import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random
import requests

st.markdown("""
    <style>
    /* Set a soft background color */
    body {
        background-color: #eef2f7;
    }
    /* Style for main title */
    h1 {
        color: #00FFFF;
        font-family: 'Roboto', sans-serif;
        font-weight: 700;
        text-align: center;
        margin-bottom: 25px;
    }
    /* Style for headers */
    h2 {
        color: #FFFACD;
        font-family: 'Roboto', sans-serif;
        font-weight: 600;
        margin-top: 30px;
    }
    
    /* Style for subheaders */
     h3 {
        color: #ba95b0;
        font-family: 'Roboto', sans-serif;
        font-weight: 500;
        margin-top: 20px;
    }
    .custom-subheader {
        color: #00FFFF;
        font-family: 'Roboto', sans-serif;
        font-weight: 600;
        margin-bottom: 15px;
    }
    /* Paragraph styling */
    p {
        font-family: 'Georgia', serif;
        line-height: 1.8;
        color: #FFFFFF;
        margin-bottom: 20px;
    }
    /* List styling with checkmark bullets */
    .icon-bullet {
        list-style-type: none;
        padding-left: 20px;
    }
    .icon-bullet li {
        font-family: 'Georgia', serif;
        font-size: 1.1em;
        margin-bottom: 10px;
        color: #FFFFF0;
    }
    .icon-bullet li::before {
        content: "✔️";
        padding-right: 10px;
        color: #b3b3ff;
    }
    /* Sidebar styling */
    .sidebar .sidebar-content {
        background-color: #ffffff;
        border-radius: 10px;
        padding: 15px;
    }
    .sidebar h2 {
        color: #495057;
    }
    /* Custom button style */
    .streamlit-button {
        background-color: #00FFFF;
        color: #000000;
        font-weight: bold;
    }
    </style>
    """, unsafe_allow_html=True)

st.title("Semi-Structured Data 📊📜")
st.markdown("""
Semi-structured data is a type of data that doesn’t follow strict rules like a table but still has some structure, like labels or tags, to organize it. Examples include:
<ul class="icon-bullet">
<li>CSV 📋</li>
<li>XML 🏷️</li>
<li>JSON 🌐</li>
<li>HTML 🖥️</li>
</ul>
""", unsafe_allow_html=True)
if st.button("🚀 Open Jupyter Notebook for entire semi-structured data"):
    notebook_url = "https://github.com/raj2216/my-code/blob/main/DATA%20HANDLING%20(1).ipynb"
    st.markdown(f"[Click here to go to the Jupyter notebook]({notebook_url})", unsafe_allow_html=True)


st.sidebar.title("Navigation 🧭")
file_type = st.sidebar.radio(
    "Choose a file type to learn more:",
    ("CSV", "XML", "JSON", "HTML")
)

