"""CortexLab Dashboard - Futuristic Landing Page."""
import streamlit as st
import numpy as np
from theme import inject_theme, hero_header, glow_card, section_header, feature_card
from session import init_session, show_analysis_log
from utils import make_roi_indices
from brain_mesh import load_fsaverage_mesh, generate_sample_activations, render_interactive_3d
st.set_page_config(page_title="CortexLab", page_icon="🧠", layout="wide", initial_sidebar_state="collapsed")
init_session()
inject_theme()
# --- Hero ---
hero_header(
"CortexLab",
"Enhanced multimodal fMRI brain encoding toolkit built on Meta's TRIBE v2",
)
# --- 3D Brain Hero ---
st.markdown("", unsafe_allow_html=True)
with st.spinner("Rendering brain..."):
coords, faces = load_fsaverage_mesh("left", "fsaverage5")
n_verts = coords.shape[0]
roi_indices, _ = make_roi_indices()
mesh_roi = {name: np.clip((idx * n_verts // 580).astype(int), 0, n_verts - 1) for name, idx in roi_indices.items()}
activations = generate_sample_activations(n_verts, mesh_roi, "multimodal", seed=42)
fig = render_interactive_3d(
coords, faces, activations, cmap="Inferno", vmin=0, vmax=0.8,
bg_color="#050510", initial_view="Lateral Left",
roi_indices=mesh_roi, show_labels=False,
)
if fig is not None:
fig.update_layout(
height=500,
margin=dict(l=0, r=0, t=0, b=0),
)
st.plotly_chart(fig, use_container_width=True)
# --- Stats Bar ---
st.markdown("", unsafe_allow_html=True)
c1, c2, c3, c4, c5 = st.columns(5)
with c1: glow_card("Tests", "89", "All passing", "#10B981")
with c2: glow_card("Analysis Modules", "10", "Brain encoding", "#7C3AED")
with c3: glow_card("ROIs", "29", "HCP MMP1.0", "#3B82F6")
with c4: glow_card("Contributors", "4", "Open source", "#EC4899")
with c5: glow_card("License", "CC BY-NC", "Non-commercial", "#F59E0B")
# --- Feature Grid ---
st.markdown("", unsafe_allow_html=True)
section_header("Analysis Tools", "Everything you need for computational neuroscience research")
col1, col2, col3 = st.columns(3)
with col1:
st.markdown(feature_card(
"🎯", "Brain Alignment Benchmark",
"Score any AI model against brain responses. RSA, CKA, Procrustes with permutation tests, bootstrap CIs, and FDR correction.",
"#7C3AED"
), unsafe_allow_html=True)
st.page_link("pages/1_Brain_Alignment.py", label="Open Brain Alignment")
with col2:
st.markdown(feature_card(
"📊", "Cognitive Load Scorer",
"Predict cognitive demand across visual, auditory, language, and executive dimensions with confidence bands.",
"#3B82F6"
), unsafe_allow_html=True)
st.page_link("pages/2_Cognitive_Load.py", label="Open Cognitive Load")
with col3:
st.markdown(feature_card(
"⏱️", "Temporal Dynamics",
"Peak response latency, lag correlations, sustained vs transient decomposition, cross-ROI lag matrix.",
"#06B6D4"
), unsafe_allow_html=True)
st.page_link("pages/3_Temporal_Dynamics.py", label="Open Temporal Dynamics")
col4, col5, col6 = st.columns(3)
with col4:
st.markdown(feature_card(
"🔗", "ROI Connectivity",
"Functional connectivity, partial correlation, network clustering, modularity, degree and betweenness centrality.",
"#10B981"
), unsafe_allow_html=True)
st.page_link("pages/4_Connectivity.py", label="Open Connectivity")
with col5:
st.markdown(feature_card(
"🧠", "3D Brain Viewer",
"Interactive rotatable brain surface with activation overlays, publication-quality multi-view panels, ROI highlighting.",
"#EC4899"
), unsafe_allow_html=True)
st.page_link("pages/5_Brain_Viewer.py", label="Open Brain Viewer")
with col6:
st.markdown(feature_card(
"🔴", "Live Inference",
"Real-time brain prediction from webcam, screen capture, or video. All metrics update live. Works in simulation mode or with full CortexLab + GPU.",
"#EF4444"
), unsafe_allow_html=True)
st.page_link("pages/6_Live_Inference.py", label="Open Live Inference")
# --- Data Config (collapsed) ---
st.markdown("", unsafe_allow_html=True)
with st.expander("Data Configuration", expanded=False):
from session import data_summary_widget, upload_npy_widget
col_src, col_params = st.columns([1, 2])
with col_src:
source = st.radio("Data source", ["Synthetic (realistic)", "Upload your data"], index=0)
st.session_state["data_source"] = "synthetic" if "Synthetic" in source else "uploaded"
with col_params:
if st.session_state["data_source"] == "synthetic":
c1, c2, c3, c4 = st.columns(4)
st.session_state["stimulus_type"] = c1.selectbox("Stimulus", ["visual", "auditory", "language", "multimodal"])
st.session_state["n_timepoints"] = c2.slider("TRs", 30, 200, 80)
st.session_state["tr_seconds"] = c3.slider("TR (s)", 0.5, 2.0, 1.0, 0.1)
st.session_state["seed"] = c4.number_input("Seed", value=42, min_value=0)
roi_indices_full, n_vertices = make_roi_indices()
st.session_state["roi_indices"] = roi_indices_full
st.session_state["n_vertices"] = n_vertices
from synthetic import generate_realistic_predictions
predictions = generate_realistic_predictions(
st.session_state["n_timepoints"], roi_indices_full,
st.session_state["stimulus_type"], st.session_state["tr_seconds"],
seed=st.session_state["seed"],
)
st.session_state["brain_predictions"] = predictions
else:
uploaded = upload_npy_widget("Upload predictions (.npy)", "upload_home")
if uploaded is not None:
st.session_state["brain_predictions"] = uploaded
roi_indices_full, _ = make_roi_indices()
st.session_state["roi_indices"] = roi_indices_full
preds = st.session_state.get("brain_predictions")
roi_idx = st.session_state.get("roi_indices")
if preds is not None and roi_idx is not None:
data_summary_widget(preds, roi_idx)
# --- Footer ---
show_analysis_log()
st.markdown("""
""", unsafe_allow_html=True)