "Did You Know?"
+]
+
+st.sidebar.title("Topics")
+selection = st.sidebar.radio("Go to", topics)
+
+# Initialize session state with the first topic as the default
+if "page" not in st.session_state:
+    st.session_state.page = topics[0]
+
+# Update session state automatically when sidebar selection changes
+st.session_state.page = selection
+
+# Display the selected content
+show_content(st.session_state.page)

pages/02_ml_vs_dl.py

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+# import streamlit as st
+# from streamlit_lottie import st_lottie
+# import requests
+
+# # Function to load Lottie animations
+# def load_lottie_url(url: str):
+#     response = requests.get(url)
+#     if response.status_code != 200:
+#         return None
+#     return response.json()
+
+# # Load Lottie animations (you can uncomment these as per your need)
+# # lottie_ml = load_lottie_url("https://assets8.lottiefiles.com/packages/lf20_5eyehzdr.json")
+# # lottie_dl = load_lottie_url("https://assets8.lottiefiles.com/packages/lf20_vfnu1k6m.json")
+
+# # Sidebar navigation
+# st.sidebar.title("Navigation")
+# page = st.sidebar.radio("Go to:", ["Home", "ML vs DL", "Comparison Table"])
+
+# # Add Navigate button to update page
+# if st.sidebar.button("Navigate"):
+#     st.session_state.page = page
+
+# # Set page to session state if not already defined
+# if "page" not in st.session_state:
+#     st.session_state.page = page
+
+# # Home page
+# if st.session_state.page == "Home":
+#     st.title("Understanding Machine Learning and Deep Learning")
+#     st.markdown(
+#         """
+#         Welcome to the interactive guide on Machine Learning (ML) and Deep Learning (DL). This space helps you 
+#         explore the differences, capabilities, and applications of ML and DL in a structured manner.
+#         """
+#     )
+#     # If lottie_ml is loaded, display it (uncomment the following line when using animations)
+#     # if lottie_ml:
+#     #     st_lottie(lottie_ml, height=300, key="ml_home")
+
+# # ML vs DL page
+# elif st.session_state.page == "ML vs DL":
+#     st.title("Difference Between Machine Learning (ML) and Deep Learning (DL)")
+
+#     st.subheader("Machine Learning 🖥️")
+#     st.markdown(
+#         """
+#         - Uses statistics to understand patterns in data and make predictions 📊.
+#         - Can learn with less data 📉.
+#         - Handles structured data; unstructured data must be converted to structured form 🔄.
+#         - Requires less memory 🧠💾.
+#         - Trains models in less time ⏱️.
+#         - Can run efficiently on CPUs without requiring powerful hardware 🖥️.
+#         """
+#     )
+    
+#     st.subheader("Deep Learning 🤖")
+#     st.markdown(
+#         """
+#         - Uses neural networks to mimic brain-like learning and decision-making 🧠.
+#         - Requires large amounts of data for better accuracy 🍽️📊.
+#         - Handles both structured and unstructured data like images, text, and audio 🖼️📝🎧.
+#         - Requires more memory and storage 🧠💾.
+#         - Takes more time to train due to complex calculations ⏱️.
+#         - Needs GPUs and advanced hardware for efficient processing 🖥️💡.
+#         """
+#     )
+#     # If lottie_dl is loaded, display it (uncomment the following line when using animations)
+#     # if lottie_dl:
+#     #     st_lottie(lottie_dl, height=300, key="dl_page")
+
+# # Comparison Table page
+# elif st.session_state.page == "Comparison Table":
+#     st.title("Comparison Table: ML vs DL")
+
+#     st.markdown(
+#         """
+#         | **Aspect**              | **Machine Learning (ML)**                        | **Deep Learning (DL)**                          |
+#         |-------------------------|-------------------------------------------------|-------------------------------------------------|
+#         | **Definition**          | Uses algorithms and statistics to learn from data. | Uses neural networks to mimic brain-like decision-making. |
+#         | **Data Dependency**     | Works well with smaller datasets.               | Requires large datasets for better accuracy.   |
+#         | **Data Type**           | Handles structured data only.                   | Handles both structured and unstructured data. |
+#         | **Training Time**       | Requires less time to train.                    | Requires more time to train.                   |
+#         | **Hardware**            | Can run on CPUs.                                | Requires GPUs and advanced hardware.           |
+#         | **Memory Requirement**  | Uses less memory.                               | Requires more memory and storage.              |
+#         """
+#     )
+
+#     st.info(
+#         "Did you know? Deep Learning models are inspired by the human brain, making them exceptionally powerful for tasks like image recognition and natural language processing!"