if file_type == "CSV":
    st.title("CSV : (Comma-Separated Values) 📋")
    st.markdown("""A CSV file stores data in plain text format, where each line represents a row and values are separated by commas. 🗂️""", unsafe_allow_html=True)
    st.code("Name,Age,Location\nAlice,25,New York\nBob,30,Los Angeles", language="csv")
    code = '''
    pd.read_csv(r"/Users/rajbunny/Downloads/phones_.csv")
    '''
    st.code(code, language="python")
    st.subheader("IMPORTANT NOTE 💡")
    st.markdown("""
    In CSV files, the separator can be:
    - **Comma (`,`)**
    - **Semicolon (`;`)**
    - **Tab (`\\t`)**
    - **Pipe (`|`)**
    - **Space (` `)** 🛠️
    """, unsafe_allow_html=True)
    st.header("Major Issues When Handling CSV Files ⚠️")
    st.markdown("""
    <ul class="icon-bullet"> 
    <li>Parse error 🛑</li>
    <li>Encoding issue 🌐</li>
    <li>Memory issue 🖥️</li>
    </ul>
    """, unsafe_allow_html=True)
    st.subheader("Parse Error 📉")
    st.markdown("""A parsing error occurs when the number of values in a row doesn't match the number of columns in the header. This can cause issues during data processing.""", unsafe_allow_html=True)
    st.code("Name,Age,Location\nAlice,25,New York\nBob,30,Los Angeles\nCharlie,22", language="csv")
    st.markdown("""
    To avoid parse errors, you can use `on_bad_lines`:
    - **`skip`**: Ignores the bad rows 🗑️.
    - **`warn`**: Skips the rows but provides a warning ⚠️.
    """)
    code = '''
    data = pd.read_csv(r'/Users/rajbunny/Downloads/text.csv', on_bad_lines="skip")
    data = pd.read_csv(r'/Users/rajbunny/Downloads/text.csv', on_bad_lines="warn")
    '''
    st.code(code, language="python")
    st.subheader("Encoding Issue 🌐")
    st.markdown("""
    Encoding issues occur when the characters in a CSV file don't match the expected format.
    Common encodings include:
    - **UTF-8** (default for most files)
    - **ISO-8859-1 (Latin-1)**
    - **Windows-1252**
    """, unsafe_allow_html=True)
    st.code("""Name,Age,Location\nAlice,30,New York\nBob,25,Los Angeles\nMário,28,São""", language='csv')
    st.markdown("""
    To resolve encoding issues, specify the correct encoding while reading the file.
    """, unsafe_allow_html=True)
    code = '''
    pd.read_csv(r"/Users/rajbunny/Downloads/spam.csv", encoding="utf-8", on_bad_lines="skip")
    '''
    st.code(code, language="python")
    st.subheader("Memory Issue 🖥️")
    st.markdown("""
    For large CSV files causing memory issues, you can load them in chunks:
    """, unsafe_allow_html=True)
    code = '''
    for chunk in pd.read_csv(r"/Users/rajbunny/Downloads/spam.csv", chunksize=1000):
        print(chunk.shape)
    '''
    st.code(code, language="python")
    st.subheader("Save Data Back to CSV 💾")
    code = '''
    data.to_csv(r"output.csv")
    '''
    st.code(code, language="python")
    st.subheader("📝 To view the coding part of the Jupyter notebook:")

elif file_type == "XML":
    st.title("XML : (eXtensible Markup Language) 🏷️")
    st.markdown("""An XML file stores data in a tree-like structure using custom tags to organize information. 🌳""", unsafe_allow_html=True)
    st.code('''
<data>
  <person>
    <name>Alice</name>
    <age>30</age>
    <location>New York</location>
  </person>
  <person>
    <name>Bob</name>
    <age>25</age>
    <location>Los Angeles</location>
  </person>
</data>
''', language='xml')
    code = '''
    pd.read_xml(r"/Users/rajbunny/Downloads/sample1.xml")
    '''
    st.code(code, language="python")
    st.subheader("XPath 🧭")
    st.markdown("""
    XPath is used to navigate and pick specific parts of an XML file, like selecting nodes or attributes.
    """, unsafe_allow_html=True)
    code = '''
    data = pd.read_xml(r"/Users/rajbunny/Downloads/sample3.xml", xpath="person")
    '''
    st.code(code, language="python")
    st.subheader("Save Back to XML 💾")
    code = '''
    data.to_xml(r"output.xml")
    '''
    st.code(code, language="python")

