Add Dockerfile and app.py for HuggingFace Spaces deployment

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.headless=true", "--server.address=0.0.0.0"]

app.py

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+"""CortexLab Dashboard - Home Page with Data Management."""
+
+import streamlit as st
+import numpy as np
+
+from session import init_session, data_summary_widget, show_analysis_log, upload_npy_widget
+from utils import make_roi_indices
+
+st.set_page_config(page_title="CortexLab Dashboard", page_icon="🧠", layout="wide", initial_sidebar_state="expanded")
+init_session()
+
+st.title("CortexLab Dashboard")
+st.markdown("**Research-grade analysis toolkit for multimodal fMRI brain encoding**")
+
+# --- Data Source ---
+st.divider()
+st.subheader("Data Configuration")
+
+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 type", ["visual", "auditory", "language", "multimodal"])
+        st.session_state["n_timepoints"] = c2.slider("Duration (TRs)", 30, 200, 80)
+        st.session_state["tr_seconds"] = c3.slider("TR (seconds)", 0.5, 2.0, 1.0, 0.1)
+        st.session_state["seed"] = c4.number_input("Seed", value=42, min_value=0)
+
+        # Generate on config change
+        roi_indices, n_vertices = make_roi_indices()
+        st.session_state["roi_indices"] = roi_indices
+        st.session_state["n_vertices"] = n_vertices
+
+        from synthetic import generate_realistic_predictions
+        predictions = generate_realistic_predictions(
+            st.session_state["n_timepoints"], roi_indices,
+            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 brain predictions (.npy, shape: timepoints x vertices)", "upload_predictions")
+        if uploaded is not None:
+            st.session_state["brain_predictions"] = uploaded
+            roi_indices, _ = make_roi_indices()
+            st.session_state["roi_indices"] = roi_indices
+
+# --- Data Summary ---
+roi_indices = st.session_state.get("roi_indices")
+predictions = st.session_state.get("brain_predictions")
+if predictions is not None and roi_indices is not None:
+    data_summary_widget(predictions, roi_indices)
+
+    # Show HRF-convolved signal preview
+    with st.expander("Data Preview", expanded=False):
+        import plotly.graph_objects as go
+        from utils import ROI_GROUPS
+
+        fig = go.Figure()
+        t = np.arange(predictions.shape[0]) * st.session_state.get("tr_seconds", 1.0)
+        colors = {"Visual": "#00D2FF", "Auditory": "#FF6B6B", "Language": "#A29BFE", "Executive": "#FFEAA7"}
+        for group, rois in ROI_GROUPS.items():
+            vals = []
+            for roi in rois:
+                if roi in roi_indices:
+                    verts = roi_indices[roi]
+                    valid = verts[verts < predictions.shape[1]]
+                    if len(valid) > 0:
+                        vals.append(np.abs(predictions[:, valid]).mean(axis=1))
+            if vals:
+                mean_tc = np.mean(vals, axis=0)
+                fig.add_trace(go.Scatter(x=t, y=mean_tc, name=group, line=dict(color=colors.get(group, "#888"), width=2)))
+
+        fig.update_layout(
+            xaxis_title="Time (seconds)", yaxis_title="Mean |activation|",
+            height=300, template="plotly_dark",
+            legend=dict(orientation="h", yanchor="bottom", y=1.02),
+        )
+        st.plotly_chart(fig, use_container_width=True)
+        st.caption("Mean absolute activation per functional group. Note the hemodynamic response shape and modality-specific activation patterns.")
+
+# --- Navigation ---
+st.divider()
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("Analysis Tools")
+    st.page_link("pages/1_Brain_Alignment.py", label="Brain Alignment Benchmark", icon="🎯")
+    st.caption("RSA, CKA, Procrustes with permutation tests, bootstrap CIs, FDR correction, noise ceiling, and RDM visualization")
+
+    st.page_link("pages/2_Cognitive_Load.py", label="Cognitive Load Scorer", icon="📊")
+    st.caption("Timeline with confidence bands, dimension correlation, per-ROI breakdown, comparison mode")
+
+with col2:
+    st.subheader("Advanced Analysis")
+    st.page_link("pages/3_Temporal_Dynamics.py", label="Temporal Dynamics", icon="⏱️")
+    st.caption("Raw timecourses, peak latency hierarchy, optimal lag analysis, cross-ROI lag matrix")
+
+    st.page_link("pages/4_Connectivity.py", label="ROI Connectivity", icon="🔗")
+    st.caption("Partial correlation, modularity, betweenness centrality, dendrogram, network graph")
+
+# --- Analysis Log ---
+show_analysis_log()
+
+st.divider()
+st.caption("[GitHub](https://github.com/siddhant-rajhans/cortexlab) | [HuggingFace](https://huggingface.co/SID2000/cortexlab) | [Dashboard Repo](https://github.com/siddhant-rajhans/cortexlab-dashboard)")