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31d5c57 32cf4da 31d5c57 32cf4da e872263 32cf4da e872263 32cf4da e872263 32cf4da 31d5c57 f3aa8aa 31d5c57 f3aa8aa 31d5c57 f3aa8aa 31d5c57 f3aa8aa 31d5c57 32cf4da f3aa8aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | import streamlit as st
import os
import base64
import pandas as pd
import numpy as np
from src.backend.data_loader import get_metadata
def get_base64_of_bin_file(bin_file):
with open(bin_file, 'rb') as f:
data = f.read()
return base64.b64encode(data).decode()
def get_header_stats():
"""Calculate real-time stats for the header banner for ALL organs using correct metadata columns."""
df = get_metadata()
if df.empty:
return {
'human': {'total': "0", 'spots': "0", 'organs': []},
'mouse': {'total': "0", 'spots': "0", 'organs': []}
}
fmt = lambda x: f"{x:,}"
spot_col = 'spots_under_tissue' if 'spots_under_tissue' in df.columns else None
def get_species_stats(species_mask):
spec_df = df[species_mask]
total_samples = len(spec_df)
if spot_col:
spec_df[spot_col] = pd.to_numeric(spec_df[spot_col], errors='coerce').fillna(0)
total_spots = spec_df[spot_col].sum()
else:
total_spots = 0
org_groups = spec_df.groupby('organ')
organs_data = []
for name, group in org_groups:
s_count = len(group)
spots = group[spot_col].sum() if spot_col else 0
organs_data.append({
'name': name.upper(),
'samples': fmt(s_count),
'spots': fmt(int(spots)) if spots > 0 else "0"
})
organs_data.sort(key=lambda x: int(x['samples'].replace(',', '')), reverse=True)
return {
'total': fmt(total_samples),
'spots': fmt(int(total_spots)) if total_spots > 0 else "0",
'organs': organs_data
}
human_mask = df['species'].str.contains('human|homo', case=False, na=False)
mouse_mask = df['species'].str.contains('mouse|mus', case=False, na=False)
return {
'human': get_species_stats(human_mask),
'mouse': get_species_stats(mouse_mask)
}
def render_header():
"""Render a premium atlas header with optimized glassmorphism cards using st.html."""
load_css()
h_img_path = "assets/human_red.png"
m_img_path = "assets/mouse_red.png"
bg_img_path = "assets/network_bg_red.png"
h_base64 = get_base64_of_bin_file(h_img_path) if os.path.exists(h_img_path) else ""
m_base64 = get_base64_of_bin_file(m_img_path) if os.path.exists(m_img_path) else ""
bg_base64 = get_base64_of_bin_file(bg_img_path) if os.path.exists(bg_img_path) else ""
stats = get_header_stats()
def build_circular_organs(organs_list, radius=290):
N = len(organs_list)
html = ""
for i, org in enumerate(organs_list):
angle = (i / N) * 2 * np.pi - (np.pi / 2)
x = radius * np.cos(angle)
y = radius * np.sin(angle)
html += f'''
<div class="circular-bubble" style="transform: translate(calc(-50% + {x}px), calc(-50% + {y}px));">
<div class="bubble-content">
<div class="bubble-org-name">{org['name']}</div>
<div class="bubble-row">
<span class="row-label">Samples:</span>
<span class="row-val">{org['samples']}</span>
</div>
<div class="bubble-row">
<span class="row-label">Spots:</span>
<span class="row-val">{org['spots']}</span>
</div>
</div>
</div>'''
return html
h_bubbles = build_circular_organs(stats['human']['organs'], radius=290)
m_bubbles = build_circular_organs(stats['mouse']['organs'], radius=290)
subtitle = "A spatial atlas of tumour microenvironment metabolism and metabolic interactions inferred by a pretrained self-supervised metabolic hypergraph"
header_html = f"""
<div class="atlas-main-header">
<div class="network-bg" style="background-image: url('data:image/png;base64,{bg_base64}');"></div>
<div class="header-content">
<div class="branding-bar">
<h1 class="brand-title">spMetaTME-Atlas</h1>
<p style="color: #666; font-size: 2rem; font-weight: 500; margin-top: -5px;">{subtitle}</p>
</div>
<div class="atlas-stage">
<!-- HUMAN STAGE -->
<div class="species-stage-box">
<div class="circular-container">{h_bubbles}</div>
<div class="center-figure-group">
<div class="icon-background" style="background-image: url('data:image/png;base64,{bg_base64}');"></div>
<img src="data:image/png;base64,{h_base64}" class="main-silhouette">
<div class="stage-badge">
HUMAN ATLAS
<span class="spots-tag">{stats['human']['total']} Samples | {stats['human']['spots']} Spots</span>
</div>
</div>
</div>
<!-- MOUSE STAGE -->
<div class="species-stage-box">
<div class="circular-container">{m_bubbles}</div>
<div class="center-figure-group">
<div class="icon-background" style="background-image: url('data:image/png;base64,{bg_base64}');"></div>
<img src="data:image/png;base64,{m_base64}" class="main-silhouette">
<div class="stage-badge" style="background: #7d1a1a;">
MOUSE ATLAS
<span class="spots-tag">{stats['mouse']['total']} Samples | {stats['mouse']['spots']} Spots</span>
</div>
</div>
</div>
</div>
</div>
</div>
"""
st.html(header_html)
@st.cache_resource(show_spinner=False)
def load_css():
"""Load and apply CSS - cached to prevent reloading on every rerun."""
css_path = "assets/style.css"
css_content = ""
if os.path.exists(css_path):
with open(css_path) as f:
css_content = f.read()
st.markdown("""
<link href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
""", unsafe_allow_html=True)
if css_content:
st.markdown(f"<style>{css_content}</style>", unsafe_allow_html=True)
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