elif file_type == "JSON":
    st.title("JSON : (JavaScript Object Notation) 🌐")
    st.markdown("""JSON stores data as key-value pairs, making it easy to convert into a table or dictionary-like format.All the api data will be in the form of JSON and its of two types given below..
    <ul class="icon-bullet">
    <li>Structured</li>
    <li>Un Structured</li>
    </ul>""",unsafe_allow_html=True)
    st.header("Structured JSON Format")
    code='''
    d1='{"name":["p1","p2"],"age":[21,22]}'
    '''
    st.code(code,language="python")
    st.markdown("""it's in the form of dictonary given inside a string""",unsafe_allow_html=True)
    st.header("How to read a Structured json file?")
    code='''
    data=pd.read_json(d1)
    '''
    st.code(code,language="python")
    st.header("Data Frame to json")
    st.subheader("Orient as index")
    code='''
    jason_with_index=data.to_json(orient="index")
    output='{"0":{"name":"p1","age":21},"1":{"name":"p2","age":22}}'
    '''
    st.code(code,language="python")
    st.markdown("""While converting when we give orient as index then keys will index and rest will be values..""",unsafe_allow_html=True)
    st.subheader("To Convert Back to Data Frame")
    code='''
    pd.read_json(jason_with_index,orient="index")
    '''
    st.code(code,language="python")
    st.subheader("Orient as column")
    code='''
    jason_with_index=data.to_json(orient="column")
    output='{"name":{"0":"p1","1":"p2"},"age":{"0":21,"1":22}}'
    '''
    st.code(code,language="python")
    st.markdown("""While converting when we give orient as column then keys will column and rest will be values..""",unsafe_allow_html=True)
    st.subheader("To Convert Back to Data Frame")
    code='''
    pd.read_json(jason_with_index,orient="column")
    '''
    st.code(code,language="python")
    st.subheader("Orient as values")
    code='''
    jason_with_index=data.to_json(orient="values")
    output='[["p1",21],["p2",22]]'
    '''
    st.code(code,language="python")
    st.markdown("""While converting when we give orient as values then we will be getting a nested list""",unsafe_allow_html=True)
    st.subheader("To Convert Back to Data Frame")
    code='''
    pd.read_json(jason_with_index,orient="values")
    '''
    st.code(code,language="python")
    st.subheader("Orient as split")
    code='''
    jason_with_index=data.to_json(orient="split")
    output='{"columns":["name","age"],"index":[0,1],"data":[["p1",21],["p2",22]]}'
    '''
    st.code(code,language="python")
    st.markdown("""While converting when we give orient as split then we will be getting index as seperate key,columns as seperate key,and finally data as seperate key...""",unsafe_allow_html=True)
    st.subheader("To Convert Back to Data Frame")
    code='''
    pd.read_json(jason_with_index,orient="split")
    '''
    st.code(code,language="python")
    st.subheader("First Un Structured JSON Format")
    code='''
    d2={"name":["p1","p2"],"marks":{"sem1":{"maths":[11,12],"hindi":[11,12]},"sem2":{"maths":[11,11],"hindi":[12,12]}}}
    '''
    st.code(code,"python")
    st.markdown("""A json format can be said as unstructured json format when have dictonary inside a dictonary....""")
    st.subheader("How to read Un Structured JSON Format?")
    code='''
    pd.json_normalize(d2,max_level=1)
    pd.json_normalize(d2)
    '''
    st.code(code,"python")
    st.markdown("""When we have a json file having dictonary inside a dictonary we havr use **json_normalize** if we jst pass the file as it is default i will check all the levels but when we pass max_level value it will check till that level only..""",unsafe_allow_html=True)
    st.subheader("Second Un Structured JSON Format")
    code='''
    x=[{"name":"p1","age":21,"marks":[{"maths":11,"hindi":11}]},{"name":"p1","age":21,"marks":[{"maths":11,"hindi":11}]}]
    '''
    st.code(code,language="python")
    st.markdown("""A json formal is also said as unstructured json format when we have dictonary inside a list.""",unsafe_allow_html=True)
    st.subheader("How to read Un Structured JSON Format?")
    code='''
    pd.json_normalize(x,record_path="marks",meta=["name","age"])
    '''
    st.code(code,language="python")
    st.markdown("""When we have a dictonary inside a list for that column we will be useing **record_path** and to say also to include remaing columns we use **meta** and pass those columns also..""")
    
elif file_type == "HTML":
    st.title("HTML : (HyperText Markup Language) 🖥️")
    st.markdown("""HTML is a Semi Structured data by this html we can retrive only the tables present inside the particular table..""")
    st.subheader("How to read and get the Tabular data from the url.?")
    code='''
    data=pd.read_html("https://en.wikipedia.org/wiki/Indian_Premier_League")
    data=pd.read_html("https://en.wikipedia.org/wiki/Indian_Premier_League",match="Texas Super Kings")
    '''
    st.code(code,language="python")
    st.subheader("Note")
    st.markdown("""First one will give all the tables and Second one will give only the matched word tabels only..""")