import streamlit as st from st_pages import Page, show_pages, Section, add_indentation from PIL import Image from utils import check_password ################################################################################## # PAGE CONFIGURATION # ################################################################################## st.set_page_config(layout="wide") ################################################################################## # PASSWORD CHECK # ################################################################################## if check_password(): ################################################################################## # GOOGLE DRIVE CONNEXION # ################################################################################## # if ["drive_oauth"] not in st.session_state: # st.session_state["drive_oauth"] = authenticate_drive() # drive_oauth = st.session_state["drive_oauth"] ################################################################################## # TITLE # ################################################################################## st.image("images/AI.jpg") st.markdown(" ") col1, col2 = st.columns([0.65,0.35], gap="medium") with col1: st.title("AI and Data Science Examples") st.subheader("HEC Paris, 2023-2024") # st.markdown("""**Course provided by Shirish C. SRIVASTAVA**
# **Hi! PARIS Engineering team**: Laurène DAVID, Salma HOUIDI and Maeva N'GUESSAN""", unsafe_allow_html=True) #st.markdown("in collaboration with Hi! PARIS engineers: Laurène DAVID, Salma HOUIDI and Maeva N'GUESSAN") # with col2: #Hi! PARIS collaboration mention # st.markdown(" ") # st.markdown(" ") #st.markdown(" ") url = "https://www.hi-paris.fr/" #st.markdown("This app was funded by the Hi! PARIS Center") st.markdown("""###### **The app was made in collaboration with [Hi! PARIS](%s)** """ % url, unsafe_allow_html=True) image_hiparis = Image.open('images/hi-paris.png') st.image(image_hiparis, width=150) st.markdown(" ") st.divider() # #Hi! PARIS collaboration mention # st.markdown(" ") # image_hiparis = Image.open('images/hi-paris.png') # st.image(image_hiparis, width=150) # url = "https://www.hi-paris.fr/" # st.markdown("**The app was made in collaboration with [Hi! PARIS](%s)**" % url) ################################################################################## # DASHBOARD/SIDEBAR # ################################################################################## # AI use case pages show_pages( [ Page("main_page.py", "Home Page", "🏠"), Section(name=" ", icon=""), Section(name=" ", icon=""), Section(name="Machine Learning", icon="1️⃣"), Page("pages/supervised_unsupervised_page.py", "1| Supervised vs Unsupervised 🔍", ""), Page("pages/timeseries_analysis.py", "2| Time Series Forecasting 📈", ""), Page("pages/recommendation_system.py", "3| Recommendation systems 🛒", ""), Section(name="Natural Language Processing", icon="2️⃣"), Page("pages/topic_modeling.py", "1| Topic Modeling 📚", ""), Page("pages/sentiment_analysis.py", "2| Sentiment Analysis 👍", ""), Section(name="Computer Vision", icon="3️⃣"), Page("pages/image_classification.py", "1| Image Classification 🖼️", ""), Page("pages/object_detection.py", "2| Object Detection 📹", ""), Page("pages/go_further.py", "🚀 Go further") ] ) ################################################################################## # PAGE CONTENT # ################################################################################## st.header("About the app") st.info("""The goal of the **AI and Data Science Examples** is to give an introduction to Data Science by showcasing real-life applications. The app includes use cases using traditional Machine Learning algorithms on structured data, as well as models that analyze unstructured data (text, images,...).""") st.markdown(" ") st.markdown("""The app contains four sections: - 1️⃣ **Machine Learning**: This first section covers use cases where structured data (data in a tabular format) is used to train an AI model. You will find pages on *Supervised/Unsupervised Learning*, *Time Series Forecasting* and AI powered *Recommendation Systems*. - 2️⃣ **Natural Language Processing** (NLP): This second section showcases AI applications where large amounts of text data is analyzed using Deep Learning models. Pages on *Topic Modeling* and *Sentiment Analysis*, which are different kinds of NLP models, can be found in this section. - 3️⃣ **Computer Vision**: This third section covers a sub-field of AI called Computer Vision, which deals with image/video data. The field of Computer Vision includes *Image classification* and *Object Detection*, which are both featured in this section. - 🚀 **Go further**: In the final section, you will gain a deeper understanding of AI models and how they function. The page features multiple models to try, as well as different datasets to train a model on. """) st.image("images/ML_domains.png", caption="""This figure showcases a selection of sub-fields of AI, which includes Machine Learning, NLP and Computer Vision.""") # st.markdown(" ") # st.markdown(" ") # st.markdown("## Want to learn more about AI ?") # st.markdown("""**Hi! PARIS**, a multidisciplinary center on Data Analysis and AI founded by Institut Polytechnique de Paris and HEC Paris, # hosts every year a **Data Science Bootcamp** for students of all levels.""")