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import streamlit as st
from PIL import Image
# Page title
st.title("Chris Capobianco's ML Portfolio")
st.markdown('Hello, welcome to my ML portfolio.')
st.markdown('Please have a look at the descriptions below, and select a project from the sidebar.')
st.header('Projects', divider='red')
do = Image.open("assets/document.jpg")
mv = Image.open("assets/movie.jpg")
# wp = Image.open("assets/weather.png")
sm = Image.open("assets/stock-market.png")
mu = Image.open("assets/music.jpg")
llm = Image.open("assets/llm.png")
ear = Image.open("assets/earthquake.png")
with st.container():
text_column, image_column = st.columns((3,1))
with text_column:
st.subheader("Document Classifier", divider="green")
st.markdown("""
- Used OCR text and a Random Forest classification model to predict a document's classification
- Trained on Real World Documents Collection at Kaggle
""")
with image_column:
st.image(do)
with st.container():
text_column, image_column = st.columns((3,1))
with text_column:
st.subheader("Movie Recommendation", divider="green")
st.markdown("""
- Created a content based recommendation system using cosine similarity
- Trained on almost 5k movies and credits from the TMDB dataset available at Kaggle
""")
with image_column:
st.image(mv)
# with st.container():
# text_column, image_column = st.columns((3,1))
# with text_column:
# st.subheader("Weather Classification", divider="green")
# st.markdown("""
# - Created a Random Forest classification model to predict the weather
# - Trained on three years of data for the city of Seattle, Washington
# """)
# with image_column:
# st.image(wp)
with st.container():
text_column, image_column = st.columns((3,1))
with text_column:
st.subheader("Stock Market Forecast", divider="green")
st.markdown("""
- Created a two layer GRU model to forecast of stock prices
- Trained on 2006-2018 closing prices of four well known stocks
""")
with image_column:
st.image(sm)
with st.container():
text_column, image_column = st.columns((3,1))
with text_column:
st.subheader("Generative Music", divider="green")
st.markdown("""
- Created a LSTM model to generate music
- Trained on MIDI files from Final Fantasy series
""")
with image_column:
st.image(mu)
with st.container():
text_column, image_column = st.columns((3,1))
with text_column:
st.subheader("Fine Tuned LLM", divider="green")
st.markdown("""
- Fine tuned a LLM to act like math assistant
- The base model is Meta's Llama 3.1 (8B) Instruct
""")
with image_column:
st.image(llm)
with st.container():
text_column, image_column = st.columns((3,1))
with text_column:
st.subheader("Urban Safety Planner", divider="green")
st.markdown("""
- Analyze Earthquake and Population Density data
- Locate areas that need extra earthquake reinforcement
""")
with image_column:
st.image(ear) |