Update

backend/app.py

@@ -90,7 +90,6 @@ def generate_ai_summary(abnormal_cells, normal_cells, avg_confidence):
 Observed Cell Counts:
 - {abnormal_cells} Abnormal Cells
 - {normal_cells} Normal Cells
-- Detection Confidence: {avg_confidence:.1f}%
 
 Write a 2-3 sentence professional medical assessment focusing on:
 1. Cell count analysis
@@ -108,7 +107,7 @@ Use objective, scientific language suitable for a pathology report."""
             details=True,
             stop_sequences=["\n\n", "###"]
         )
-        
+
         # Handle different response formats
         if hasattr(response, 'generated_text'):
             return response.generated_text.strip()
@@ -116,21 +115,21 @@ Use objective, scientific language suitable for a pathology report."""
             return response.get('generated_text', '').strip()
         elif isinstance(response, str):
             return response.strip()
-        
+
         # Fallback summary if response format is unexpected
         ratio = abnormal_cells / (abnormal_cells + normal_cells) * 100 if (abnormal_cells + normal_cells) > 0 else 0
-        return f"Analysis shows {abnormal_cells} abnormal cells ({ratio:.1f}%) and {normal_cells} normal cells, with average detection confidence of {avg_confidence:.1f}%."
+        return f"Analysis shows {abnormal_cells} abnormal cells ({ratio:.1f}%) and {normal_cells} normal cells."
         
     except Exception as e:
         # Provide a structured fallback summary instead of error message
         total = abnormal_cells + normal_cells
         if total == 0:
             return "No cells were detected in the sample. Consider re-scanning or adjusting detection parameters."
-        
+
         ratio = (abnormal_cells / total) * 100
         severity = "high" if ratio > 70 else "moderate" if ratio > 30 else "low"
-        
-        return f"Quantitative analysis detected {abnormal_cells} abnormal cells ({ratio:.1f}%) among {total} total cells, indicating {severity} abnormality ratio. Average detection confidence: {avg_confidence:.1f}%."
+
+        return f"Quantitative analysis detected {abnormal_cells} abnormal cells ({ratio:.1f}%) among {total} total cells, indicating {severity} abnormality ratio."
 
 
 def generate_mwt_summary(predicted_label, confidences, avg_confidence):
@@ -144,7 +143,6 @@ You are a concise cytopathology expert. Given an MWT classifier result, write a
 
 Result:
 - Predicted label: {predicted_label}
-- Confidence (average): {avg_confidence:.1f}%
 - Class probabilities: {json.dumps(confidences)}
 
 Provide guidance on the significance of the result and any suggested next steps in plain, objective language.
@@ -166,9 +164,9 @@ Provide guidance on the significance of the result and any suggested next steps
         elif isinstance(response, str):
             return response.strip()
 
-        return f"Result: {predicted_label} (avg confidence {avg_confidence:.1f}%)."
+        return f"Result: {predicted_label}."
     except Exception as e:
-        return f"Quantitative result: {predicted_label} with average confidence {avg_confidence:.1f}%."
+        return f"Quantitative result: {predicted_label}." 
 
 
 def generate_cin_summary(predicted_grade, confidences, avg_confidence):
@@ -182,7 +180,6 @@ You are a concise gynecologic pathology expert. Given a CIN classifier result, w
 
 Result:
 - Predicted grade: {predicted_grade}
-- Confidence (average): {avg_confidence:.1f}%
 - Class probabilities: {json.dumps(confidences)}
 
 Provide a brief statement about clinical significance and suggested next steps (e.g., further colposcopic evaluation) in objective, clinical language.
@@ -204,9 +201,9 @@ Provide a brief statement about clinical significance and suggested next steps (
         elif isinstance(response, str):
             return response.strip()
 
-        return f"Result: {predicted_grade} (avg confidence {avg_confidence:.1f}%)."
+        return f"Result: {predicted_grade}."
     except Exception:
-        return f"Quantitative result: {predicted_grade} with average confidence {avg_confidence:.1f}%."
+        return f"Quantitative result: {predicted_grade}." 
 
