🏥 Medical Image Analysis Report
📋 Metadata
| File Type: |
{metadata.get('file_type', 'Unknown')} |
| Modality: |
{results.get('modality', 'Unknown')} |
| Image Size: |
{metadata.get('shape', 'Unknown')} |
| Timestamp: |
{results.get('timestamp', 'N/A')} |
"""
# Point Analysis
if 'point_analysis' in results:
pa = results['point_analysis']
tissue = pa.get('tissue_type', {})
html += f"""
🎯 Point Analysis
| Position: |
({pa.get('location', {}).get('x', 'N/A')}, {pa.get('location', {}).get('y', 'N/A')}) |
"""
if results.get('modality') == 'CT':
html += f"""
| HU Value: |
{pa.get('hu_value', 'N/A'):.1f} |
"""
else:
html += f"""
| Intensity: |
{pa.get('intensity', 'N/A'):.3f} |
"""
html += f"""
| Tissue Type: |
{tissue.get('icon', '')}
{tissue.get('type', 'Unknown').replace('_', ' ')}
|
| Confidence: |
{pa.get('confidence', 'N/A')} |
"""
if 'reasoning' in pa:
html += f"""
"""
html += "
"
# Segmentation Results
if 'segmentation' in results and results['segmentation']:
seg = results['segmentation']
if 'statistics' in seg:
# Fat segmentation for CT
stats = seg['statistics']
html += f"""
🔬 Fat Segmentation Analysis
Total Fat
{stats.get('total_fat_percentage', 0):.1f}%
Subcutaneous
{stats.get('subcutaneous_fat_percentage', 0):.1f}%
Visceral
{stats.get('visceral_fat_percentage', 0):.1f}%
V/S Ratio
{stats.get('visceral_subcutaneous_ratio', 0):.2f}
"""
if 'interpretation' in seg:
interp = seg['interpretation']
obesity_color = "#16a34a" if interp.get("obesity_risk") == "normal" else "#d97706" if interp.get("obesity_risk") == "moderate" else "#dc2626"
visceral_color = "#16a34a" if interp.get("visceral_risk") == "normal" else "#d97706" if interp.get("visceral_risk") == "moderate" else "#dc2626"
html += f"""
Risk Assessment
Obesity Risk:
{interp.get('obesity_risk', 'N/A').upper()}
Visceral Risk:
{interp.get('visceral_risk', 'N/A').upper()}
"""
if interp.get('recommendations'):
html += """
💡 Recommendations
"""
for rec in interp['recommendations']:
html += f"- {rec}
"
html += "
"
html += "
"
html += "
"
# Quality Assessment
if 'quality_metrics' in results:
quality = results['quality_metrics']
quality_colors = {
'excellent': '#16a34a',
'good': '#16a34a',
'fair': '#d97706',
'poor': '#dc2626',
'unknown': '#6b7280'
}
q_color = quality_colors.get(quality.get('overall_quality', 'unknown'), '#6b7280')
html += f"""
📊 Image Quality Assessment
Overall Quality:
{quality.get('overall_quality', 'unknown').upper()}
"""
if quality.get('issues'):
html += f"""
Issues Detected:
"""
for issue in quality['issues']:
html += f"- {issue}
"
html += "
"
html += "
"
html += "
"
return html
def create_demo():
with gr.Blocks(
title="Medical Image Analyzer - Enhanced Demo",
theme=gr.themes.Soft(
primary_hue="blue",
secondary_hue="blue",
neutral_hue="slate",
text_size="md",
spacing_size="md",
radius_size="md",
).set(
# Medical blue theme colors
body_background_fill="*neutral_950",
body_background_fill_dark="*neutral_950",
block_background_fill="*neutral_900",
block_background_fill_dark="*neutral_900",
border_color_primary="*primary_600",
border_color_primary_dark="*primary_600",
# Text colors for better contrast
body_text_color="*neutral_100",
body_text_color_dark="*neutral_100",
body_text_color_subdued="*neutral_300",
body_text_color_subdued_dark="*neutral_300",
# Button colors
button_primary_background_fill="*primary_600",
button_primary_background_fill_dark="*primary_600",
button_primary_text_color="white",
button_primary_text_color_dark="white",
),
css="""
/* Medical blue theme with high contrast */
:root {
--medical-blue: #1e40af;
--medical-blue-light: #3b82f6;
--medical-blue-dark: #1e3a8a;
--text-primary: #f9fafb;
--text-secondary: #e5e7eb;
--bg-primary: #0f172a;
--bg-secondary: #1e293b;
--bg-tertiary: #334155;
}
/* Override default text colors for medical theme */
* {
color: var(--text-primary) !important;
}
/* Style the file upload area */
.file-upload {
border: 2px dashed var(--medical-blue-light) !important;
border-radius: 8px !important;
padding: 20px !important;
text-align: center !important;
background: var(--bg-secondary) !important;
transition: all 0.3s ease !important;
color: var(--text-primary) !important;
}
.file-upload:hover {
border-color: var(--medical-blue) !important;
background: var(--bg-tertiary) !important;
box-shadow: 0 0 20px rgba(59, 130, 246, 0.2) !important;
}
/* Ensure report text is readable with white background */
.medical-report {
background: #ffffff !important;
border: 2px solid var(--medical-blue-light) !important;
border-radius: 8px !important;
padding: 16px !important;
color: #1a1a1a !important;
}
.medical-report * {
color: #1f2937 !important; /* Dark gray text */
}
.medical-report h2 {
color: #1e40af !important; /* Medical blue for main heading */
}
.medical-report h3, .medical-report h4 {
color: #1e3a8a !important; /* Darker medical blue for subheadings */
}
.medical-report strong {
color: #374151 !important; /* Darker gray for labels */
}
.medical-report td {
color: #1f2937 !important; /* Ensure table text is dark */
}
/* Report sections with light blue background */
.medical-report > div {
background: #f0f9ff !important;
color: #1f2937 !important;
}
/* Medical blue accents for UI elements */
.gr-button-primary {
background: var(--medical-blue) !important;
border-color: var(--medical-blue) !important;
}
.gr-button-primary:hover {
background: var(--medical-blue-dark) !important;
border-color: var(--medical-blue-dark) !important;
}
/* Tab styling */
.gr-tab-item {
border-color: var(--medical-blue-light) !important;
}
.gr-tab-item.selected {
background: var(--medical-blue) !important;
color: white !important;
}
/* Accordion styling */
.gr-accordion {
border-color: var(--medical-blue-light) !important;
}
/* Slider track in medical blue */
input[type="range"]::-webkit-slider-track {
background: var(--bg-tertiary) !important;
}
input[type="range"]::-webkit-slider-thumb {
background: var(--medical-blue) !important;
}
"""
) as demo:
gr.Markdown("""
# 🏥 Medical Image Analyzer
Supports **DICOM** (.dcm) and all image formats with automatic modality detection!
""")
with gr.Row():
with gr.Column(scale=1):
# File upload - no file type restrictions
with gr.Group():
gr.Markdown("### 📤 Upload Medical Image")
file_input = gr.File(
label="Select Medical Image File (.dcm, .dicom, IM_*, .png, .jpg, etc.)",
file_count="single",
type="filepath",
elem_classes="file-upload"
# Note: NO file_types parameter = accepts ALL files
)
gr.Markdown("""