Deleted Files

home.py

@@ -1,137 +0,0 @@
-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)

pages/01_home.py

@@ -1,154 +0,0 @@
-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 if not already done
-if "page" not in st.session_state:
-    st.session_state.page = selection
-
-# Update session state if sidebar selection is changed
-if st.sidebar.button("Navigate"):
-    st.session_state.page = selection
-
-# Display the selected content
-show_content(st.session_state.page)

pages/03_ml_vs_dl.py

@@ -1,91 +0,0 @@
-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!"
-    )

pages/05_life_cycle_of_ml.py

@@ -1,115 +0,0 @@
-import streamlit as st
-
-# Configure the Streamlit app
-st.set_page_config(
-    page_title="ML Project Lifecycle",
-    page_icon="📊",
-    layout="wide"
-)
-
-# Sidebar navigation
-st.sidebar.title("Navigation")
-page = st.sidebar.radio(
-    "Choose a page:",
-    [
-        "Overview of ML Project Lifecycle",
-        "1. Problem Definition",
-        "2. Data Collection and Exploration",
-        "3. Data Preprocessing",
-        "4. Feature Engineering",
-        "5. Model Selection and Training",
-        "6. Model Evaluation",
-        "7. Model Deployment"
-    ]
-)
-
-# Main page content
-if page == "Overview of ML Project Lifecycle":
-    st.markdown("<h1 style='text-align: center; color: white;'>Machine Learning Project Lifecycle</h1>", unsafe_allow_html=True)
-    st.markdown("""
-        This application provides a comprehensive overview of the **Machine Learning Project Lifecycle**. 
-        Explore the different stages of building an ML project by navigating through the pages on the left.
-
-        ### The ML Project Lifecycle Includes:
-        1. **Problem Definition**
-        2. **Data Collection and Exploration**
-        3. **Data Preprocessing**
-        4. **Feature Engineering**
-        5. **Model Selection and Training**
-        6. **Model Evaluation**
-        7. **Model Deployment**
-    """)
-    st.image(
-        "https://www.researchgate.net/profile/Hazem-El-Sayed/publication/335522887/figure/fig1/AS:798613045346310@1567359745828/Typical-ML-project-lifecycle.ppm",
-        caption="Typical ML Project Lifecycle",
-        use_column_width=True
-    )
-    st.markdown("Navigate through the stages using the sidebar to explore each phase in detail.")
-
-# Pages for individual lifecycle stages
-elif page == "1. Problem Definition":
-    st.title("1. Problem Definition")
-    st.markdown("""
-        In this stage, the focus is on:
-        - Understanding the business context and objectives.
-        - Identifying the problem you are solving.
-        - Breaking the problem into manageable sub-problems.
-    """)
-
-elif page == "2. Data Collection and Exploration":
-    st.title("2. Data Collection and Exploration")
-    st.markdown("""
-        This stage involves:
-        - Gathering data from relevant sources (APIs, databases, web scraping, etc.).
-        - Performing Exploratory Data Analysis (EDA) to understand patterns, trends, and outliers.
-        - Visualizing data using tools like `Matplotlib` and `Seaborn`.
-    """)
-
-elif page == "3. Data Preprocessing":
-    st.title("3. Data Preprocessing")
-    st.markdown("""
-        Preprocessing involves:
-        - Cleaning the data (handling missing values, duplicates, and errors).
-        - Scaling and normalizing features.
-        - Encoding categorical variables into numerical formats.
-    """)
-
-elif page == "4. Feature Engineering":
-    st.title("4. Feature Engineering")
-    st.markdown("""
-        Feature engineering includes:
-        - Selecting the most relevant features for the model.
-        - Creating new features using domain knowledge.
-        - Reducing data dimensionality using techniques like PCA.
-    """)
-
-elif page == "5. Model Selection and Training":
-    st.title("5. Model Selection and Training")
-    st.markdown("""
-        This phase involves:
-        - Choosing the right machine learning algorithm based on the problem type (classification, regression, clustering, etc.).
-        - Training the model using the training dataset.
-        - Fine-tuning hyperparameters to optimize performance.
-    """)
-
-elif page == "6. Model Evaluation":
-    st.title("6. Model Evaluation")
-    st.markdown("""
-        Model evaluation ensures:
-        - Validation using techniques like cross-validation.
-        - Testing on unseen data to estimate real-world performance.
-        - Comparing multiple models and choosing the best one based on metrics like accuracy, F1-score, etc.
-    """)
-
-elif page == "7. Model Deployment":
-    st.title("7. Model Deployment")
-    st.markdown("""
-        In this stage:
-        - The model is deployed into production using tools like Flask, FastAPI, or cloud platforms (AWS, GCP, Azure).
-        - Monitoring the model to track its performance over time.
-        - Setting up pipelines for periodic retraining with new data.
-    """)
-
-# Footer
-st.sidebar.markdown("---")
-st.sidebar.markdown("<p style='text-align: center;'>© 2024 Yash Harish Gupta</p>", unsafe_allow_html=True)

