import streamlit as st

st.set_page_config(
    page_title="U-Net Architecture Prototype",
    page_icon="👋",
)

st.title("Enhancing the Performance of SVM and CNN Models in Detection and Classification of Cervical Cells in Pap Smear Images Using U-Net Architecture for Image Segmentation")
st.write("A prototype for our U-Net Architecture for Image Segmentation of Cervical Cancer Cells, and SVM and CNN for Classification of Cervical Cancer Cells")
st.write("Thesis Project by Group DJY of Mapua University")
st.image("pages/Cervical-Cancer-Cells.jpg", caption='', width=700)


st.header("How does this app work? ⚙")
st.write("""
Once you upload an image of cervical cancer cells using any of our models,
they analyze the cell structure and classify the type of cancer present.
The system will then predict the type of cancer cells based on the analysis.  
""")

st.subheader("How to use this app? 🤔")
st.markdown("""
1. Select U-Net-Model to segment your Cervical Cells image into predicted Cytoplasm and Nuclei Mask.
2. Download predicted Cytoplasm and Nuclei Mask image from U-Net-Model.
3. Head to either CNN or SVM Model to classify Cervical Cells image. 
4. Upload Cervical Cells image, Predicted Cytoplasm image, and Predicted Nuclei image in the model choosen.
5. Wait for the model to analyze and classify the type of cancer present.
6. The model will output predicted Cervical Cancer cell type based on the analysis with images of the uploaded images and plotted image of concatenated image of predicted Cytoplasm and Nuclei Mask. 
""")

st.sidebar.info("Please select a model from above 👆")

st.sidebar.write("""
### Background 📖
Cervical cancer is a significant health issue that profoundly impacts women's lives, making awareness and education crucial. 
It is a leading cause of cancer-related morbidity and mortality among women globally. 
Understanding the importance of regular screenings and vaccinations is vital for effective treatment. 

### The Problem 💡
Current methods in early detection like the Pap smear test can be slow and labor-intensive, prompting researchers to develop classification models to assist medical professionals. 
However, many of these models face challenges with image segmentation, particularly in cases of overlapping cells. 
This prototype seeks to improve upon existing machine learning models by incorporating the U-Net architecture, designed for precise image segmentation, to enhance the identification of cancerous cells in cervical samples, ultimately facilitating faster and more accurate diagnoses.
""")

st.sidebar.image("pages/Mapua-logo.png", width=270)