Update

backend/app.py

@@ -569,7 +569,7 @@ async def predict(model_name: str = Form(...), file: UploadFile = File(...)):
 
 # =====================================================
 
-def create_designed_pdf(pdf_path, report_data, analysis_summary_json):
+def create_designed_pdf(pdf_path, report_data, analysis_summary_json, annotated_image_path=None):
     doc = SimpleDocTemplate(pdf_path, pagesize=letter,
                             rightMargin=72, leftMargin=72,
                             topMargin=72, bottomMargin=18)
@@ -665,6 +665,19 @@ def create_designed_pdf(pdf_path, report_data, analysis_summary_json):
     story.append(Paragraph(ai_summary.get('ai_interpretation', 'Not available.'), styles['NormalSmall']))
     story.append(Spacer(1, 0.2*inch))
 
+    # If an annotated image path was provided and exists on disk, embed it
+    if annotated_image_path:
+        try:
+            if os.path.isfile(annotated_image_path):
+                story.append(Spacer(1, 0.1*inch))
+                # Use a reasonable width for the embedded image
+                img = ReportLabImage(annotated_image_path, width=5*inch, kind='proportional')
+                story.append(img)
+                story.append(Spacer(1, 0.2*inch))
+        except Exception as e:
+            # Don't fail the whole PDF creation if image embedding fails
+            print(f"⚠️ Could not embed annotated image in PDF: {e}")
+
     # Doctor's Notes
     story.append(Paragraph("Doctor's Notes", styles['Section']))
     story.append(Paragraph(report_data.get('doctor_notes') or 'No additional notes provided.', styles['NormalSmall']))
@@ -677,8 +690,8 @@ def create_designed_pdf(pdf_path, report_data, analysis_summary_json):
 
     # Signatures
     story.append(Paragraph("Signatures", styles['Section']))
-    story.append(Paragraph("Dr. Emily Roberts, MD (Cytopathologist)", styles['NormalSmall']))
-    story.append(Paragraph("Dr. James Wilson, MD (Pathologist)", styles['NormalSmall']))
+    story.append(Paragraph("Rajesh Venugopal, Physician", styles['NormalSmall']))
+    #story.append(Paragraph("", styles['NormalSmall']))
     story.append(Spacer(1, 0.1*inch))
     story.append(Paragraph(f"Generated on: {datetime.datetime.now().strftime('%b %d, %Y, %I:%M %p')}", styles['NormalSmall']))
 
@@ -694,6 +707,7 @@ async def generate_report(
     notes: str = Form(None),
     analysis_id: str = Form(None),
     analysis_summary: str = Form(None),
+    file: UploadFile = File(None),
 ):
     """Generate a structured medical report from analysis results and metadata."""
     try:
@@ -735,23 +749,62 @@ async def generate_report(
         with open(report_json, "w", encoding="utf-8") as f:
             json.dump(report_data, f, indent=2, ensure_ascii=False)
             
-        # Attempt to create a PDF version if reportlab is available
+        # We'll create PDF later (after parsing analysis_summary and resolving
+        # any annotated/input image). Initialize pdf_url here.
         pdf_url = None
+
+        # Parse analysis_summary to get AI results
+        try:
+            ai_summary = json.loads(analysis_summary) if analysis_summary else {}
+        except (json.JSONDecodeError, TypeError):
+            ai_summary = {}
+
+        # Resolve annotated image: prefer AI/analysis annotated image; if none,
+        # save the uploaded input image (if provided) into the report folder
+        # and use that as the embedded image.
+        annotated_img = ai_summary.get('annotated_image_url') or report_data.get("analysis", {}).get("annotated_image_url") or ""
+        annotated_img_full = ""
+        annotated_img_local = None
+
+        if annotated_img:
+            # If it's an outputs path served by StaticFiles, map to local file
+            if isinstance(annotated_img, str) and annotated_img.startswith('/outputs/'):
+                rel = annotated_img[len('/outputs/'):].lstrip('/')
+                annotated_img_local = os.path.join(OUTPUT_DIR, rel)
+                annotated_img_full = annotated_img
+            else:
+                # keep absolute URLs as-is for HTML
+                annotated_img_full = annotated_img if isinstance(annotated_img, str) else ''
+
+        # If no annotated image provided, but an input file was uploaded, save it
+        if not annotated_img_full and file is not None and getattr(file, 'filename', None):
+            try:
+                input_filename = f"input_image{os.path.splitext(file.filename)[1] or '.jpg'}"
+                input_path = os.path.join(report_dir, input_filename)
+                contents = await file.read()
+                with open(input_path, 'wb') as out_f:
+                    out_f.write(contents)
+                annotated_img_full = f"/outputs/reports/{report_id}/{input_filename}"
+                annotated_img_local = input_path
+            except Exception as e:
+                print(f"⚠️ Failed to save uploaded input image for report: {e}")
+
+        # Ensure annotated_img_full has a leading slash if it's a relative path
+        if annotated_img_full and not annotated_img_full.startswith(('http://', 'https://')):
+            annotated_img_full = annotated_img_full if annotated_img_full.startswith('/') else '/' + annotated_img_full
+
+        # Now attempt to create the PDF (passing the local annotated image path
+        # so the PDF writer can embed it). If annotation is remote or not
+        # available, PDF creation will still proceed without the image.
         if REPORTLAB_AVAILABLE:
             try:
                 pdf_path = os.path.join(report_dir, "report.pdf")
-                create_designed_pdf(pdf_path, report_data, analysis_summary)
+                create_designed_pdf(pdf_path, report_data, analysis_summary, annotated_img_local)
                 pdf_url = f"/outputs/reports/{report_id}/report.pdf"
             except Exception as e:
                 print(f"Error creating designed PDF: {e}")
                 pdf_url = None
 
-        # Parse analysis_summary to get AI results
-        try:
-            ai_summary = json.loads(analysis_summary) if analysis_summary else {}
-        except (json.JSONDecodeError, TypeError):
-            ai_summary = {}
-
         # Determine report type based on analysis summary or model used
         model_used = ai_summary.get('model_used', '')
         if 'YOLO' in model_used or 'yolo' in str(analysis_id).lower():
@@ -809,18 +862,11 @@ async def generate_report(
         json_url = f"/outputs/reports/{report_id}/report.json"
         html_url = f"/outputs/reports/{report_id}/report.html"
 
-        # Prefer annotated image URL from the analysis summary (ai_summary) if present,
-        # otherwise fall back to the analysis annotated_image_url (which may be the analysis_id).
-        annotated_img = ai_summary.get('annotated_image_url') or report_data.get("analysis", {}).get("annotated_image_url") or ""
-
-        # Normalize annotated image path: allow absolute http(s) URLs or ensure relative paths start with '/'.
-        annotated_img_full = ""
-        if annotated_img:
-            if isinstance(annotated_img, str) and annotated_img.startswith(('http://', 'https://')):
-                annotated_img_full = annotated_img
-            else:
-                # ensure leading slash for relative paths so StaticFiles routing works
-                annotated_img_full = annotated_img if str(annotated_img).startswith('/') else '/' + str(annotated_img)
+        # annotated_img_full was computed earlier; ensure it's defined and set
+        # annotated_img (used by the HTML template conditional) accordingly.
+        if 'annotated_img_full' not in locals() or not annotated_img_full:
+            annotated_img_full = ''
+        annotated_img = annotated_img_full
 
         download_pdf_btn = f'<a href="{pdf_url}" download style="text-decoration:none"><button class="btn-secondary">Download PDF</button></a>' if pdf_url else ''