import streamlit as st

# App Title and Introduction
st.title('Machine Learning Learning Hub')
st.write('Welcome to the ML Learning Hub, your gateway to learning Machine Learning!')

# Navigation and Layout
section = st.sidebar.selectbox('Choose a Section', 
                               ('Home', 'Beginner Resources', 'Intermediate Resources', 
                                'Advanced Resources', 'Projects', 'Communities'))

# Define Sections

# Home Section
if section == 'Home':
    st.header('Welcome to the ML Learning Hub!')
    st.write('Select a section from the sidebar to begin exploring resources.')

# Beginner Resources
elif section == 'Beginner Resources':
    st.header('Beginner Resources')
    st.write('Here are some great resources for ML beginners:')
    st.markdown('[Machine Learning by Andrew Ng on Coursera](https://www.coursera.org/learn/machine-learning)')
    st.markdown('[Introduction to Machine Learning for Coders by fast.ai](https://course.fast.ai/ml)')
    st.markdown('[Google\'s Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)')

# Intermediate Resources
elif section == 'Intermediate Resources':
    st.header('Intermediate Resources')
    st.write('Resources for those who are familiar with the basics:')
    st.markdown('[Deep Learning Specialization by Andrew Ng on Coursera](https://www.coursera.org/specializations/deep-learning)')
    st.markdown('[Kaggle Micro-Courses](https://www.kaggle.com/learn/overview)')
    st.markdown('[DataCamp Machine Learning Scientist with Python Track](https://www.datacamp.com/tracks/machine-learning-scientist-with-python)')

# Advanced Resources
elif section == 'Advanced Resources':
    st.header('Advanced Resources')
    st.write('For those looking to deepen their ML knowledge:')
    st.markdown('[Advanced Machine Learning Specialization on Coursera](https://www.coursera.org/specializations/aml)')
    st.markdown('[MIT\'s Deep Learning for Self-Driving Cars](http://selfdrivingcars.mit.edu/)')
    st.markdown('[The Elements of Statistical Learning: Data Mining, Inference, and Prediction](https://web.stanford.edu/~hastie/ElemStatLearn/)')

# Projects
elif section == 'Projects':
    st.header('Projects')
    st.write('Hands-on projects to apply your ML skills:')
    st.markdown('[Kaggle Competitions](https://www.kaggle.com/competitions)')
    st.markdown('[TensorFlow Projects](https://www.tensorflow.org/resources/learn-ml)')
    st.markdown('[GitHub ML Showcase](https://github.com/collections/machine-learning)')

# Communities
elif section == 'Communities':
    st.header('Communities')
    st.write('Join ML communities to learn and share:')
    st.markdown('[r/MachineLearning on Reddit](https://www.reddit.com/r/MachineLearning/)')
    st.markdown('[Data Science Stack Exchange](https://datascience.stackexchange.com/)')
    st.markdown('[AI & Machine Learning on Stack Overflow](https://stackoverflow.com/tags/machine-learning)')

# Run the App
# To run the app, save this script and use the command: streamlit run [script_name].py