+#     )
+
+import streamlit as st
+from streamlit_lottie import st_lottie
+import requests
+
+# Function to load Lottie animations
+def load_lottie_url(url: str):
+    response = requests.get(url)
+    if response.status_code != 200:
+        return None
+    return response.json()
+
+# Sidebar navigation
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Go to:", ["Home", "ML vs DL", "Comparison Table"])
+
+# Initialize session state if not already done
+if "page" not in st.session_state:
+    st.session_state.page = page
+else:
+    st.session_state.page = page  # Automatically update the session state when the radio selection changes
+
+# Home page
+if st.session_state.page == "Home":
+    st.title("Understanding Machine Learning and Deep Learning")
+    st.markdown(
+        """
+        Welcome to the interactive guide on Machine Learning (ML) and Deep Learning (DL). This space helps you 
+        explore the differences, capabilities, and applications of ML and DL in a structured manner.
+        """
+    )
+
+# ML vs DL page
+elif st.session_state.page == "ML vs DL":
+    st.title("Difference Between Machine Learning (ML) and Deep Learning (DL)")
+
+    st.subheader("Machine Learning 🖥️")
+    st.markdown(
+        """
+        - Uses statistics to understand patterns in data and make predictions 📊.
+        - Can learn with less data 📉.
+        - Handles structured data; unstructured data must be converted to structured form 🔄.
+        - Requires less memory 🧠💾.
+        - Trains models in less time ⏱️.
+        - Can run efficiently on CPUs without requiring powerful hardware 🖥️.
+        """
+    )
+    
+    st.subheader("Deep Learning 🤖")
+    st.markdown(
+        """
+        - Uses neural networks to mimic brain-like learning and decision-making 🧠.
+        - Requires large amounts of data for better accuracy 🍽️📊.
+        - Handles both structured and unstructured data like images, text, and audio 🖼️📝🎧.
+        - Requires more memory and storage 🧠💾.
+        - Takes more time to train due to complex calculations ⏱️.
+        - Needs GPUs and advanced hardware for efficient processing 🖥️💡.
+        """
+    )
+
+# Comparison Table page
+elif st.session_state.page == "Comparison Table":
+    st.title("Comparison Table: ML vs DL")
+
+    st.markdown(
+        """
+        | **Aspect**              | **Machine Learning (ML)**                        | **Deep Learning (DL)**                          |
+        |-------------------------|-------------------------------------------------|-------------------------------------------------|
+        | **Definition**          | Uses algorithms and statistics to learn from data. | Uses neural networks to mimic brain-like decision-making. |
+        | **Data Dependency**     | Works well with smaller datasets.               | Requires large datasets for better accuracy.   |
+        | **Data Type**           | Handles structured data only.                   | Handles both structured and unstructured data. |
+        | **Training Time**       | Requires less time to train.                    | Requires more time to train.                   |
+        | **Hardware**            | Can run on CPUs.                                | Requires GPUs and advanced hardware.           |
+        | **Memory Requirement**  | Uses less memory.                               | Requires more memory and storage.              |
+        """
+    )
+
+    st.info(
+        "Did you know? Deep Learning models are inspired by the human brain, making them exceptionally powerful for tasks like image recognition and natural language processing!"
