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52e54aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | import streamlit as st
import streamlit.components.v1 as components
import os # Import the os module
import pandas as pd
from sidebar_logo import add_sidebar_logo, load_css
# Set page configuration
st.set_page_config(
layout="wide"
)
add_sidebar_logo()
load_css()
st.markdown("""
<div style="
font-size: 1.9rem;
font-weight: 800;
background: linear-gradient(135deg, #a78bfa, #818cf8, #f472b6);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-family: 'Poppins', sans-serif;
">
📓 2. Python Basics
</div>
""", unsafe_allow_html=True)
# Define the file path with regular spaces
path_to_html = "Python_basics.html"
# Check if the HTML file exists
if not os.path.exists(path_to_html):
st.error("HTML file not found!")
else:
# Read HTML content
with open(path_to_html, 'r', encoding='utf-8') as f:
html_data = f.read()
# Show HTML content
st.header(":violet[Python basics]")
st.markdown("""Hi guys. Now that you are familiar with Jupyter notebook, let us start with Python. The entire
Machine Learning we are going study is implemented using Python. So , it is essential for us to
understand some core Python basics to get started. You can download the Jupyter notebook
at the end of this session. I have enclosed it at the bottom of this page.
For now, let's get started with Python.""")
st.write("---")
st.components.v1.html(html_data, width=1000, height=13500)
def download_notebook():
with open("Python_basics.ipynb", "rb") as f:
data = f.read()
return data
# Create a download button for the notebook
st.write("----")
st.write("To download 'Python basics' Jupyter notebook click on the button below.")
button_label = ":violet[Download Jupyter Notebook]"
button_download = st.download_button(label=button_label, data=download_notebook(), file_name="Python_basics.ipynb", mime='application/x-ipynb+json')
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