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| 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; | |
| "> | |
| 📓 1. Linear Regression | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Define the file path with regular spaces | |
| path_to_html = "Linear_Regression.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[Linear Regression]") | |
| st.markdown(""" Hi guys. I hope you are excited to finally start with Machine Learning. Today marks our first day to learn | |
| Machine Learning and I am thrilled to present you the first ML algorithm which goes by the name 'Linear Regression'. | |
| I would request you to download the excel file 'advertising.csv' by clicking on the download button below. I would | |
| also appreciate if you code along with me by keeping your Jupyter notebook open as well. Follow the session from start | |
| to end diligently to understand the concepts better. At the end of each session, I would anyways allow you to download | |
| the Jupyter notebooks I created for every topic. You could find a button at the bottom of every session page that says | |
| 'Download jupyter notebook'. | |
| Assuming that you are ready, let us start with Machine Learning.""") | |
| df = pd.read_csv("advertising.csv") | |
| def download_csv(): | |
| df.to_csv("advertising.csv", index=False) | |
| with open("advertising.csv", "rb") as f: | |
| data = f.read() | |
| return data | |
| # Create a download button | |
| button_label = ":violet[Download CSV]" | |
| button_download = st.download_button(label=button_label, data=download_csv(), file_name='advertising.csv', mime='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') | |
| st.write("---") | |
| st.components.v1.html(html_data, width=1000, height=16000) | |
| def download_notebook(): | |
| with open("Linear_Regression.ipynb", "rb") as f: | |
| data = f.read() | |
| return data | |
| # Create a download button for the notebook | |
| st.write("----") | |
| st.write("To download 'Linear Regression' 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="Linear_Regression.ipynb", mime='application/x-ipynb+json') | |
| st.markdown("""I have created an app which predicts Sales using Linear Regression. | |
| Click on the link below to check it out.""") | |
| st.write("[Visit the app >](https://sales-prediction-app.onrender.com/)") | |