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#!/usr/bin/env python3
"""
Frontend Wrapper Test für Medical Image Analyzer
Nutzt Standard Gradio Komponenten um die Backend-Funktionalität zu testen
"""
import gradio as gr
import numpy as np
import sys
import os
from pathlib import Path
# Add backend to path
sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(__file__)), 'backend'))
from gradio_medical_image_analyzer import MedicalImageAnalyzer
import cv2
def draw_roi_on_image(image, roi_x, roi_y, roi_radius):
"""Draw ROI circle on the image"""
# Convert to RGB if grayscale
if len(image.shape) == 2:
image_rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
else:
image_rgb = image.copy()
# Draw ROI circle
center = (int(roi_x), int(roi_y))
radius = int(roi_radius)
# Draw outer circle (white)
cv2.circle(image_rgb, center, radius, (255, 255, 255), 2)
# Draw inner circle (red)
cv2.circle(image_rgb, center, radius-1, (255, 0, 0), 2)
# Draw center cross
cv2.line(image_rgb, (center[0]-5, center[1]), (center[0]+5, center[1]), (255, 0, 0), 2)
cv2.line(image_rgb, (center[0], center[1]-5), (center[0], center[1]+5), (255, 0, 0), 2)
return image_rgb
def create_fat_overlay(base_image, segmentation_results):
"""Create overlay image with fat segmentation highlighted"""
# Convert to RGB
if len(base_image.shape) == 2:
overlay_img = cv2.cvtColor(base_image, cv2.COLOR_GRAY2RGB)
else:
overlay_img = base_image.copy()
# Check if we have segmentation masks
if not segmentation_results or 'segments' not in segmentation_results:
return overlay_img
segments = segmentation_results.get('segments', {})
# Apply subcutaneous fat overlay (yellow)
if 'subcutaneous' in segments and segments['subcutaneous'].get('mask') is not None:
mask = segments['subcutaneous']['mask']
yellow_overlay = np.zeros_like(overlay_img)
yellow_overlay[mask > 0] = [255, 255, 0] # Yellow
overlay_img = cv2.addWeighted(overlay_img, 0.7, yellow_overlay, 0.3, 0)
# Apply visceral fat overlay (red)
if 'visceral' in segments and segments['visceral'].get('mask') is not None:
mask = segments['visceral']['mask']
red_overlay = np.zeros_like(overlay_img)
red_overlay[mask > 0] = [255, 0, 0] # Red
overlay_img = cv2.addWeighted(overlay_img, 0.7, red_overlay, 0.3, 0)
# Add legend
cv2.putText(overlay_img, "Yellow: Subcutaneous Fat", (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
cv2.putText(overlay_img, "Red: Visceral Fat", (10, 60),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
return overlay_img
def process_and_analyze(file_obj, task, roi_x, roi_y, roi_radius, symptoms, show_overlay=False):
"""
Processes uploaded file and performs analysis
"""
if file_obj is None:
return None, "No file selected", "", {}, None
# Create analyzer instance
analyzer = MedicalImageAnalyzer(
analysis_mode="structured",
include_confidence=True,
include_reasoning=True
)
try:
# Process the file (DICOM or image)
file_path = file_obj.name if hasattr(file_obj, 'name') else str(file_obj)
pixel_array, display_array, metadata = analyzer.process_file(file_path)
# Update modality from file metadata
modality = metadata.get('modality', 'CR')
# Prepare analysis parameters
analysis_params = {
"image": pixel_array,
"modality": modality,
"task": task
}
# Add ROI if applicable
if task in ["analyze_point", "full_analysis"]:
# Scale ROI coordinates to image size
h, w = pixel_array.shape
