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README.md

@@ -16,11 +16,10 @@ tags:
   - ai-agents
 ---
 
-
 # `gradio_medical_image_analyzer`
-<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.1%20-%20orange"> <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>
+<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.1.0%20-%20orange"> <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>
 
-AI-agent optimized medical image analysis component for Gradio
+AI-agent optimized medical image analysis component for Gradio with DICOM support
 
 ## Installation
 
@@ -55,11 +54,11 @@ def draw_roi_on_image(image, roi_x, roi_y, roi_radius):
         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)
@@ -67,7 +66,7 @@ def draw_roi_on_image(image, roi_x, roi_y, roi_radius):
     # 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):
@@ -77,33 +76,33 @@ def create_fat_overlay(base_image, segmentation_results):
         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.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.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):
@@ -112,68 +111,68 @@ def process_and_analyze(file_obj, modality, task, roi_x, roi_y, roi_radius, symp
     """
     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
@@ -183,15 +182,15 @@ def process_and_analyze(file_obj, modality, task, roi_x, roi_y, roi_radius, symp
                 # 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}%",
+                    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}%",
+                    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}%",
+                    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
@@ -199,31 +198,31 @@ def process_and_analyze(file_obj, modality, task, roi_x, roi_y, roi_radius, symp
 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%;
+    <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;
+        
+        <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;
+        
+        <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;
+            <h3 style='color: #1e3a8a !important; 
+                       font-size: 18px; 
+                       font-weight: 600; 
                        margin-bottom: 12px;'>
                 📋 Metadata
             </h3>
@@ -247,21 +246,21 @@ def create_visual_report(results, metadata):
             </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;
+        <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;
+            <h3 style='color: #1e3a8a !important; 
+                       font-size: 18px; 
+                       font-weight: 600; 
                        margin-bottom: 12px;'>
                 🎯 Point Analysis
             </h3>
@@ -271,7 +270,7 @@ def create_visual_report(results, metadata):
                     <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>
@@ -286,12 +285,12 @@ def create_visual_report(results, metadata):
                     <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-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>
@@ -301,38 +300,38 @@ def create_visual_report(results, metadata):
                 </tr>
             </table>
         """
-
+        
         if 'reasoning' in pa:
             html += f"""
-            <div style='margin-top: 12px;
-                        padding: 12px;
-                        background: #dbeafe;
-                        border-left: 3px solid #3b82f6;
+            <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;
+            <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;
+                <h3 style='color: #1e3a8a !important; 
+                           font-size: 18px; 
+                           font-weight: 600; 
                            margin-bottom: 12px;'>
                     🔬 Fat Segmentation Analysis
                 </h3>
@@ -355,12 +354,12 @@ def create_visual_report(results, metadata):
                     </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>
@@ -375,7 +374,7 @@ def create_visual_report(results, metadata):
                         </div>
                     </div>
                 """
-
+                
                 if interp.get('recommendations'):
                     html += """
                     <div style='margin-top: 12px; padding-top: 12px; border-top: 1px solid #e5e7eb;'>
@@ -385,10 +384,10 @@ def create_visual_report(results, metadata):
                     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']
@@ -400,38 +399,38 @@ def create_visual_report(results, metadata):
             '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;
+        <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;
+            <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;
+                    <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;
+            <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;'>
@@ -439,15 +438,15 @@ def create_visual_report(results, metadata):
             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",
+        title="Medical Image Analyzer - Enhanced Demo", 
         theme=gr.themes.Soft(
             primary_hue="blue",
             secondary_hue="blue",
@@ -486,12 +485,12 @@ def create_demo():
             --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;
@@ -502,13 +501,13 @@ def create_demo():
             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;
@@ -517,64 +516,64 @@ def create_demo():
             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;
         }
@@ -582,10 +581,10 @@ def create_demo():
     ) 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
@@ -600,11 +599,11 @@ def create_demo():
                     )
                     gr.Markdown("""
                     <small style='color: #666;'>
-                    Accepts: DICOM (.dcm, .dicom), Images (.png, .jpg, .jpeg, .tiff, .bmp),
+                    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"],
@@ -612,7 +611,7 @@ def create_demo():
                     label="Modality",
                     info="Will be auto-detected for DICOM files"
                 )
-
+                
                 # Task selection
                 task = gr.Dropdown(
                     choices=[
@@ -623,13 +622,13 @@ def create_demo():
                     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(
@@ -639,7 +638,7 @@ def create_demo():
                         ],
                         label="Symptoms/Indication"
                     )
-
+                
                 # Visualization options
                 with gr.Accordion("🎨 Visualization Options", open=True):
                     show_overlay = gr.Checkbox(
@@ -647,25 +646,25 @@ def create_demo():
                         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("""
@@ -674,51 +673,51 @@ def create_demo():
             - **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__":
@@ -995,3 +994,4 @@ The code snippet below is accurate in cases where the component is used as both
  ) -> typing.Dict[str, typing.Any][str, typing.Any]:
      return value
  ```
+ 

space.py

@@ -21,10 +21,10 @@ with gr.Blocks(
 # `gradio_medical_image_analyzer`
 
 <div style="display: flex; gap: 7px;">
-<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.1%20-%20orange"> <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>
