Add integrated DFU dashboard + Fitzpatrick adjustment (English UI)

app.py

@@ -138,9 +138,9 @@ def apply_foot_guide(frame):
     result = blended[:, :, :3].astype(np.uint8)
 
     # Add instruction text at top
-    cv2.putText(result, "Posicione el pie dentro de la guia",
+    cv2.putText(result, "Position the foot inside the guide",
                 (w // 2 - 200, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 220, 120), 2, cv2.LINE_AA)
-    cv2.putText(result, "Distancia: 30-40 cm | Iluminacion uniforme",
+    cv2.putText(result, "Distance: 30-40 cm | Uniform lighting",
                 (w // 2 - 230, h - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (200, 200, 200), 1, cv2.LINE_AA)
 
     return result
@@ -157,7 +157,7 @@ def generate_static_guide():
     font_small = _get_font_regular(14)
 
     # Title
-    draw.text((W // 2 - 130, 15), "Guia de Captura DFU", fill=(31, 41, 55), font=font_title)
+    draw.text((W // 2 - 130, 15), "DFU Capture Guide", fill=(31, 41, 55), font=font_title)
 
     # Draw foot silhouette (simplified)
     foot_guide_rgba = generate_foot_guide(400, 350)
@@ -176,13 +176,13 @@ def generate_static_guide():
     # Instructions
     y = 425
     instructions = [
-        ("1.", "Planta del pie hacia la camara (vista plantar)"),
-        ("2.", "Distancia: 30-40 cm del lente"),
-        ("3.", "Iluminacion uniforme, sin sombras directas"),
-        ("4.", "Fondo neutro (sabana blanca o azul)"),
-        ("5.", "Incluir toda la ulcera + 3-5 cm de piel sana"),
-        ("6.", "Sin flash directo (causa reflejos)"),
-        ("7.", "Pie centrado dentro de la silueta verde"),
+        ("1.", "Plantar view of the foot facing the camera"),
+        ("2.", "Distance: 30-40 cm from the lens"),
+        ("3.", "Uniform lighting, no harsh shadows"),
+        ("4.", "Neutral background (white or blue sheet)"),
+        ("5.", "Include the full ulcer + 3-5 cm of healthy skin"),
+        ("6.", "Avoid direct flash (causes glare)"),
+        ("7.", "Center the foot inside the green silhouette"),
     ]
     for num, text in instructions:
         draw.text((30, y), num, fill=(5, 150, 105), font=font_title)
@@ -192,10 +192,10 @@ def generate_static_guide():
     # Bottom note
     draw.line([(30, y + 5), (W - 30, y + 5)], fill=(229, 231, 235), width=1)
     draw.text((30, y + 12),
-              "Tip: Para mejor resultado, capture con luz natural",
+              "Tip: For best results capture with diffuse natural",
               fill=(107, 114, 128), font=font_small)
     draw.text((30, y + 32),
-              "difusa. Evite luces cenitales que generan sombra.",
+              "light. Avoid overhead lights that create shadows.",
               fill=(107, 114, 128), font=font_small)
 
     return np.array(img)
@@ -263,7 +263,7 @@ class DFUReport(FPDF):
         self._font("B", 14)
         self.set_text_color(255, 255, 255)
         self.set_xy(10, 4)
-        self.cell(0, 8, "WoundNetB7 - Informe de Analisis DFU", 0, 0, "L")
+        self.cell(0, 8, "WoundNetB7 - Integrated DFU Assessment Report", 0, 0, "L")
         self._font("", 8)
         self.set_text_color(156, 163, 175)
         self.set_xy(10, 13)
@@ -277,9 +277,9 @@ class DFUReport(FPDF):
         self.set_y(-12)
         self._font("", 7)
         self.set_text_color(156, 163, 175)
-        self.cell(0, 5, "WoundNetB7 | Tesis Doctoral | Marcelo Marquez-Murillo | "
-                  "Dice 0.927 (CI 95%: [0.917, 0.936]) | Debiasing: 46.6% gap reduction (p < 1e-55)", 0, 0, "C")
-        self.cell(0, 5, f"Pag {self.page_no()}/{{nb}}", 0, 0, "R")
+        self.cell(0, 5, "WoundNetB7 | Doctoral Thesis | Marcelo Marquez-Murillo | "
+                  "Dice 0.927 (95% CI: [0.917, 0.936]) | Debiasing: 46.6% gap reduction (p < 1e-55)", 0, 0, "C")
+        self.cell(0, 5, f"Page {self.page_no()}/{{nb}}", 0, 0, "R")
 
     def section_title(self, number, title):
         self._font("B", 11)
@@ -325,8 +325,8 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
     pdf.add_page()
 