roi_x_scaled = int(roi_x * w / 512) # Assuming slider max is 512
roi_y_scaled = int(roi_y * h / 512)
analysis_params["roi"] = {
"x": roi_x_scaled,
"y": roi_y_scaled,
"radius": roi_radius
}
# Add clinical context
if symptoms:
analysis_params["clinical_context"] = ", ".join(symptoms)
# Perform analysis
results = analyzer.analyze_image(**analysis_params)
# Create visual report
if analyzer.analysis_mode == "visual":
visual_report = analyzer._create_html_report(results)
else:
# Create our own visual report
visual_report = create_visual_report(results, metadata)
# Add metadata info
info = f"📄 {metadata.get('file_type', 'Unknown')} | "
info += f"🏥 {modality} | "
info += f"📐 {metadata.get('shape', 'Unknown')}"
if metadata.get('window_center'):
info += f" | Window C:{metadata['window_center']:.0f} W:{metadata['window_width']:.0f}"
# Create overlay image if requested
overlay_image = None
if show_overlay:
# For ROI visualization
if task in ["analyze_point", "full_analysis"] and roi_x and roi_y:
overlay_image = draw_roi_on_image(display_array.copy(), roi_x, roi_y, roi_radius)
# For fat segmentation overlay
if task == "segment_fat" and 'segmentation' in results and modality == 'CT':
# Get segmentation masks from results
seg_results = {
'segments': {
'subcutaneous': {'mask': None},
'visceral': {'mask': None}
}
}
# Check if we have actual mask data
if 'segments' in results['segmentation']:
seg_results = results['segmentation']
overlay_image = create_fat_overlay(display_array.copy(), seg_results)
return display_array, info, visual_report, results, overlay_image
except Exception as e:
error_msg = f"Error: {str(e)}"
return None, error_msg, f"<div style='color: red;'>❌ {error_msg}</div>", {"error": error_msg}, None
def create_visual_report(results, metadata):
"""Creates a visual HTML report with improved styling"""
html = f"""
<div class='medical-report' style='font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
padding: 24px;
background: #ffffff;
border-radius: 12px;
max-width: 100%;
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
color: #1a1a1a !important;'>
<h2 style='color: #1e40af !important;
border-bottom: 3px solid #3b82f6;
padding-bottom: 12px;
margin-bottom: 20px;
font-size: 24px;
font-weight: 600;'>
🏥 Medical Image Analysis Report
</h2>
<div style='background: #f0f9ff;
padding: 20px;
margin: 16px 0;
border-radius: 8px;
box-shadow: 0 1px 3px rgba(0,0,0,0.1);'>
<h3 style='color: #1e3a8a !important;
font-size: 18px;
font-weight: 600;
margin-bottom: 12px;'>
📋 Metadata
</h3>
<table style='width: 100%; border-collapse: collapse;'>
<tr>
<td style='padding: 8px 0; color: #4b5563 !important; width: 40%;'><strong style='color: #374151 !important;'>File Type:</strong></td>
<td style='padding: 8px 0; color: #1f2937 !important;'>{metadata.get('file_type', 'Unknown')}</td>
</tr>
<tr>
<td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Modality:</strong></td>
<td style='padding: 8px 0; color: #1f2937 !important;'>{results.get('modality', 'Unknown')}</td>
</tr>
<tr>
<td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Image Size:</strong></td>
<td style='padding: 8px 0; color: #1f2937 !important;'>{metadata.get('shape', 'Unknown')}</td>
</tr>
<tr>
<td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Timestamp:</strong></td>
<td style='padding: 8px 0; color: #1f2937 !important;'>{results.get('timestamp', 'N/A')}</td>
</tr>