+<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.1.0%20-%20orange"> <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
+AI-agent optimized medical image analysis component for Gradio with DICOM support
 """, elem_classes=["md-custom"], header_links=True)
     app.render()
     gr.Markdown(

src/README.md

@@ -16,11 +16,10 @@ tags:
   - ai-agents
 ---
 
-
 # `gradio_medical_image_analyzer`
-<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.1%20-%20orange"> <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>
+<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.1.0%20-%20orange"> <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>
 
-AI-agent optimized medical image analysis component for Gradio
+AI-agent optimized medical image analysis component for Gradio with DICOM support
 
 ## Installation
 
@@ -55,11 +54,11 @@ def draw_roi_on_image(image, roi_x, roi_y, roi_radius):
         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)
@@ -67,7 +66,7 @@ def draw_roi_on_image(image, roi_x, roi_y, roi_radius):
     # 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):
@@ -77,33 +76,33 @@ def create_fat_overlay(base_image, segmentation_results):
         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.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.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):
@@ -112,68 +111,68 @@ def process_and_analyze(file_obj, modality, task, roi_x, roi_y, roi_radius, symp
     """
     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
@@ -183,15 +182,15 @@ def process_and_analyze(file_obj, modality, task, roi_x, roi_y, roi_radius, symp
                 # 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}%",
+                    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}%",
+                    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}%",
+                    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
@@ -199,31 +198,31 @@ def process_and_analyze(file_obj, modality, task, roi_x, roi_y, roi_radius, symp
 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%;
+    <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;
+        
+        <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;
+        
+        <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;
+            <h3 style='color: #1e3a8a !important; 
+                       font-size: 18px; 
+                       font-weight: 600; 
                        margin-bottom: 12px;'>
                 📋 Metadata
             </h3>
@@ -247,21 +246,21 @@ def create_visual_report(results, metadata):
             </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;
+        <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;
+            <h3 style='color: #1e3a8a !important; 
+                       font-size: 18px; 
+                       font-weight: 600; 
                        margin-bottom: 12px;'>
                 🎯 Point Analysis
             </h3>
@@ -271,7 +270,7 @@ def create_visual_report(results, metadata):
                     <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>
@@ -286,12 +285,12 @@ def create_visual_report(results, metadata):
                     <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-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>
@@ -301,38 +300,38 @@ def create_visual_report(results, metadata):
                 </tr>
             </table>
         """
-
+        
         if 'reasoning' in pa:
             html += f"""
-            <div style='margin-top: 12px;
-                        padding: 12px;
-                        background: #dbeafe;
-                        border-left: 3px solid #3b82f6;
+            <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;
+            <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;
+                <h3 style='color: #1e3a8a !important; 
+                           font-size: 18px; 
+                           font-weight: 600; 
                            margin-bottom: 12px;'>
                     🔬 Fat Segmentation Analysis
                 </h3>
@@ -355,12 +354,12 @@ def create_visual_report(results, metadata):
                     </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>
@@ -375,7 +374,7 @@ def create_visual_report(results, metadata):
                         </div>
                     </div>
                 """
-
+                
                 if interp.get('recommendations'):
                     html += """
                     <div style='margin-top: 12px; padding-top: 12px; border-top: 1px solid #e5e7eb;'>
@@ -385,10 +384,10 @@ def create_visual_report(results, metadata):
                     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']
@@ -400,38 +399,38 @@ def create_visual_report(results, metadata):
             '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;
+        <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;
+            <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;
+                    <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;
+            <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;'>
@@ -439,15 +438,15 @@ def create_visual_report(results, metadata):
             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",
+        title="Medical Image Analyzer - Enhanced Demo", 
         theme=gr.themes.Soft(
             primary_hue="blue",
             secondary_hue="blue",
@@ -486,12 +485,12 @@ def create_demo():
             --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;
@@ -502,13 +501,13 @@ def create_demo():
             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;
@@ -517,64 +516,64 @@ def create_demo():
             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;
         }
@@ -582,10 +581,10 @@ def create_demo():
     ) 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
@@ -600,11 +599,11 @@ def create_demo():
                     )
                     gr.Markdown("""
                     <small style='color: #666;'>
-                    Accepts: DICOM (.dcm, .dicom), Images (.png, .jpg, .jpeg, .tiff, .bmp),
+                    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"],
@@ -612,7 +611,7 @@ def create_demo():
                     label="Modality",
                     info="Will be auto-detected for DICOM files"
                 )
-
+                
                 # Task selection
                 task = gr.Dropdown(
                     choices=[
@@ -623,13 +622,13 @@ def create_demo():
                     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(
@@ -639,7 +638,7 @@ def create_demo():
                         ],
                         label="Symptoms/Indication"
                     )
-
+                
                 # Visualization options
                 with gr.Accordion("🎨 Visualization Options", open=True):
                     show_overlay = gr.Checkbox(
@@ -647,25 +646,25 @@ def create_demo():
                         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("""
@@ -674,51 +673,51 @@ def create_demo():
             - **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__":
@@ -995,3 +994,4 @@ The code snippet below is accurate in cases where the component is used as both
  ) -> typing.Dict[str, typing.Any][str, typing.Any]:
      return value
  ```
+ 

