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import gradio as gr
from app import demo as app
import os
_docs = {'MedicalImageAnalyzer': {'description': 'A Gradio component for AI-agent compatible medical image analysis.\n\nProvides structured output for:\n- HU value analysis (CT only)\n- Tissue classification\n- Fat segmentation (subcutaneous, visceral)\n- Confidence scores and reasoning', 'members': {'__init__': {'value': {'type': 'typing.Optional[typing.Dict[str, typing.Any]][\n typing.Dict[str, typing.Any][str, typing.Any], None\n]', 'default': 'None', 'description': None}, 'label': {'type': 'typing.Optional[str][str, None]', 'default': 'None', 'description': None}, 'info': {'type': 'typing.Optional[str][str, None]', 'default': 'None', 'description': None}, 'every': {'type': 'typing.Optional[float][float, None]', 'default': 'None', 'description': None}, 'show_label': {'type': 'typing.Optional[bool][bool, None]', 'default': 'None', 'description': None}, 'container': {'type': 'typing.Optional[bool][bool, None]', 'default': 'None', 'description': None}, 'scale': {'type': 'typing.Optional[int][int, None]', 'default': 'None', 'description': None}, 'min_width': {'type': 'typing.Optional[int][int, None]', 'default': 'None', 'description': None}, 'visible': {'type': 'typing.Optional[bool][bool, None]', 'default': 'None', 'description': None}, 'elem_id': {'type': 'typing.Optional[str][str, None]', 'default': 'None', 'description': None}, 'elem_classes': {'type': 'typing.Union[typing.List[str], str, NoneType][\n typing.List[str][str], str, None\n]', 'default': 'None', 'description': None}, 'render': {'type': 'typing.Optional[bool][bool, None]', 'default': 'None', 'description': None}, 'key': {'type': 'typing.Union[int, str, NoneType][int, str, None]', 'default': 'None', 'description': None}, 'analysis_mode': {'type': 'str', 'default': '"structured"', 'description': '"structured" for AI agents, "visual" for human interpretation'}, 'include_confidence': {'type': 'bool', 'default': 'True', 'description': 'Include confidence scores in results'}, 'include_reasoning': {'type': 'bool', 'default': 'True', 'description': 'Include reasoning/explanation for findings'}, 'segmentation_types': {'type': 'typing.List[str][str]', 'default': 'None', 'description': 'List of segmentation types to perform'}}, 'postprocess': {'value': {'type': 'typing.Dict[str, typing.Any][str, typing.Any]', 'description': None}}, 'preprocess': {'return': {'type': 'typing.Dict[str, typing.Any][str, typing.Any]', 'description': None}, 'value': None}}, 'events': {'change': {'type': None, 'default': None, 'description': 'Triggered when the value of the MedicalImageAnalyzer changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.'}, 'select': {'type': None, 'default': None, 'description': 'Event listener for when the user selects or deselects the MedicalImageAnalyzer. Uses event data gradio.SelectData to carry `value` referring to the label of the MedicalImageAnalyzer, and `selected` to refer to state of the MedicalImageAnalyzer. See EventData documentation on how to use this event data'}, 'upload': {'type': None, 'default': None, 'description': 'This listener is triggered when the user uploads a file into the MedicalImageAnalyzer.'}, 'clear': {'type': None, 'default': None, 'description': 'This listener is triggered when the user clears the MedicalImageAnalyzer using the clear button for the component.'}}}, '__meta__': {'additional_interfaces': {}, 'user_fn_refs': {'MedicalImageAnalyzer': []}}}
abs_path = os.path.join(os.path.dirname(__file__), "css.css")
with gr.Blocks(
css=abs_path,
theme=gr.themes.Default(
font_mono=[
gr.themes.GoogleFont("Inconsolata"),
"monospace",
],
),
) as demo:
gr.Markdown(
"""
# `gradio_medical_image_analyzer`
<div style="display: flex; gap: 7px;">
<a href="https://pypi.org/project/gradio_medical_image_analyzer/" target="_blank"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/gradio_medical_image_analyzer"></a> <a href="https://github.com/thedatadudech/gradio-medical-image-analyzer/issues" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/Issues-white?logo=github&logoColor=black"></a> <a href="https://huggingface.co/spaces/AbdullahIsaMarkus/gradio_medical_image_analyzer/discussions" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/%F0%9F%A4%97%20Discuss-%23097EFF?style=flat&logoColor=black"></a>
</div>
AI-agent optimized medical image analysis component for Gradio with DICOM support
""", elem_classes=["md-custom"], header_links=True)
app.render()
gr.Markdown(
"""
## Installation
```bash
pip install gradio_medical_image_analyzer
```
## Usage
```python
#!/usr/bin/env python3
\"\"\"
Demo for MedicalImageAnalyzer - Enhanced with file upload and overlay visualization
\"\"\"
import gradio as gr
import numpy as np
import sys
import os
import cv2
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
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, modality, 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, {}, 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 if it's a DICOM
if metadata.get('file_type') == 'DICOM' and 'modality' in metadata:
modality = metadata['modality']
# 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"] = {"symptoms": symptoms}
# Perform analysis
results = analyzer.analyze_image(**analysis_params)