</table>
</div>
"""
# Point Analysis
if 'point_analysis' in results:
pa = results['point_analysis']
tissue = pa.get('tissue_type', {})
html += f"""
<div style='background: #f0f9ff;
padding: 20px;
margin: 16px 0;
border-radius: 8px;
box-shadow: 0 1px 3px rgba(0,0,0,0.1);'>
<h3 style='color: #1e3a8a !important;
font-size: 18px;
font-weight: 600;
margin-bottom: 12px;'>
🎯 Point Analysis
</h3>
<table style='width: 100%; border-collapse: collapse;'>
<tr>
<td style='padding: 8px 0; color: #4b5563 !important; width: 40%;'><strong style='color: #374151 !important;'>Position:</strong></td>
<td style='padding: 8px 0; color: #1f2937 !important;'>({pa.get('location', {}).get('x', 'N/A')}, {pa.get('location', {}).get('y', 'N/A')})</td>
</tr>
"""
if results.get('modality') == 'CT':
html += f"""
<tr>
<td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>HU Value:</strong></td>
<td style='padding: 8px 0; color: #1f2937 !important; font-weight: 500;'>{pa.get('hu_value', 'N/A'):.1f}</td>
</tr>
"""
else:
html += f"""
<tr>
<td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Intensity:</strong></td>
<td style='padding: 8px 0; color: #1f2937 !important;'>{pa.get('intensity', 'N/A'):.3f}</td>
</tr>
"""
html += f"""
<tr>
<td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Tissue Type:</strong></td>
<td style='padding: 8px 0; color: #1f2937 !important;'>
<span style='font-size: 1.3em; vertical-align: middle;'>{tissue.get('icon', '')}</span>
<span style='font-weight: 500; text-transform: capitalize;'>{tissue.get('type', 'Unknown').replace('_', ' ')}</span>
</td>
</tr>
<tr>
<td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Confidence:</strong></td>
<td style='padding: 8px 0; color: #1f2937 !important;'>{pa.get('confidence', 'N/A')}</td>
</tr>
</table>
"""
if 'reasoning' in pa:
html += f"""
<div style='margin-top: 12px;
padding: 12px;
background: #f0f7ff;
border-left: 3px solid #0066cc;
border-radius: 4px;'>
<p style='margin: 0; color: #4b5563 !important; font-style: italic;'>
💭 {pa['reasoning']}
</p>
</div>
"""
html += "</div>"
# 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"""
<div style='background: #f0f9ff;
padding: 20px;
margin: 16px 0;
border-radius: 8px;
box-shadow: 0 1px 3px rgba(0,0,0,0.1);'>
<h3 style='color: #1e3a8a !important;
font-size: 18px;
font-weight: 600;
margin-bottom: 12px;'>
🔬 Fat Segmentation Analysis
</h3>
<div style='display: grid; grid-template-columns: 1fr 1fr; gap: 16px;'>
<div style='padding: 16px; background: #ffffff; border-radius: 6px; border: 1px solid #e5e7eb;'>
<h4 style='color: #6b7280 !important; font-size: 14px; margin: 0 0 8px 0; font-weight: 500;'>Total Fat</h4>
<p style='color: #1f2937 !important; font-size: 24px; font-weight: 600; margin: 0;'>{stats.get('total_fat_percentage', 0):.1f}%</p>
</div>
<div style='padding: 16px; background: #fffbeb; border-radius: 6px; border: 1px solid #fbbf24;'>
<h4 style='color: #92400e !important; font-size: 14px; margin: 0 0 8px 0; font-weight: 500;'>Subcutaneous</h4>
<p style='color: #d97706 !important; font-size: 24px; font-weight: 600; margin: 0;'>{stats.get('subcutaneous_fat_percentage', 0):.1f}%</p>
</div>
<div style='padding: 16px; background: #fef2f2; border-radius: 6px; border: 1px solid #fca5a5;'>
<h4 style='color: #991b1b !important; font-size: 14px; margin: 0 0 8px 0; font-weight: 500;'>Visceral</h4>