     # ── Section 1: Images ──
-    pdf.section_title(1, "Segmentacion")
-    pdf.add_image_pair(orig_path, "Imagen Original", binary_path, "Segmentacion Binaria (Ulcera)")
+    pdf.section_title(1, "Segmentation")
+    pdf.add_image_pair(orig_path, "Original Image", binary_path, "Binary Ulcer Segmentation")
     pdf.ln(2)
 
     # Multiclass + legend
@@ -334,9 +334,9 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
     pdf.set_text_color(107, 114, 128)
     x_start = pdf.get_x()
     y_start = pdf.get_y()
-    pdf.cell(90, 4, "Segmentacion Multiclase", 0, 0, "C")
+    pdf.cell(90, 4, "Multi-Class Segmentation", 0, 0, "C")
     pdf.cell(5, 4, "", 0, 0)
-    pdf.cell(90, 4, "Distribucion de Clases", 0, 1, "C")
+    pdf.cell(90, 4, "Class Area Distribution", 0, 1, "C")
 
     pdf.image(multi_path, x=x_start, y=pdf.get_y(), w=90, h=60)
 
@@ -345,10 +345,10 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
     legend_y = pdf.get_y() + 5
 
     class_info = [
-        ("Pie", result.class_distribution.get("foot", 0), (34, 197, 94)),
-        ("Perilesion", result.class_distribution.get("perilesion", 0), (249, 115, 22)),
-        ("Ulcera", result.class_distribution.get("ulcer", 0), (239, 68, 68)),
-        ("Fondo", result.class_distribution.get("background", 0), (107, 114, 128)),
+        ("Foot", result.class_distribution.get("foot", 0), (34, 197, 94)),
+        ("Perilesional", result.class_distribution.get("perilesion", 0), (249, 115, 22)),
+        ("Ulcer", result.class_distribution.get("ulcer", 0), (239, 68, 68)),
+        ("Background", result.class_distribution.get("background", 0), (107, 114, 128)),
     ]
 
     for cls_name, pct, (r, g, b) in class_info:
@@ -377,12 +377,12 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
     h_img, w_img = result.image_size
     pdf._font("", 8)
     pdf.set_text_color(107, 114, 128)
-    pdf.cell(0, 4, f"Resolucion: {w_img}x{h_img} px  |  Device: {result.device}  |  "
-             f"Area ulcera: {result.class_distribution.get('ulcer', 0):.1f}%", 0, 1)
+    pdf.cell(0, 4, f"Resolution: {w_img}x{h_img} px  |  Device: {result.device}  |  "
+             f"Ulcer area: {result.class_distribution.get('ulcer', 0):.1f}%", 0, 1)
     pdf.ln(4)
 