 
 # =====================================================
@@ -392,14 +389,13 @@ def predict_histopathology(image):
         # Return confidence as dictionary with both class probabilities (like MWT/CIN)
         confidences = {class_names[i]: float(prediction_proba[i]) for i in range(len(class_names))}
         avg_confidence = float(np.max(prediction_proba)) * 100
-        
+
         return {
             "model_used": "Histopathology Classifier",
             "prediction": class_names[predicted_class],
             "confidence": confidences,
             "summary": {
-                "avg_confidence": round(avg_confidence, 2),
-                "ai_interpretation": f"Histopathological analysis indicates {class_names[predicted_class].lower()} tissue with {avg_confidence:.1f}% confidence.",
+                "ai_interpretation": f"Histopathological analysis indicates {class_names[predicted_class].lower()} tissue.",
             },
         }
     except Exception as e:
@@ -475,7 +471,6 @@ async def predict(model_name: str = Form(...), file: UploadFile = File(...)):
             "summary": {
                 "abnormal_cells": abnormal_cells,
                 "normal_cells": normal_cells,
-                "avg_confidence": round(float(avg_confidence), 2),
                 "ai_interpretation": ai_summary,
             },
         }
@@ -498,7 +493,6 @@ async def predict(model_name: str = Form(...), file: UploadFile = File(...)):
             "prediction": predicted_label,
             "confidence": confidences,
             "summary": {
-                "avg_confidence": round(avg_confidence, 2),
                 "ai_interpretation": ai_interp,
             },
         }
@@ -596,7 +590,6 @@ async def predict(model_name: str = Form(...), file: UploadFile = File(...)):
             "prediction": predicted_label,
             "confidence": confidences,
             "summary": {
-                "avg_confidence": round(avg_confidence, 2),
                 "ai_interpretation": ai_interp,
             },
         }
@@ -690,7 +683,6 @@ def create_designed_pdf(pdf_path, report_data, analysis_summary_json, annotated_
     # AI Analysis Section
     story.append(Paragraph("AI-ASSISTED ANALYSIS", styles['Section']))
     story.append(Paragraph("<b>System:</b> Manalife AI System — Automated Analysis", styles['NormalSmall']))
-    story.append(Paragraph(f"<b>Confidence Score:</b> {ai_summary.get('avg_confidence', 'N/A')}%", styles['NormalSmall']))
     
     # Add metrics based on report type
     if report_type == "HISTOPATHOLOGY":
@@ -885,7 +877,6 @@ async def generate_report(
             
             analysis_metrics_html = f"""
             <tr><th>System</th><td>Manalife AI System — Automated Analysis</td></tr>
-            <tr><th>Confidence Score</th><td>{ai_summary.get('avg_confidence', 'N/A')}%</td></tr>
             <tr><th>Benign Confidence</th><td>{benign_conf:.2f}%</td></tr>
             <tr><th>Malignant Confidence</th><td>{malignant_conf:.2f}%</td></tr>
             """
@@ -893,7 +884,6 @@ async def generate_report(
             # For cytology (YOLO), show abnormal/normal cells
             analysis_metrics_html = f"""
             <tr><th>System</th><td>Manalife AI System — Automated Analysis</td></tr>
-            <tr><th>Confidence Score</th><td>{ai_summary.get('avg_confidence', 'N/A')}%</td></tr>
             <tr><th>Abnormal Cells</th><td>{ai_summary.get('abnormal_cells', 'N/A')}</td></tr>
             <tr><th>Normal Cells</th><td>{ai_summary.get('normal_cells', 'N/A')}</td></tr>
             """
@@ -908,7 +898,6 @@ async def generate_report(
             
             analysis_metrics_html = f"""
             <tr><th>System</th><td>Manalife AI System — Automated Analysis</td></tr>
-            <tr><th>Confidence Score</th><td>{ai_summary.get('avg_confidence', 'N/A')}%</td></tr>
             {confidence_rows}
             """
 