<p style='color: #dc2626 !important; font-size: 24px; font-weight: 600; margin: 0;'>{stats.get('visceral_fat_percentage', 0):.1f}%</p>
</div>
<div style='padding: 16px; background: #eff6ff; border-radius: 6px; border: 1px solid #93c5fd;'>
<h4 style='color: #1e3a8a !important; font-size: 14px; margin: 0 0 8px 0; font-weight: 500;'>V/S Ratio</h4>
<p style='color: #1e40af !important; font-size: 24px; font-weight: 600; margin: 0;'>{stats.get('visceral_subcutaneous_ratio', 0):.2f}</p>
</div>
</div>
"""
if 'interpretation' in seg:
interp = seg['interpretation']
obesity_color = "#27ae60" if interp.get("obesity_risk") == "normal" else "#f39c12" if interp.get("obesity_risk") == "moderate" else "#e74c3c"
visceral_color = "#27ae60" if interp.get("visceral_risk") == "normal" else "#f39c12" if interp.get("visceral_risk") == "moderate" else "#e74c3c"
html += f"""
<div style='margin-top: 16px; padding: 16px; background: #f8f9fa; border-radius: 6px;'>
<h4 style='color: #333; font-size: 16px; font-weight: 600; margin-bottom: 8px;'>Risk Assessment</h4>
<div style='display: grid; grid-template-columns: 1fr 1fr; gap: 12px;'>
<div>
<span style='color: #666; font-size: 14px;'>Obesity Risk:</span>
<span style='color: {obesity_color}; font-weight: 600; margin-left: 8px;'>{interp.get('obesity_risk', 'N/A').upper()}</span>
</div>
<div>
<span style='color: #666; font-size: 14px;'>Visceral Risk:</span>
<span style='color: {visceral_color}; font-weight: 600; margin-left: 8px;'>{interp.get('visceral_risk', 'N/A').upper()}</span>
</div>
</div>
"""
if interp.get('recommendations'):
html += """
<div style='margin-top: 12px; padding-top: 12px; border-top: 1px solid #e0e0e0;'>
<h5 style='color: #333; font-size: 14px; font-weight: 600; margin-bottom: 8px;'>💡 Recommendations</h5>
<ul style='margin: 0; padding-left: 20px; color: #555;'>
"""
for rec in interp['recommendations']:
html += f"<li style='margin: 4px 0;'>{rec}</li>"
html += "</ul></div>"
html += "</div>"
html += "</div>"
elif 'tissue_distribution' in seg:
# X-ray tissue distribution
html += f"""
<div style='background: white;
padding: 20px;
margin: 16px 0;
border-radius: 8px;
box-shadow: 0 1px 3px rgba(0,0,0,0.1);'>
<h3 style='color: #333;
font-size: 18px;
font-weight: 600;
margin-bottom: 12px;'>
🦴 Tissue Distribution
</h3>
<div style='display: grid; grid-template-columns: repeat(auto-fit, minmax(120px, 1fr)); gap: 12px;'>
"""
tissue_dist = seg['tissue_distribution']
tissue_icons = {
'bone': '🦴', 'soft_tissue': '🔴', 'air': '🌫️',
'metal': '⚙️', 'fat': '🟡', 'fluid': '💧'
}
tissue_colors = {
'bone': '#fff7e6',
'soft_tissue': '#fee',
'air': '#e6f3ff',
'metal': '#f0f0f0',
'fat': '#fffbe6',
'fluid': '#e6f7ff'
}
for tissue, percentage in tissue_dist.items():
if percentage > 0:
icon = tissue_icons.get(tissue, '📍')
bg_color = tissue_colors.get(tissue, '#f8f9fa')
html += f"""
<div style='text-align: center;
padding: 16px;
background: {bg_color};
border-radius: 8px;
transition: transform 0.2s;'>
<div style='font-size: 2.5em; margin-bottom: 8px;'>{icon}</div>
<div style='font-weight: 600;
color: #333;
font-size: 14px;
margin-bottom: 4px;'>
{tissue.replace('_', ' ').title()}
</div>
<div style='color: #0066cc;
font-size: 20px;
font-weight: 700;'>
{percentage:.1f}%
</div>
</div>
"""
html += "</div>"
if seg.get('clinical_findings'):
html += """
<div style='margin-top: 16px;
padding: 16px;
background: #fff3cd;