     # ── Section 2: Fitzpatrick ──
-    pdf.section_title(2, "Estimacion Fitzpatrick / ITA")
+    pdf.section_title(2, "Fitzpatrick / ITA Skin Type Estimation")
     fitz = result.fitzpatrick
     if fitz and fitz.confidence > 0:
         ftype = fitz.fitzpatrick_type
@@ -400,7 +400,7 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
             y_warn = pdf.get_y()
             pdf.rect(pdf.get_x(), y_warn, 185, 10, "DF")
             pdf.set_xy(pdf.get_x() + 2, y_warn + 1)
-            pdf.cell(0, 4, "ADVERTENCIA: Iluminacion insuficiente - tipo Fitzpatrick puede estar sobreestimado", 0, 1)
+            pdf.cell(0, 4, "WARNING: Insufficient lighting — Fitzpatrick type may be overestimated", 0, 1)
             pdf._font("", 7)
             pdf.set_text_color(153, 27, 27)
             pdf.cell(0, 3, lighting_warning, 0, 1)
@@ -413,7 +413,7 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
             y_warn = pdf.get_y()
             pdf.rect(pdf.get_x(), y_warn, 185, 10, "DF")
             pdf.set_xy(pdf.get_x() + 2, y_warn + 1)
-            pdf.cell(0, 4, "PRECAUCION: Iluminacion suboptima - resultado puede tener 1-2 niveles de error", 0, 1)
+            pdf.cell(0, 4, "CAUTION: Suboptimal lighting — result may be off by 1-2 levels", 0, 1)
             pdf._font("", 7)
             pdf.set_text_color(146, 64, 14)
             pdf.cell(0, 3, lighting_warning, 0, 1)
@@ -428,7 +428,7 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
         pdf._font("B", 16)
         pdf.set_text_color(*fg)
         pdf.set_xy(x_badge, y_badge + 2)
-        pdf.cell(35, 9, f"Tipo {ftype}", 0, 0, "C")
+        pdf.cell(35, 9, f"Type {ftype}", 0, 0, "C")
         pdf._font("", 8)
         pdf.set_xy(x_badge, y_badge + 12)
         pdf.cell(35, 6, fitz.fitzpatrick_label, 0, 0, "C")
@@ -440,12 +440,12 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
         det_y = y_badge
         l_scene = getattr(fitz, "l_scene_mean", 0)
         details = [
-            ("ITA", f"{fitz.ita_angle:.1f} +/- {fitz.ita_std:.1f} grados"),
-            ("L* medio (piel sana)", f"{fitz.l_skin_mean:.1f}"),
-            ("L* escena (global)", f"{l_scene:.1f}"),
-            ("b* medio (piel sana)", f"{fitz.b_skin_mean:.1f}"),
-            ("Pixeles sanos", f"{fitz.healthy_pixels:,}"),
-            ("Confianza", f"{fitz.confidence:.0%}"),
+            ("ITA", f"{fitz.ita_angle:.1f} +/- {fitz.ita_std:.1f} deg"),
+            ("L* mean (healthy skin)", f"{fitz.l_skin_mean:.1f}"),
+            ("L* scene (global)", f"{l_scene:.1f}"),
+            ("b* mean (healthy skin)", f"{fitz.b_skin_mean:.1f}"),
+            ("Healthy pixels", f"{fitz.healthy_pixels:,}"),
+            ("Confidence", f"{fitz.confidence:.0%}"),
         ]
         for label, value in details:
             pdf.set_xy(det_x, det_y)
@@ -459,11 +459,11 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
     else:
         pdf._font("", 9)
         pdf.set_text_color(107, 114, 128)
-        pdf.cell(0, 5, "No estimable (insuficientes pixeles de piel sana).", 0, 1)
+        pdf.cell(0, 5, "Not estimable (insufficient healthy-skin pixels).", 0, 1)
     pdf.ln(4)
 
     # ── Section 3: PWAT ──
-    pdf.section_title(3, "PWAT - Scores Raw vs Ajustados por Fitzpatrick")
+    pdf.section_title(3, "PWAT — Raw vs Fitzpatrick-Adjusted Scores")
     pwat = result.pwat
     if pwat and pwat.scores_raw:
         ftype_str = pwat.fitzpatrick_type or "III"
@@ -473,7 +473,7 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
         pdf._font("B", 9)
         pdf.set_text_color(55, 65, 81)
         col_widths = [55, 25, 25, 25, 20, 35]
-        headers = ["Item PWAT", "Raw", "Ajust.", "Delta", "Escala", ""]
+        headers = ["PWAT Item", "Raw", "Adj.", "Delta", "Scale", ""]
         for w, h in zip(col_widths, headers):
             pdf.cell(w, 6, h, 1, 0, "C", fill=True)
         pdf.ln()
@@ -512,7 +512,7 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
 
             # Severity label
             pdf._font("", 7)
-            sev_labels = {0: "Normal", 1: "Leve", 2: "Moderado", 3: "Severo", 4: "Extremo"}
+            sev_labels = {0: "Normal", 1: "Mild", 2: "Moderate", 3: "Severe", 4: "Extreme"}
             pdf.set_text_color(107, 114, 128)
             pdf.cell(col_widths[5], 6, sev_labels.get(raw, ""), "RB", 0, "L")
             pdf.ln()
@@ -533,21 +533,21 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
         # Score interpretation
         pdf._font("", 8)
         pdf.set_text_color(107, 114, 128)
-        pdf.cell(0, 4, "Escala: 0 (normal) - 4 (extremo) por item. Total: 0-24.", 0, 1)
-        pdf.cell(0, 4, f"Correccion de sesgo aplicada segun Fitzpatrick tipo {ftype_str} "
-                 "(calibrada en 61 imagenes, r=0.975).", 0, 1)
+        pdf.cell(0, 4, "Scale: 0 (normal) — 4 (extreme) per item. Total: 0-24.", 0, 1)
+        pdf.cell(0, 4, f"Bias correction applied for Fitzpatrick type {ftype_str} "
+                 "(calibrated on 61 images, r=0.975).", 0, 1)
 
