', unsafe_allow_html=True)
else:
st.sidebar.image("assets/logo.png", width=50)
st.sidebar.divider()
# Input mode
input_options = ["PDB ID", "Upload file"]
if auth_is_logged_in():
input_options.insert(0, "From saved")
input_mode = st.sidebar.radio(
"Protein input",
input_options,
help="Retrieve from RCSB, upload, or load from your saved structures",
)
protein_source = None
pdb_id = None
if input_mode == "From saved" and auth_is_logged_in():
user_id = auth_get_user_id()
saved = list_user_cached(user_id)
if saved:
chosen = st.sidebar.selectbox("Your saved structures", options=[n for n, _ in saved], format_func=lambda x: x)
if chosen:
protein_source = load_from_user_cache(user_id, chosen)
pdb_id = chosen
else:
st.sidebar.info("Hey, there. No saved structures yet. Request or upload a PDB or CIF to save it.")
elif input_mode == "PDB ID":
pdb_id = st.sidebar.text_input("PDB ID", value="1LYZ", max_chars=10)
if pdb_id:
try:
path = fetch_pdb(pdb_id)
protein_source = path
if auth_is_logged_in():
save_fetched_to_user_cache(auth_get_user_id(), pdb_id, path)
except Exception as e:
st.sidebar.error(f"Request failed: {e}")
else:
uploaded = st.sidebar.file_uploader("Upload PDB or CIF", type=["pdb", "cif"])
if uploaded:
data = uploaded.read()
with tempfile.NamedTemporaryFile(delete=False, suffix=Path(uploaded.name).suffix) as f:
f.write(data)
protein_source = f.name
pdb_id = Path(uploaded.name).stem
if auth_is_logged_in():
save_upload_to_user_cache(auth_get_user_id(), uploaded.name, data)
# Config
config = load_config()
monomers = load_monomers()
monomer_names = list(monomers.keys())
MAX_MONOMERS = 4
# Model selector
_model_labels = [f"{name} ({k})" for k, (name, _, _) in MODEL_TYPES.items()]
_model_keys = list(MODEL_TYPES.keys())
if GPR_AVAILABLE:
_model_labels.append("GPR (with uncertainty)")
_model_keys.append("gpr")
model_choice_idx = st.sidebar.selectbox(
"Prediction model",
range(len(_model_labels)),
format_func=lambda i: _model_labels[i],
help="RF | SVM | Ridge | Logistic | Gradient Boosting | KNN | GPR",
)
model_key = _model_keys[model_choice_idx]
use_gpr = model_key == "gpr"
def get_predictor():
if use_gpr:
return GPRStabilityPredictor()
return StabilityPredictor(use_surrogate=True, model_type=model_key)
# Auth0: trigger login via query param (for gradient link-button)
if auth0_available() and st.query_params.get("login") == "auth0":
st.login("auth0")
st.stop()
# Auth0: status in sidebar when logged in
if auth0_available() and hasattr(st, "user") and getattr(st.user, "is_logged_in", False):
st.sidebar.caption(f"Signed in as {getattr(st.user, 'name', getattr(st.user, 'email', ''))}")
# Persist composition in session state (updated in Structure tab)
if "composition" not in st.session_state:
st.session_state.composition = {name: 0.25 if i < 4 else 0.0 for i, name in enumerate(monomer_names)}
total = 1.0
st.session_state.composition = {k: v for k, v in st.session_state.composition.items() if v > 0}
if st.session_state.composition:
t = sum(st.session_state.composition.values())
st.session_state.composition = {k: v/t for k, v in st.session_state.composition.items()}
# Main content - header row (compact: no tall widget to avoid blank space)
header_col1, header_col2 = st.columns([3, 1])
with header_col1:
logo_col, title_col = st.columns([1, 4])
with logo_col:
if LOGO_PATH.exists():
_logo_b64 = __import__("base64").b64encode(LOGO_PATH.read_bytes()).decode()
st.markdown(f"""
""", unsafe_allow_html=True)
else:
st.image("assets/logo.png", width=80)
with title_col:
st.title("ProteinPRO")
st.markdown("**Mapping Protein Chemistry to Polymer Chemistry**")
with header_col2:
st.markdown('', unsafe_allow_html=True)
if auth0_available():
try:
logged_in = getattr(st.user, "is_logged_in", False) if hasattr(st, "user") else False
if logged_in:
st.caption(f"Signed in as {getattr(st.user, 'name', getattr(st.user, 'email', 'User'))}")
if st.button("Log out", key="auth_logout", type="primary"):
st.logout()
else:
st.markdown("""
""", unsafe_allow_html=True)
except Exception:
st.markdown("""
""", unsafe_allow_html=True)
# Hero content block: immediately below header, centered (no nested container)
st.markdown("""
ProteinPRO predicts protein–polymer hybrid (PPH) formulation stability by matching chemical features from your protein structure to PET-RAFT monomer composition chemical features.