border-left: 4px solid #ffc107;
border-radius: 4px;'>
<h4 style='color: #856404;
font-size: 16px;
font-weight: 600;
margin: 0 0 8px 0;'>
⚠️ Clinical Findings
</h4>
<ul style='margin: 0; padding-left: 20px; color: #856404;'>
"""
for finding in seg['clinical_findings']:
html += f"<li style='margin: 4px 0;'>{finding.get('description', 'Unknown finding')}</li>"
html += "</ul></div>"
html += "</div>"
# Quality Assessment
if 'quality_metrics' in results:
quality = results['quality_metrics']
quality_colors = {
'excellent': '#27ae60',
'good': '#27ae60',
'fair': '#f39c12',
'poor': '#e74c3c',
'unknown': '#95a5a6'
}
q_color = quality_colors.get(quality.get('overall_quality', 'unknown'), '#95a5a6')
html += f"""
<div style='background: #f0f9ff;
padding: 20px;
margin: 16px 0;
border-radius: 8px;
box-shadow: 0 1px 3px rgba(0,0,0,0.1);'>
<h3 style='color: #1e3a8a !important;
font-size: 18px;
font-weight: 600;
margin-bottom: 12px;'>
📊 Image Quality Assessment
</h3>
<div style='display: flex; align-items: center; gap: 16px;'>
<div>
<span style='color: #4b5563 !important; font-size: 14px;'>Overall Quality:</span>
<span style='color: {q_color} !important;
font-size: 18px;
font-weight: 700;
margin-left: 8px;'>
{quality.get('overall_quality', 'unknown').upper()}
</span>
</div>
</div>
"""
if quality.get('issues'):
html += f"""
<div style='margin-top: 12px;
padding: 12px;
background: #fff3cd;
border-left: 3px solid #ffc107;
border-radius: 4px;'>
<strong style='color: #856404;'>Issues Detected:</strong>
<ul style='margin: 4px 0 0 0; padding-left: 20px; color: #856404;'>
"""
for issue in quality['issues']:
html += f"<li style='margin: 2px 0;'>{issue}</li>"
html += "</ul></div>"
html += "</div>"
html += "</div>"
return html
# Create Gradio interface
with gr.Blocks(
title="Medical Image Analyzer - Wrapper Test",
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;
}
/* Fix text contrast issues */
.svelte-12ioyct { color: var(--text-primary) !important; }
/* Override table cell colors for better readability */
td {
color: var(--text-primary) !important;
padding: 8px 12px !important;
}
td strong {
color: var(--text-primary) !important;
font-weight: 600 !important;
}
/* Fix upload component text and translate */
.wrap.svelte-12ioyct::before {
content: 'Drop file here' !important;
color: var(--text-primary) !important;
}
.wrap.svelte-12ioyct::after {
content: 'Click to upload' !important;
color: var(--text-secondary) !important;
}
.wrap.svelte-12ioyct span {
display: none !important; /* Hide German text */
}
/* 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;
}
/* Make sure all text in tables is readable */
table {
width: 100%;
border-collapse: collapse;
}
th {
font-weight: 600;
background: var(--bg-tertiary);
padding: 8px 12px;
}
/* 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;
}
/* Accordion styling with medical theme */
.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;
}
/* Tab styling */
.gr-tab-item {
border-color: var(--medical-blue-light) !important;
}
.gr-tab-item.selected {
background: var(--medical-blue) !important;
color: white !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 with custom styling - 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("""
<small style='color: #666;'>
Accepts: DICOM (.dcm, .dicom), Images (.png, .jpg, .jpeg, .tiff, .bmp),
and files without extensions (e.g., IM_0001, IM_0002, etc.)