pages/image_augmentation.py

@@ -1,183 +0,0 @@
-import streamlit as st
-from PIL import Image, ImageOps
-import numpy as np
-import io
-
-# Improved App Styling
-st.set_page_config(
-    page_title="Modern Image Augmentation",
-    page_icon="✨",
-    layout="wide",
-)
-
-# Custom CSS for modern look
-st.markdown(
-    """
-    <style>
-    /* App Background */
-    .stApp {
-        background: linear-gradient(135deg, #1f1f1f, #000000);
-        color: #ffffff;
-    }
-
-    /* Title Styling */
-    h1 {
-        text-align: center;
-        color: #00ffcc;
-        font-family: 'Arial Black', sans-serif;
-    }
-
-    /* Sidebar Styling */
-    [data-testid="stSidebar"] {
-        background-color: #1c1c1c;
-        border-right: 2px solid #00ffcc;
-    }
-
-    /* Slider Styling */
-    [class^="css-qbe2hs"] {
-        color: #00ffcc !important;
-    }
-
-    /* Buttons */
-    button {
-        background-color: #00ffcc !important;
-        border: none;
-        font-weight: bold;
-    }
-
-    button:hover {
-        background-color: #00e6b8 !important;
-    }
-
-    /* Image Uploader */
-    [data-testid="stFileUploadDropzone"] {
-        background: #1c1c1c;
-        border: 2px dashed #00ffcc;
-        border-radius: 10px;
-    }
-
-    /* Augmentation Options Styling */
-    [class^="stRadio"] > div > label {
-        color: #ffffff;
-    }
-    </style>
-    """,
-    unsafe_allow_html=True,
-)
-
-st.title("✨ Modern Image Data Augmentation ✨")
-
-# File uploader for image
-uploaded_image = st.file_uploader("Upload an Image to Get Started", type=["jpg", "png", "jpeg"])
-
-if uploaded_image:
-    try:
-        image = Image.open(uploaded_image)
-        col1, col2 = st.columns(2)
-
-        # Display original image
-        with col1:
-            st.subheader("Original Image")
-            st.image(image, caption="Uploaded Image", use_container_width=True)
-
-        st.subheader("✨ Select an Augmentation")
-
-        # Augmentation options
-        augmentation_option = st.radio(
-            "Choose an Augmentation Type:",
-            [
-                "None",
-                "Cropping",
-                "Flipping",
-                "Rotation",
-                "Zooming In",
-                "Zooming Out",
-                "Translation",
-                "Shearing",
-            ],
-        )
-
-        augmented_image = image
-
-        # Augmentation Logic
-        if augmentation_option == "Cropping":
-            st.info("Adjust the crop sliders to modify the image.")
-            left = st.slider("Left Crop", 0, image.width // 2, 0)
-            top = st.slider("Top Crop", 0, image.height // 2, 0)
-            right = st.slider("Right Crop", 0, image.width // 2, 0)
-            bottom = st.slider("Bottom Crop", 0, image.height // 2, 0)
-            augmented_image = image.crop((left, top, image.width - right, image.height - bottom))
-
-        elif augmentation_option == "Flipping":
-            flip_option = st.selectbox("Choose Flip Type", ["Horizontal", "Vertical"])
-            if flip_option == "Horizontal":
-                augmented_image = ImageOps.mirror(image)
-            elif flip_option == "Vertical":
-                augmented_image = ImageOps.flip(image)
-
-        elif augmentation_option == "Rotation":
