pages/introds.py

import streamlit as st

# Static background image URL


# Dynamic CSS
custom_css = """
<style>
    html, body, [data-testid="stAppViewContainer"] {
        background: linear-gradient(
            rgba(0, 0, 0, 0.6), 
            rgba(0, 0, 0, 0.6)
        ), 
        url("https://cdn.pixabay.com/photo/2023/11/23/17/47/sunset-7704533_1280.jpg") no-repeat center center fixed;
        background-size: cover;
        font-family: Arial, sans-serif;
        color: #ffffff;
    }
    h1 {
        color: #ffffff;
        text-align: center;
        font-size: 2rem; 
        margin-top: 2px;
        text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7); 
    }
    .division {
        margin: 20px auto;
        padding: 20px;
        background: rgba(255, 255, 255, 0.1); 
        border-radius: 10px;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); 
    }
    .division h2 {
        color: #ffffff;
        margin-bottom: 10px;
        font-size: 2rem; 
        text-shadow: 1px 1px 3px rgba(0, 0, 0, 0.7);
    }
    .division p, .division ul li {
        font-size: 1.2rem; 
        line-height: 1.7; 
    }
</style>
"""

# Inject CSS into Streamlit app
st.markdown(custom_css, unsafe_allow_html=True)

# Header Section
st.markdown("<h1>Welcome to Data Science</h1>", unsafe_allow_html=True)

# Content for Data Science
st.markdown(
    """
    <div class="division">
        <h2>What is Data Science?</h2>
        <p>
            Data science combines techniques from statistics, computer science, mathematics, and domain expertise 
            to analyze and interpret data effectively. It transforms raw data into actionable insights, driving smarter 
            decisions across industries.
        </p>
        <ul>
            <li>Analyzing customer reviews to identify product sentiment</li>
            <li>Techniques Used: Natural Language Processing (NLP), sentiment analysis</li>
            <li>Optimizing delivery routes for logistics companies</li>
            <li>Techniques Used: Graph algorithms, optimization techniques</li>
        </ul>
    </div>
    <div class="division">
        <h2>Key Steps in Data Science</h2>
        <p>Data Science involves several steps, such as:</p>
        <ul>
            <li><strong>Understanding the Problem:</strong> Grasp the problem's nature and define clear objectives.</li>
            <li><strong>Data Collection:</strong> Gather relevant data from multiple sources.</li>
            <li><strong>Data Cleaning:</strong> Handle inconsistencies, missing values, and prepare data for analysis.</li>
            <li><strong>Exploratory Data Analysis (EDA):</strong> Use visualization and summary statistics to understand data.</li>
            <li><strong>Model Deployment:</strong> Implement models in a production environment for real-time decision-making.</li>
        </ul>
    </div>
    """,
    unsafe_allow_html=True
)

# Content for Deep Learning
st.markdown(
    """
    <div class="division">
        <h2>What is Deep Learning?</h2>
        <p>
            Deep learning is an artificial intelligence (AI) method that teaches computers to process data in a way inspired by the human brain. 
            Deep learning models can recognize complex patterns in images, text, sounds, and other data to produce accurate insights and predictions. 
            You can use deep learning methods to automate tasks that typically require human intelligence, such as describing images or transcribing sound files into text.
        </p>
        <ul>
            <li><strong>Entertainment:</strong> Companies like Netflix and Spotify recommend relevant content based on user preferences.</li>
            <li><strong>Natural Language Processing:</strong> Enables machines to understand and process human language, such as in translation or chatbots.</li>
            <li><strong>Fraud Detection:</strong> Helps detect and prevent fraudulent activities using predictive analytics (e.g., PayPal).</li>
        </ul>
    </div>
    <div class="division">
        <h2>Key Steps in Deep Learning</h2>
        <ul>
            <li><strong>Data Acquisition:</strong> Gather data relevant to the problem you're solving. The amount of data depends on model complexity.</li>
            <li><strong>Data Preprocessing:</strong> Split the data into training, validation, and test sets, and preprocess it for consistency.</li>
            <li><strong>Model Building and Training:</strong> Define the neural network architecture and expose it to labeled data for training.</li>
        </ul>
    </div>
    """,
    unsafe_allow_html=True
)


# Content for AI
st.markdown(
    """
    <div class="division">
        <h2>What is Artificial Intelligence?</h2>
        <p>
            Artificial intelligence is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze.AI is a broad field that encompasses many different disciplines, including computer science, data analytics and statistics, hardware and software engineering, linguistics, neuroscience, and even philosophy and psychology.On an operational level for business use, AI is a set of technologies that are based primarily on machine learning and deep learning, used for data analytics, predictions and forecasting, object categorization, natural language processing, recommendations, intelligent data retrieval, and more.
        </p>
        <ul>
            <li><strong>Retail:</strong> AI is being used to personalize the shopping experience, recommend products, and manage inventory.</li>
            <li><strong>Government:</strong> AI is being used to improve public safety, detect crime, and provide citizen services.</li>
            <li><strong>Transportation:</strong> AI is being used to develop self-driving cars and improve traffic management.</li>
        </ul>
    </div>
    <div class="division">
        <h2>Key Steps in Artificial Intelligence</h2>
        <ul>
            <li><strong>Define a Use Case:</strong> Clearly define the problems or challenges you want AI to solve. The more specific the goal, the better the chance of success.</li>
            <li><strong>Data Collection:</strong> The quality and quantity of data can directly impact the model's performance.</li>
            <li><strong>Build the Model:</strong> Identify a clear objective for what you want to do with the data.</li>
            <li><strong>Train the Model:</strong> Train the model or algorithm.</li>
            <li><strong>Evaluate the AI System:</strong> Evaluate the AI system's performance.</li>
            <li><strong>Deploy the Model:</strong> Move the model to production. Start with a limited rollout and gather feedback from enterprise users.</li>
        </ul>
    </div>
    """,
    unsafe_allow_html=True
)