</small>
""")
# Task selection
task = gr.Dropdown(
choices=[
("🎯 Point Analysis", "analyze_point"),
("🔬 Fat Segmentation (CT only)", "segment_fat"),
("📊 Full Analysis", "full_analysis")
],
value="full_analysis",
label="Analysis Task"
)
# ROI settings
with gr.Accordion("🎯 Region of Interest (ROI)", open=True):
roi_x = gr.Slider(0, 512, 256, label="X Position", step=1)
roi_y = gr.Slider(0, 512, 256, label="Y Position", step=1)
roi_radius = gr.Slider(5, 50, 10, label="Radius", step=1)
# Clinical context
with gr.Accordion("🏥 Clinical Context", open=False):
symptoms = gr.CheckboxGroup(
choices=[
"Dyspnea", "Chest Pain", "Abdominal Pain",
"Trauma", "Obesity Screening", "Routine Check"
],
label="Symptoms/Indication"
)
# Visualization options
with gr.Accordion("🎨 Visualization Options", open=True):
show_overlay = gr.Checkbox(
label="Show ROI/Segmentation Overlay",
value=True,
info="Display ROI circle or fat segmentation overlay on the image"
)
analyze_btn = gr.Button("🔬 Analyze", variant="primary", size="lg")
with gr.Column(scale=2):
# Results with tabs for different views
with gr.Tab("🖼️ Original Image"):
image_display = gr.Image(label="Medical Image", type="numpy")
with gr.Tab("🎯 Overlay View"):
overlay_display = gr.Image(label="Image with Overlay", type="numpy")
file_info = gr.Textbox(label="File Information", lines=1)
with gr.Tab("📊 Visual Report"):
report_html = gr.HTML()
with gr.Tab("🔧 JSON Output"):
json_output = gr.JSON(label="Structured Data for AI Agents")
# Examples and help
with gr.Row():
gr.Markdown("""
### 📁 Supported Formats
- **DICOM**: Automatic HU value extraction and modality detection
- **PNG/JPG**: Interpreted as X-ray (CR) unless filename contains 'CT'
- **All Formats**: Automatic grayscale conversion
- **Files without extension**: Supported (e.g., IM_0001)
### 🎯 Usage
1. Upload a medical image file
2. Select the desired analysis task
3. For point analysis: Adjust the ROI position
4. Click "Analyze"
### 💡 Tips
- The ROI sliders will automatically adjust to your image size
- CT images show HU values, X-rays show intensity values
- Fat segmentation is only available for CT images
""")
# Connect the interface
analyze_btn.click(
fn=process_and_analyze,
inputs=[file_input, task, roi_x, roi_y, roi_radius, symptoms, show_overlay],
outputs=[image_display, file_info, report_html, json_output, overlay_display]
)
# Auto-update ROI limits when image is loaded
def update_roi_on_upload(file_obj):
if file_obj is None:
return gr.update(), gr.update()
try:
analyzer = MedicalImageAnalyzer()
_, _, metadata = analyzer.process_file(file_obj.name if hasattr(file_obj, 'name') else str(file_obj))
if 'shape' in metadata:
h, w = metadata['shape']
return gr.update(maximum=w-1, value=w//2), gr.update(maximum=h-1, value=h//2)
except:
pass
return gr.update(), gr.update()
file_input.change(
fn=update_roi_on_upload,
inputs=[file_input],
outputs=[roi_x, roi_y]
)
# Add custom JavaScript to translate upload text
demo.load(
None,
None,
None,
js="""
() => {
// Wait for the page to load
setTimeout(() => {
// Find and replace German text in upload component
const uploadElements = document.querySelectorAll('.wrap.svelte-12ioyct');
uploadElements.forEach(el => {
if (el.textContent.includes('Datei hier ablegen')) {
el.innerHTML = el.innerHTML
.replace('Datei hier ablegen', 'Drop file here')
.replace('oder', 'or')
.replace('Hochladen', 'Click to upload');
}
});
// Also update the button text if it exists
const uploadButtons = document.querySelectorAll('button');
uploadButtons.forEach(btn => {
if (btn.textContent.includes('Hochladen')) {
btn.textContent = 'Upload';
}
});
}, 100);
}
"""
)
if __name__ == "__main__":
demo.launch() |