+    )

pages/03_life_cycle_of_ml_project.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+def draw_lifecycle_diagram():
+    # Labels and colors for the lifecycle phases
+    labels = [
+        "1. Gathering Data", 
+        "2. Data Preparation", 
+        "3. Data Wrangling", 
+        "4. Analyze Data", 
+        "5. Train Model", 
+        "6. Test Model", 
+        "7. Deployment"
+    ]
+    colors = [
+        "#3CB371", "#8FBC8F", "#00CED1", "#1E90FF", 
+        "#6A5ACD", "#FF8C00", "#DC143C"
+    ]
+
+    # Create a figure and axis with equal aspect ratio
+    fig, ax = plt.subplots(figsize=(9, 9), subplot_kw={"aspect": "equal"})
+    size = 0.3  # Width of the pie sections
+
+    # Create pie sections for the lifecycle phases
+    wedges, _ = ax.pie(
+        [1] * len(labels), 
+        colors=colors, 
+        radius=1, 
+        startangle=90, 
+        wedgeprops=dict(width=size, edgecolor='w')
+    )
+
+    # Add text labels around the circle
+    for i, wedge in enumerate(wedges):
+        # Calculate the angle for the label placement
+        angle = (wedge.theta2 - wedge.theta1) / 2.0 + wedge.theta1
+        x = np.cos(np.deg2rad(angle))
+        y = np.sin(np.deg2rad(angle))
+
+        # Add label text
+        ax.text(
+            1.2 * x, 1.2 * y, labels[i],
+            ha="center", va="center", fontsize=10, weight="bold",
+            bbox=dict(boxstyle="round,pad=0.3", facecolor=colors[i], edgecolor="w")
+        )
+
+    # Add center text with a descriptive title
+    ax.text(
+        0, 0, "Machine Learning\nLifecycle", 
+        ha="center", va="center", fontsize=16, weight="bold", color="black",
+        bbox=dict(boxstyle="round,pad=0.5", facecolor="white", edgecolor="black")
+    )
+
+    # Clean up the diagram style
+    ax.set(aspect="equal", xticks=[], yticks=[], title="Machine Learning Lifecycle")
+    return fig
+
+# Save the diagram or display it in Streamlit
+if __name__ == "__main__":
+    # Display the diagram
+    fig = draw_lifecycle_diagram()
+    plt.show()

pages/04_data.py

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+import streamlit as st
+
+def main():
+    st.set_page_config(page_title="Data Demystified", page_icon="📊", layout="wide")
+    
+    # Custom CSS for better engagement
+    st.markdown("""
+    <style>
+    .highlight-box {
+        padding: 1rem;
+        border-radius: 10px;
+        background: #f0f2f6;
+        margin: 1rem 0;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    .fun-fact {
+        color: #2e7d32;
+        font-weight: 500;
+    }
+    .emoji-header {
+        font-size: 2.5rem !important;
+        margin-bottom: 1rem;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+    pages = {
+        "🏠 Introduction": intro_page,
+        "📚 Data Types Overview": types_page,
+        "🗂️ Structured Data": structured_page,
+        "🧩 Semi-Structured Data": semi_structured_page,
+        "🎨 Unstructured Data": unstructured_page
+    }
+
+    with st.sidebar:
+        st.title("Navigation")
+        page = st.radio("Go to", list(pages.keys()))
+    
+    pages[page]()
+
+def intro_page():
+    st.markdown('<div class="emoji-header">📊 Welcome to Data Science Fundamentals</div>', unsafe_allow_html=True)
+    
+    col1, col2 = st.columns([3, 2])
+    with col1:
+        st.write("""
+        ### What is Data Science?
+        Data Science is the art of extracting meaningful insights from raw data - 
+        like being a digital detective uncovering hidden patterns in the numbers!