# Create 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_scaled, roi_y_scaled, roi_radius)
# For fat segmentation overlay (simplified version since we don't have masks in current implementation)
elif task == "segment_fat" and 'segmentation' in results and modality == 'CT':
# For now, just draw ROI since we don't have actual masks
overlay_image = display_array.copy()
if len(overlay_image.shape) == 2:
overlay_image = cv2.cvtColor(overlay_image, cv2.COLOR_GRAY2RGB)
# Add text overlay about fat percentages
if 'statistics' in results['segmentation']:
stats = results['segmentation']['statistics']
cv2.putText(overlay_image, f"Total Fat: {stats.get('total_fat_percentage', 0):.1f}%",
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
cv2.putText(overlay_image, f"Subcutaneous: {stats.get('subcutaneous_fat_percentage', 0):.1f}%",
(10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
cv2.putText(overlay_image, f"Visceral: {stats.get('visceral_fat_percentage', 0):.1f}%",
(10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
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: #dbeafe;
border-left: 3px solid #3b82f6;
border-radius: 4px;'>
<p style='margin: 0; color: #1e40af !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 = "#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\"\"\"
<div style='margin-top: 16px; padding: 16px; background: #f3f4f6; border-radius: 6px;'>
<h4 style='color: #374151 !important; 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: #6b7280 !important; font-size: 14px;'>Obesity Risk:</span>
<span style='color: {obesity_color} !important; font-weight: 600; margin-left: 8px;'>{interp.get('obesity_risk', 'N/A').upper()}</span>
</div>
<div>
<span style='color: #6b7280 !important; font-size: 14px;'>Visceral Risk:</span>
<span style='color: {visceral_color} !important; 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 #e5e7eb;'>
<h5 style='color: #374151 !important; font-size: 14px; font-weight: 600; margin-bottom: 8px;'>💡 Recommendations</h5>
<ul style='margin: 0; padding-left: 20px; color: #4b5563 !important;'>
\"\"\"
for rec in interp['recommendations']:
html += f"<li style='margin: 4px 0;'>{rec}</li>"
html += "</ul></div>"
html += "</div>"
html += "</div>"
# 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\"\"\"
<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: #fef3c7;
border-left: 3px solid #f59e0b;
border-radius: 4px;'>
<strong style='color: #92400e !important;'>Issues Detected:</strong>
<ul style='margin: 4px 0 0 0; padding-left: 20px; color: #92400e !important;'>
\"\"\"
for issue in quality['issues']:
html += f"<li style='margin: 2px 0;'>{issue}</li>"
html += "</ul></div>"
html += "</div>"
html += "</div>"
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(\"\"\"
<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>
\"\"\")
# Modality selection
modality = gr.Radio(
choices=["CT", "CR", "DX", "RX", "DR"],
value="CT",
label="Modality",
info="Will be auto-detected for DICOM files"
)
# 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 info 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 based on selected modality
- **All Formats**: Automatic grayscale conversion
- **Files without extension**: Supported (e.g., IM_0001) - will try DICOM first
### 🎯 Usage
1. Upload a medical image file
2. Select modality (auto-detected for DICOM)
3. Choose analysis task
4. Adjust ROI position for point analysis
5. Click "Analyze"
### 💡 Features
- **ROI Visualization**: See the exact area being analyzed
- **Fat Segmentation**: Visual percentages for CT scans
- **Multi-format Support**: Works with any medical image format
- **AI Agent Ready**: Structured JSON output for integration
\"\"\")
# Connect the interface
analyze_btn.click(
fn=process_and_analyze,
inputs=[file_input, modality, 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]
)
return demo
if __name__ == "__main__":
demo = create_demo()
demo.launch()
```
""", elem_classes=["md-custom"], header_links=True)
gr.Markdown("""
## `MedicalImageAnalyzer`
### Initialization
""", elem_classes=["md-custom"], header_links=True)
gr.ParamViewer(value=_docs["MedicalImageAnalyzer"]["members"]["__init__"], linkify=[])
gr.Markdown("### Events")
gr.ParamViewer(value=_docs["MedicalImageAnalyzer"]["events"], linkify=['Event'])
gr.Markdown("""
### User function
The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).
- When used as an Input, the component only impacts the input signature of the user function.
- When used as an output, the component only impacts the return signature of the user function.
The code snippet below is accurate in cases where the component is used as both an input and an output.
```python
def predict(
value: typing.Dict[str, typing.Any][str, typing.Any]
) -> typing.Dict[str, typing.Any][str, typing.Any]:
return value
```
""", elem_classes=["md-custom", "MedicalImageAnalyzer-user-fn"], header_links=True)
demo.load(None, js=r"""function() {
const refs = {};
const user_fn_refs = {
MedicalImageAnalyzer: [], };
requestAnimationFrame(() => {
Object.entries(user_fn_refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}-user-fn`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`<a href="#h-${ref.toLowerCase()}">${ref}</a>`
);
})
}
})
Object.entries(refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`<a href="#h-${ref.toLowerCase()}">${ref}</a>`
);
})
}
})
})
}
""")
demo.launch()
|