src/backend/gradio_medical_image_analyzer/medical_image_analyzer.py

@@ -494,16 +494,22 @@ class MedicalImageAnalyzer(Component):
         # How close is center value to mean
         center_deviation = abs(center_value - mean) / std
         
-        # Coefficient of variation
+        # Coefficient of variation (normalized)
         cv = std / (abs(mean) + 1e-6)
         
-        # Lower CV = more homogeneous = higher confidence
-        # Also consider if center value is close to mean
-        confidence = max(0.0, min(1.0, 1.0 - cv))
+        # Base confidence from homogeneity (sigmoid-like transformation)
+        # CV of 0.1 = ~95% confidence, CV of 0.5 = ~70% confidence, CV of 1.0 = ~50% confidence
+        base_confidence = 1.0 / (1.0 + cv * 2.0)
         
-        # Reduce confidence if center is far from mean
-        if center_deviation > 2:  # More than 2 standard deviations
-            confidence *= 0.8
+        # Adjust based on center deviation
+        # If center is close to mean, increase confidence
+        deviation_factor = 1.0 / (1.0 + center_deviation * 0.5)
+        
+        # Combine factors
+        confidence = base_confidence * 0.7 + deviation_factor * 0.3
+        
+        # Ensure reasonable minimum confidence for valid detections
+        confidence = max(0.5, min(0.99, confidence))
         
         return round(confidence, 2)
     

src/backend/gradio_medical_image_analyzer/medical_image_analyzer.pyi

@@ -488,16 +488,22 @@ class MedicalImageAnalyzer(Component):
         # How close is center value to mean
         center_deviation = abs(center_value - mean) / std
         
-        # Coefficient of variation
+        # Coefficient of variation (normalized)
         cv = std / (abs(mean) + 1e-6)
         
-        # Lower CV = more homogeneous = higher confidence
-        # Also consider if center value is close to mean
-        confidence = max(0.0, min(1.0, 1.0 - cv))
+        # Base confidence from homogeneity (sigmoid-like transformation)
+        # CV of 0.1 = ~95% confidence, CV of 0.5 = ~70% confidence, CV of 1.0 = ~50% confidence
+        base_confidence = 1.0 / (1.0 + cv * 2.0)
         
-        # Reduce confidence if center is far from mean
-        if center_deviation > 2:  # More than 2 standard deviations
-            confidence *= 0.8
+        # Adjust based on center deviation
+        # If center is close to mean, increase confidence
+        deviation_factor = 1.0 / (1.0 + center_deviation * 0.5)
+        
+        # Combine factors
+        confidence = base_confidence * 0.7 + deviation_factor * 0.3
+        
+        # Ensure reasonable minimum confidence for valid detections
+        confidence = max(0.5, min(0.99, confidence))
         
         return round(confidence, 2)
     

src/demo/README.md

@@ -3,11 +3,30 @@
 
 This folder contains demo applications for the `gradio_medical_image_analyzer` custom component.
 
