import streamlit as st

# Static background image URL


st.markdown(
    """
    <style>
    .stApp {
        background-image: url("https://huggingface.co/spaces/ronakreddy18/Zerotoheroinmachinelearning/resolve/main/ai-technology-brain-background-digital-transformation-concept.jpg");
        background-size: cover;
        background-repeat: no-repeat;
        background-attachment: fixed;
    }
    </style>
    """,
    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
)
# Content for ML
st.markdown(
    """
    <div class="division">
        <h2>What is Machine Learning ?</h2>
        <p>
         Machine learning (ML) is a branch of 
           and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.
     </p>
     <ul>
          <li><strong>Traffic prediction:</strong>Real Time location of the vehicle form Google Map app and sensors.Average time has taken on past days at the same time..</li>
          <li><strong>Self-driving cars:</strong>One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on self-driving car. It is using unsupervised learning method to train the car models to detect people and objects while driving.</li>
          <li><strong>Stock Market trading:</strong>Machine learning is widely used in stock market trading. In the stock market, there is always a risk of up and downs in shares, so for this machine learning's long short term memory neural network is used for the prediction of stock market trends..</li>
       </ul>
    </div>
    <div class="division">
        <h2>Methods in MACHINE LEARNING</h2>
        <ul>
            <li><strong>Superwised ML:</strong>In supervised learning, the AI model is trained based on the given input and its expected output, i.e., the label of the input. The model creates a mapping equation based on the inputs and outputs and predicts the label of the inputs in the future based on that mapping equation.</li>
            <li><strong>Unsuperwised ML:</strong>In unsupervised learning, the AI model is trained only on the inputs, without their labels. The model classifies the input data into classes that have similar features. The label of the input is then predicted in the future based on the similarity of its features with one of the classes..</li>
            <li><strong>Reinforcement ML:</strong>In reinforcement learning, the AI model tries to take the best possible action in a given situation to maximize the total profit. The model learns by getting feedback on its past outcomes..</li>
                </ul>
    </div>
    """,
    unsafe_allow_html=True
)
# Content for Generative AI
st.markdown(
    """
    <div class="division">
        <h2>What is Generative AI  ?</h2>
        <p>
           Generative AI is artificial intelligence designed to create unique text or image results in response to user prompts. 
           The technology uses machine learning to return an output based on the user’s prompt. 
           AI engineers train the technology using large data sets, which the model consults when determining the best possible answer to a prompt.
           Another way to look at generative AI is as a form of predictive artificial intelligence.
           Based on the information provided, generative AI will predict which words and in which order will give the best answer to the user's prompts.
          </ul>
    </div>
    <div class="division">
        <h2>Apllications with GenAI in daily life </h2>
        <ul>
           <li><strong>Advertising and marketing:</strong>Generative artificial intelligence offers many solutions to professionals working in advertising and marketing, such as generating text and images needed for marketing or finding new ways to interact with customers. Here are some examples of generative AI applications in advertising and marketing.</li>
           <li><strong>Media and entertainment:</strong>Media and entertainment could embrace generative AI in several ways, considering the industry primarily engages in the same task as the tech: generating unique content. Generative AI can help create and edit visual content, create short highlight videos of sporting events, and make working with content management systems easier..</li>
           <li><strong>Manufacturing:</strong>In manufacturing, professionals can use generative AI to look for ways to improve efficiency, anticipate maintenance needs before they cause problems, help engineers create better designs faster, and create a more resilient supply chain. Let’s explore these potential manufacturing solutions.</li>
               </ul>
    </div>
    """,
    unsafe_allow_html=True
)