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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. Artificial Neural Networks | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Define the file path with regular spaces | |
| path_to_html = "Artificial_Neural_Networks.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[Artificial Neural Networks]") | |
| st.markdown("""Hi guys. Welcome to the first lecture of Deep Learning series. We are going to start right from the | |
| basics which brings us to 'Artificial Neural Networks (ANN)'. Today we are going to understand how to | |
| create a ANN model, how to compile, fit & evaluate it subsequently. | |
| We are going to work on 'pima-indians-diabetes' dataset and hence I would request you guys to download | |
| it to get started. Click on the button below to download the csv file. | |
| In the notebook below I have used a link to fetch the dataset, alternatively you can copy the link as well. | |
| Right click on the Download link button and click on 'Copy Link'.""") | |
| df = pd.read_csv("pima-indians-diabetes.csv") | |
| def download_csv(): | |
| df.to_csv("pima-indians-diabetes.csv", index=False) | |
| with open("pima-indians-diabetes.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='pima-indians-diabetes.csv', mime='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') | |
| st.write("[:violet[Download link >]](https://raw.githubusercontent.com/slmsshk/pima-indians-diabetes.data.csv/main/pima-indians-diabetes.csv)") | |
| st.write("---") | |
| st.components.v1.html(html_data, width=1000, height=23285) | |
| def download_notebook(): | |
| with open("Artificial_Neural_Networks.ipynb", "rb") as f: | |
| data = f.read() | |
| return data | |
| # Create a download button for the notebook | |
| st.write("----") | |
| st.write("To download 'Artificial Neural Networks' 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="Artificial_Neural_Networks.ipynb", mime='application/x-ipynb+json') | |