+
 ## ⚠️ IMPORTANT MEDICAL DISCLAIMER ⚠️
 
 **THIS SOFTWARE IS FOR RESEARCH AND EDUCATIONAL PURPOSES ONLY**
 
-See the main README for full disclaimer. This demo is NOT for clinical use.
+🚨 **DO NOT USE FOR CLINICAL DIAGNOSIS OR MEDICAL DECISION MAKING** 🚨
+
+This component is in **EARLY DEVELOPMENT** and is intended as a **proof of concept** for medical image analysis integration with Gradio. The results produced by this software:
+
+- **ARE NOT** validated for clinical use
+- **ARE NOT** FDA approved or CE marked
+- **SHOULD NOT** be used for patient diagnosis or treatment decisions
+- **SHOULD NOT** replace professional medical judgment
+- **MAY CONTAIN** significant errors or inaccuracies
+- **ARE PROVIDED** without any warranty of accuracy or fitness for medical purposes
+
+**ALWAYS CONSULT QUALIFIED HEALTHCARE PROFESSIONALS** for medical image interpretation and clinical decisions. This software is intended solely for:
+- Research and development purposes
+- Educational demonstrations
+- Technical integration testing
+- Non-clinical experimental use
+
+By using this software, you acknowledge that you understand these limitations and agree not to use it for any clinical or medical diagnostic purposes.
+
 
 ## Demo Files
 
@@ -87,7 +106,7 @@ If you encounter issues:
 1. Ensure all dependencies are installed: `pip install -r requirements.txt`
 2. Check that you have the correct Python version (3.8+)
 3. For DICOM files, ensure they are valid medical images
-4. Report issues at: https://github.com/datadudech/gradio-medical-image-analyzer/issues
+4. Report issues at: https://github.com/thedatadudech/gradio-medical-image-analyzer/issues
 
 ---
 

src/demo/space.py

@@ -21,10 +21,10 @@ with gr.Blocks(
 # `gradio_medical_image_analyzer`
 
 <div style="display: flex; gap: 7px;">
-<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.1%20-%20orange"> <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>
+<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.1.0%20-%20orange"> <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
+AI-agent optimized medical image analysis component for Gradio with DICOM support
 """, elem_classes=["md-custom"], header_links=True)
     app.render()
     gr.Markdown(

src/pyproject.toml

@@ -8,10 +8,10 @@ build-backend = "hatchling.build"
 
 [project]
 name = "gradio_medical_image_analyzer"
-version = "0.0.1"
-description = "AI-agent optimized medical image analysis component for Gradio"
+version = "0.1.0"
+description = "AI-agent optimized medical image analysis component for Gradio with DICOM support"
 readme = "README.md"
-license = "Apache-2.0"
+license = {text = "Apache-2.0"}
 requires-python = ">=3.8"
 authors = [{ name = "Markus Clauss Vetsuisse Uni Zurich", email = "markus@data-and-ai-dude.ch" }]
 keywords = [
@@ -19,7 +19,14 @@ keywords = [
   "medical-imaging",
   "ai-agents",
   "image-analysis",
-  "gradio-template-MedicalImageAnalyzer"
+  "gradio-template-MedicalImageAnalyzer",
+  "dicom",
+  "veterinary",
+  "ct-scan",
+  "x-ray",
+  "fat-segmentation",
+  "medical-ai",
+  "hackathon-2025"
 ]
 # Add dependencies here
 dependencies = [
@@ -40,8 +47,15 @@ classifiers = [
   'Programming Language :: Python :: 3.9',
   'Programming Language :: Python :: 3.10',
   'Programming Language :: Python :: 3.11',
+  'Programming Language :: Python :: 3.12',
   'Topic :: Scientific/Engineering :: Medical Science Apps.',
   'Topic :: Scientific/Engineering :: Artificial Intelligence',
+  'Topic :: Scientific/Engineering :: Image Processing',
+  'Topic :: Software Development :: Libraries :: Python Modules',
+  'Intended Audience :: Healthcare Industry',
+  'Intended Audience :: Science/Research',
+  'Intended Audience :: Developers',
+  'License :: OSI Approved :: Apache Software License',
 ]
 
 [project.urls]