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import streamlit as st |
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from st_pages import Page, show_pages, Section, add_indentation |
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from PIL import Image |
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from utils import check_password |
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st.set_page_config(layout="wide") |
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if check_password(): |
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st.image("images/AI.jpg") |
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st.markdown(" ") |
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col1, col2 = st.columns([0.65,0.35], gap="medium") |
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with col1: |
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st.title("AI and Data Science Examples") |
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st.subheader("HEC Paris, 2023-2024") |
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url = "https://www.hi-paris.fr/" |
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st.markdown("""###### **The app was made in collaboration with [Hi! PARIS](%s)** """ % url, unsafe_allow_html=True) |
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image_hiparis = Image.open('images/hi-paris.png') |
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st.image(image_hiparis, width=150) |
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st.markdown(" ") |
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st.divider() |
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show_pages( |
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[ |
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Page("main_page.py", "Home Page", "🏠"), |
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Section(name=" ", icon=""), |
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Section(name=" ", icon=""), |
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Section(name="Machine Learning", icon="1️⃣"), |
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Page("pages/supervised_unsupervised_page.py", "1| Supervised vs Unsupervised 🔍", ""), |
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Page("pages/timeseries_analysis.py", "2| Time Series Forecasting 📈", ""), |
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Page("pages/recommendation_system.py", "3| Recommendation systems 🛒", ""), |
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Section(name="Natural Language Processing", icon="2️⃣"), |
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Page("pages/topic_modeling.py", "1| Topic Modeling 📚", ""), |
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Page("pages/sentiment_analysis.py", "2| Sentiment Analysis 👍", ""), |
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Section(name="Computer Vision", icon="3️⃣"), |
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Page("pages/image_classification.py", "1| Image Classification 🖼️", ""), |
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Page("pages/object_detection.py", "2| Object Detection 📹", ""), |
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Page("pages/go_further.py", "🚀 Go further") |
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] |
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) |
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st.header("About the app") |
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st.info("""The goal of the **AI and Data Science Examples** is to give an introduction to Data Science by showcasing real-life applications. |
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The app includes use cases using traditional Machine Learning algorithms on structured data, as well as models that analyze unstructured data (text, images,...).""") |
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st.markdown(" ") |
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st.markdown("""The app contains four sections: |
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- 1️⃣ **Machine Learning**: This first section covers use cases where structured data (data in a tabular format) is used to train an AI model. |
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You will find pages on *Supervised/Unsupervised Learning*, *Time Series Forecasting* and AI powered *Recommendation Systems*. |
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- 2️⃣ **Natural Language Processing** (NLP): This second section showcases AI applications where large amounts of text data is analyzed using Deep Learning models. |
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Pages on *Topic Modeling* and *Sentiment Analysis*, which are different kinds of NLP models, can be found in this section. |
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- 3️⃣ **Computer Vision**: This third section covers a sub-field of AI called Computer Vision, which deals with image/video data. |
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The field of Computer Vision includes *Image classification* and *Object Detection*, which are both featured in this section. |
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- 🚀 **Go further**: In the final section, you will gain a deeper understanding of AI models and how they function. |
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The page features multiple models to try, as well as different datasets to train a model on. |
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""") |
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st.image("images/ML_domains.png", |
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caption="""This figure showcases a selection of sub-fields of AI, which includes |
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Machine Learning, NLP and Computer Vision.""") |
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