-            rotation_angle = st.slider("Rotation Angle (°)", 0, 360, 0)
-            if rotation_angle:
-                augmented_image = image.rotate(rotation_angle, expand=True)
-
-        elif augmentation_option == "Zooming In":
-            zoom_factor = st.slider("Zoom Factor (%)", 100, 200, 100) / 100.0
-            if zoom_factor != 1.0:
-                width, height = image.size
-                new_width = int(width * zoom_factor)
-                new_height = int(height * zoom_factor)
-                zoomed_image = image.resize((new_width, new_height))
-                augmented_image = zoomed_image.crop(((new_width - width) // 2, (new_height - height) // 2,
-                                                     (new_width + width) // 2, (new_height + height) // 2))
-
-        elif augmentation_option == "Zooming Out":
-            zoom_factor = st.slider("Zoom Factor (%)", 50, 100, 100) / 100.0
-            if zoom_factor != 1.0:
-                width, height = image.size
-                new_width = int(width * zoom_factor)
-                new_height = int(height * zoom_factor)
-                zoomed_image = image.resize((new_width, new_height))
-                padded_image = Image.new("RGB", (width, height), (0, 0, 0))  # Black padding
-                padded_image.paste(zoomed_image, ((width - new_width) // 2, (height - new_height) // 2))
-                augmented_image = padded_image
-
-        elif augmentation_option == "Translation":
-            translation_x = st.slider("Horizontal Shift (Pixels)", -100, 100, 0)
-            translation_y = st.slider("Vertical Shift (Pixels)", -100, 100, 0)
-            if translation_x or translation_y:
-                translation_matrix = (1, 0, translation_x, 0, 1, translation_y)
-                augmented_image = image.transform(
-                    image.size, Image.AFFINE, translation_matrix, fillcolor=(0, 0, 0)
-                )
-
-        elif augmentation_option == "Shearing":
-            shear_angle = st.slider("Shearing Angle (°)", -45, 45, 0)
-            if shear_angle:
-                shear_matrix = (1, np.tan(np.radians(shear_angle)), 0, 0, 1, 0)
-                augmented_image = image.transform(
-                    (image.width + abs(int(image.height * np.tan(np.radians(shear_angle)))), image.height),
-                    Image.AFFINE,
-                    shear_matrix,
-                    fillcolor=(0, 0, 0),
-                )
-
-        # Display Augmented Image
-        with col2:
-            st.subheader(f"{augmentation_option} Applied")
-            st.image(augmented_image, caption="Augmented Image", use_container_width=True)
-
-        # Download button
-        buffer = io.BytesIO()
-        augmented_image.save(buffer, format="PNG")
-        buffer.seek(0)
-        st.download_button(
-            "🎉 Download Augmented Image 🎉",
-            buffer,
-            file_name="augmented_image.png",
-            mime="image/png",
-        )
-
-    except Exception as e:
-        st.error(f"An error occurred: {e}")
-else:
-    st.warning("Please upload an image to begin.")

pages/life_cycle_of_ml_project.py

@@ -1,65 +0,0 @@
-<<<<<<< HEAD
-=======
-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()
->>>>>>> 8f9a0e1fa29de18412c739d9d5c3da24894df34d