@@ -1036,12 +1025,8 @@ async def generate_report(
           <div class="section-title">Signatures</div>
           <div class="signatures">
             <div class="sig">
-              <div style="font-weight:700">Dr. Emily Roberts</div>
-              <div class="muted">MD, pathologist</div>
-            </div>
-            <div class="sig">
-              <div style="font-weight:700">Dr. James Wilson</div>
-              <div class="muted">MD, pathologist</div>
+              <div style="font-weight:700">Rajesh Venugopal</div>
+              <div class="muted">Physician</div>
             </div>
           </div>
         </div>

frontend/src/components/ResultsPanel.tsx

@@ -139,9 +139,7 @@ export function ResultsPanel({ uploadedImage, result, loading }: ResultsPanelPro
               {typeof summary.normal_cells !== 'undefined' && (
                 <><strong>Normal Cells:</strong> {summary.normal_cells} <br /></>
               )}
-              {typeof summary.avg_confidence !== 'undefined' && (
-                <><strong>Average Confidence:</strong> {summary.avg_confidence?.toFixed(2)}% <br /></>
-              )}
+              {/* average confidence removed */}
             </p>
             <div className="mt-3 text-gray-800 text-sm italic border-t pt-2">
               {summary.ai_interpretation || "No AI interpretation available."}
@@ -155,9 +153,6 @@ export function ResultsPanel({ uploadedImage, result, loading }: ResultsPanelPro
         return (
           <div className="bg-gray-50 p-4 rounded-lg mb-6">
             <h3 className="text-lg font-semibold text-gray-800 mb-2">AI Summary</h3>
-            <p className="text-gray-700 text-sm leading-relaxed">
-              <strong>Average Confidence:</strong> {typeof summary.avg_confidence !== 'undefined' ? `${summary.avg_confidence?.toFixed(2)}%` : "-"}
-            </p>
             <div className="mt-3 text-gray-800 text-sm italic border-t pt-2">
               {summary.ai_interpretation || "No AI interpretation available."}
             </div>
@@ -169,16 +164,13 @@ export function ResultsPanel({ uploadedImage, result, loading }: ResultsPanelPro
       return (
         <div className="bg-gray-50 p-4 rounded-lg mb-6">
           <h3 className="text-lg font-semibold text-gray-800 mb-2">AI Summary</h3>
-          <p className="text-gray-700 text-sm leading-relaxed">
+            <p className="text-gray-700 text-sm leading-relaxed">
             {typeof summary.abnormal_cells !== 'undefined' && (
               <><strong>Abnormal Cells:</strong> {summary.abnormal_cells} <br /></>
             )}
             {typeof summary.normal_cells !== 'undefined' && (
               <><strong>Normal Cells:</strong> {summary.normal_cells} <br /></>
             )}
-            {typeof summary.avg_confidence !== 'undefined' && (
-              <><strong>Average Confidence:</strong> {summary.avg_confidence?.toFixed(2)}% <br /></>
-            )}
           </p>
           <div className="mt-3 text-gray-800 text-sm italic border-t pt-2">
             {summary.ai_interpretation || "No AI interpretation available."}
@@ -212,20 +204,7 @@ export function ResultsPanel({ uploadedImage, result, loading }: ResultsPanelPro
         {model_used && /mwt|cin|histopathology/i.test(model_used) ? (
           <div>
             {/* Average confidence bar */}
-            <div className="mb-3">
-              <div className="flex items-center justify-between mb-1">
-                <span className="text-sm font-medium text-gray-700">Average confidence</span>
-                <span className="text-sm font-mono text-gray-600">
-                  {summary?.avg_confidence ? `${summary.avg_confidence.toFixed(2)}%` : "-"}
-                </span>
-              </div>
-              <div className="w-full bg-gray-200 rounded-full h-4 overflow-hidden">
-                <div
-                  className="h-4 bg-gradient-to-r from-amber-600 to-amber-400"
-                  style={{ width: `${summary?.avg_confidence ?? 0}%` }}
-                />
-              </div>
-            </div>
+            {/* Average confidence removed from visualization */}
 
             {/* Per-class bars */}
             <div className="space-y-2">