import streamlit as st
import os
import base64
def load_image_as_base64(image_path):
repo_root = os.path.dirname(os.path.abspath(__file__))
image_path = os.path.join(repo_root, image_path)
with open(image_path, "rb") as f:
data = f.read()
return base64.b64encode(data).decode()
# Read image and encode in base64
logo_as_base64 = load_image_as_base64(os.path.join("img", "GVHD-Orgs-logo.png"))
st.set_page_config(page_title="GVHD Predictions", layout="wide")
# --- Flexible Footer CSS ---
st.markdown(
"""
""",
unsafe_allow_html=True
)
st.markdown("""
## GVHD-Intel Pro
### Clinical Decision-Support & Outcome Simulation Platform
Interactive framework enabling transplant physicians to explore how different
patient, donor, and conditioning variables influence:
• Acute GVHD incidence
• Chronic GVHD risk
• Overall survival probability
Compare scenarios and identify combinations associated with lower predicted risk
to support evidence-informed transplant planning.
""", unsafe_allow_html=True)
# add GVHD tagline
st.markdown("""
A modular prediction framework for Acute & Chronic GVHD risk assessment.
Predict. Learn. Adapt.
""", unsafe_allow_html=True)
# --- Partners Section ---
st.divider()
st.subheader("Partners:")
st.markdown(
f"""
""",
unsafe_allow_html=True
)
# --- Disclaimer Section ---
st.divider()
st.write(
"""
**Disclaimer: Experimental research platform — not approved for clinical use.**\\
Modular AI-driven framework that centers can retrain with their own multi-center datasets to build and validate population-specific GVHD prediction models.\\
Model performance varies by training data and updates.
""")
st.markdown('', unsafe_allow_html=True)
# --- End of MAIN CONTENT ---
# --- Footer ---
st.markdown(
"""
""",
unsafe_allow_html=True
) 