+        
+        **Did you know?** 🤔  
+        <span class="fun-fact">Every day, we create 2.5 quintillion bytes of data - 
+        that's equivalent to 250,000 Libraries of Congress!</span>
+        """, unsafe_allow_html=True)
+        
+        st.markdown("""
+        ### Why It Matters:
+        - Predict future trends 🌟
+        - Solve complex problems 🧩
+        - Power AI innovations 🤖
+        - Drive business decisions 💼
+        """)
+    
+    with col2:
+        st.image("https://cdn-uploads.huggingface.co/production/uploads/64c972774515835c4dadd754/qpmLGi47ucDCWYEi8eZhE.png", 
+                caption="Data is Everywhere!", width=300)
+
+def types_page():
+    st.header("📦 The Three Data Superheroes")
+    
+    with st.expander("🔍 Quick Comparison"):
+        st.table({
+            "Type": ["Structured", "Semi-Structured", "Unstructured"],
+            "Organization": ["Perfectly Organized", "Partially Organized", "Chaotic Creativity"],
+            "Examples": ["SQL Databases, Excel", "JSON, XML, CSV", "Images, Videos, Text"]
+        })
+    
+    cols = st.columns(3)
+    data_types = [
+        ("🗂️ Structured", "Neat rows & columns", "#e3f2fd"),
+        ("🧩 Semi-Structured", "Flexible tags & markers", "#f0f4c3"),
+        ("🎨 Unstructured", "Creative free-form", "#ffcdd2")
+    ]
+    
+    for col, (icon, desc, color) in zip(cols, data_types):
+        with col:
+            st.markdown(f"""
+            <div style="background: {color}; padding: 1rem; border-radius: 10px;">
+                <h3>{icon} {desc.split()[0]}</h3>
+                <p>{' '.join(desc.split()[1:])}</p>
+            </div>
+            """, unsafe_allow_html=True)
+
+def structured_page():
+    st.header("🗂️ Structured Data: The Organized Perfectionist")
+    
+    with st.container():
+        st.markdown("""
+        ### Characteristics:
+        - Strict schema 🔒
+        - Tabular format 📊
+        - Easy to query 🔍
+        
+        **Did you know?** 🤔  
+        <span class="fun-fact">The first computerized database appeared in 1963 - 
+        it weighed more than a car! 🚗💾</span>
+        """, unsafe_allow_html=True)
+        
+        st.image("https://cdn-uploads.huggingface.co/production/uploads/64c972774515835c4dadd754/dSbyOXaQ6N_Kg2TLxgEyt.png", 
+                width=400, caption="Structured Data Example")
+        
+        with st.expander("💡 Real-World Examples"):
+            st.markdown("""
+            - Financial records 💰
+            - Inventory systems 📦
+            - Student databases 🎓
+            - Railway timetables 🚄
+            """)
+
+def semi_structured_page():
+    st.header("🧩 Semi-Structured Data: The Flexible Friend")
+    
+    with st.container():
+        st.markdown("""
+        ### Why It's Special:
+        - Partial organization 🎭
+        - Self-describing formats 📝
+        - Web-friendly 🌐
+        
+        **Did you know?** 🤔  
+        <span class="fun-fact">JSON was created in 2001 - 
+        the same year Wikipedia launched! 🎂📚</span>
+        """, unsafe_allow_html=True)
+        
+        cols = st.columns(2)
+        with cols[0]:
+            st.code("""
+            {
+              "name": "Data Hero",
+              "skills": ["JSON", "XML", "CSV"],
+              "mission": "Bring order to chaos!"
+            }
+            """, language="json")
+        
+        with cols[1]:
+            st.markdown("""
+            ### Common Formats:
+            - JSON (Web APIs) 🌍
+            - XML (Document markup) 📄
+            - CSV (Spreadsheet data) 📋
+            - Email headers 📧
+            """)
+
+def unstructured_page():
+    st.header("🎨 Unstructured Data: The Creative Chaos")
+    
+    with st.container():
+        st.markdown("""
+        ### The Wild West of Data:
+        - No predefined format 🎨
+        - Human-friendly formats 😊
+        - Requires AI processing 🤖
+        
+        **Did you know?** 🤔  
+        <span class="fun-fact">90% of all digital data is unstructured - 
+        that's like having 1000 Netflix movies for every person on Earth! 🍿🌍</span>
+        """, unsafe_allow_html=True)
+        
+        st.image("https://cdn-uploads.huggingface.co/production/uploads/64c972774515835c4dadd754/xhaNBRanDaj8esumqo9hl.png", 
+                width=400, caption="Unstructured Data in Action")
+        
+        with st.expander("🌐 Modern Applications"):
+            st.markdown("""
+            - Facial recognition systems 👩💻
+            - Voice assistants 🗣️
+            - Medical image analysis 🏥
+            - Social media monitoring 📱
+            """)
+
+if __name__ == "__main__":
+    main()