import streamlit as st
from streamlit_lottie import st_lottie  # For Lottie animations
import requests

# Function to load Lottie animations
def load_lottie_url(url):
    response = requests.get(url)
    if response.status_code == 200:
        return response.json()
    return None

# Custom CSS for enhanced styling
st.markdown(
    """
    <style>
    .stApp {
        background: linear-gradient(135deg, #1f1c2c, #928dab);
        color: #E0E0E0;
    }
    .stTitle {
        text-align: center;
        color: #76FF03;
    }
    .css-1d391kg p, .css-1v0mbdj p {
        color: #B0BEC5;
    }
    .stButton > button:hover {
        background-color: #4CAF50;
        color: white;
        transform: scale(1.05);
    }
    </style>
    """,
    unsafe_allow_html=True,
)

# App Title
st.title("📊 Data Types Explorer")

# Sidebar for navigation
st.sidebar.subheader("🔍 Explore Data Types")
data_type = st.sidebar.selectbox(
    "Select a Type of Data:",
    ["Structured Data", "Unstructured Data", "Semi-Structured Data"]
)

# Structured Data Section
if data_type == "Structured Data":
    st.subheader("📋 Structured Data")
    st.write("""
    **Definition**: Structured data is organized and stored in a predefined format like rows and columns, making it easily searchable and manageable.
    """)
    st.markdown("""
    **Features**:
    - Fixed schema (e.g., tables with defined columns and data types).
    - Easy to process using query languages like SQL.
    - Relationships between data points are well-defined.
    """)
    st.markdown("**Examples**: Excel Files 📊, MySQL Databases 💾")

    if st.button("Learn About Excel Files 📂"):
        st.subheader("Excel Files")
        st.write("""
        Excel files store structured data in rows and columns, making them ideal for analysis, visualization, and calculations.
        """)
        st.code("""
import pandas as pd
df = pd.read_excel('file.xlsx')
df.to_csv('file.csv', index=False)
        """, language="python")

    if st.button("Learn About MySQL Databases 💻"):
        st.subheader("MySQL Databases")
        st.write("""
        MySQL is a relational database management system for storing structured data in tables. SQL is used to query and manipulate data.
        """)

# Unstructured Data Section
elif data_type == "Unstructured Data":
    st.subheader("🗂️ Unstructured Data")
    st.write("""
    **Definition**: Unstructured data lacks any predefined structure or schema, making it the most difficult to organize and analyze.
    """)
    st.markdown("**Examples**: Images 🖼️, Videos 🎥, Audio 🔊, Text 📝")

    if st.button("Learn About Images 📷"):
        st.subheader("Working with Images")
        st.write("""
        Images are a form of unstructured data represented as grids of pixels. Common formats include JPG, PNG, and BMP.
        """)

    if st.button("Learn About Videos 🎥"):
        st.subheader("Working with Videos")
        st.write("""
        Videos are sequences of frames used in various applications like object detection and activity recognition. Common formats include MP4 and AVI.
        """)

# Semi-Structured Data Section
elif data_type == "Semi-Structured Data":
    st.subheader("📄 Semi-Structured Data")
    st.write("""
    **Definition**: Semi-structured data is partially organized using tags or key-value pairs. It is more flexible than structured data.
    """)
    st.markdown("**Examples**: JSON Files 📑, XML Files 🌐")

    if st.button("Learn About JSON Files 📄"):
        st.subheader("JSON Files")
        st.write("""
        JSON stores data as key-value pairs and is commonly used in APIs and web development.
        """)

    if st.button("Learn About XML Files 📄"):
        st.subheader("XML Files")
        st.write("""
        XML stores data in a tree structure with nested elements. It is used in configuration files and data exchange.
        """)

# Footer
st.write("This app provides a clear understanding of different data types. 🎉")