         # Interpretation ranges
         pdf.ln(2)
         pdf._font("B", 8)
         pdf.set_text_color(55, 65, 81)
-        pdf.cell(0, 4, "Interpretacion del puntaje total:", 0, 1)
+        pdf.cell(0, 4, "Interpretation of total score:", 0, 1)
         pdf._font("", 8)
         ranges = [
-            ("0-6:", "Herida en buen estado de cicatrizacion", (34, 197, 94)),
-            ("7-12:", "Herida con compromiso moderado, requiere seguimiento", (249, 115, 22)),
-            ("13-18:", "Herida con compromiso severo, ajustar tratamiento", (239, 68, 68)),
-            ("19-24:", "Herida critica, evaluacion urgente", (180, 30, 30)),
+            ("0-6:",   "Wound healing well",                                     (34, 197, 94)),
+            ("7-12:",  "Moderate compromise — clinical follow-up required",      (249, 115, 22)),
+            ("13-18:", "Severe compromise — adjust treatment",                   (239, 68, 68)),
+            ("19-24:", "Critical wound — urgent reassessment",                   (180, 30, 30)),
         ]
         for label, desc, (r, g, b) in ranges:
             pdf.set_fill_color(r, g, b)
@@ -563,10 +563,10 @@ def generate_pdf_report(image_rgb, binary_overlay, multiclass_overlay, result):
     else:
         pdf._font("", 9)
         pdf.set_text_color(107, 114, 128)
-        pdf.cell(0, 5, "No estimable (ulcera no detectada o area insuficiente).", 0, 1)
+        pdf.cell(0, 5, "Not estimable (ulcer not detected or area too small).", 0, 1)
 
     # Save PDF
-    pdf_path = os.path.join(tmpdir, "WoundNetB7_Informe_DFU.pdf")
+    pdf_path = os.path.join(tmpdir, "WoundNetB7_DFU_Report.pdf")
     pdf.output(pdf_path)
 
     # Cleanup temp images
@@ -596,7 +596,7 @@ def generate_report_files(image_rgb, binary_overlay, multiclass_overlay, result)
         "tta_folds": 6,
         "debiasing": "Fitzpatrick-calibrated ITA (86.9% accuracy, r=0.975)",
     }
-    json_path = os.path.join(tmpdir, "WoundNetB7_Informe_DFU.json")
+    json_path = os.path.join(tmpdir, "WoundNetB7_DFU_Report.json")
     with open(json_path, "w", encoding="utf-8") as f:
         json.dump(report_data, f, indent=2, ensure_ascii=False)
 
@@ -613,17 +613,19 @@ def analyze_image(image):
     if image is None:
         empty = np.zeros((100, 100, 3), dtype=np.uint8)
         _last_analysis.clear()
-        return empty, empty, "", "", "", "{}"
+        return empty, empty, empty, "", "", "", "{}"
 
     img_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
     result = pipe.analyze(img_bgr, use_tta=True)
 
     binary_overlay = pipe.visualize_binary(img_bgr, result)
     multiclass_overlay = pipe.visualize_multiclass(img_bgr, result)
+    dashboard = pipe.render_integrated_report(img_bgr, result)
 
     _last_analysis["image_rgb"] = image
     _last_analysis["binary"] = binary_overlay
     _last_analysis["multiclass"] = multiclass_overlay
+    _last_analysis["dashboard"] = dashboard
     _last_analysis["result"] = result
 
     seg_stats = build_seg_stats_html(result)
@@ -631,7 +633,7 @@ def analyze_image(image):
     pwat_html = build_pwat_html(result.pwat)
     json_out = json.dumps(result.to_dict(), indent=2, ensure_ascii=False)
 
-    return binary_overlay, multiclass_overlay, seg_stats, fitz_html, pwat_html, json_out
+    return dashboard, binary_overlay, multiclass_overlay, seg_stats, fitz_html, pwat_html, json_out
 
 
 def analyze_from_camera(image):
@@ -654,7 +656,7 @@ def download_report():
 
 def build_fitz_html(fitz):
     if fitz is None or fitz.confidence == 0:
-        return "<p style='color:#6b7280;'>No se pudo estimar (insuficientes pixeles de piel sana).</p>"
+        return "<p style='color:#6b7280;'>Not estimable (insufficient healthy-skin pixels).</p>"
     bg = FITZ_COLORS.get(fitz.fitzpatrick_type, "#e5e7eb")
     fg = FITZ_TEXT_COLORS.get(fitz.fitzpatrick_type, "#1f2937")
 
@@ -668,17 +670,17 @@ def build_fitz_html(fitz):
         warning_html = f"""
         <div style="background:#fef2f2; border:1px solid #fca5a5; border-radius:8px;
                     padding:12px 16px; margin-bottom:12px; font-size:0.9em;">
-          <span style="color:#dc2626; font-weight:700;">&#9888; Iluminacion insuficiente</span><br>
+          <span style="color:#dc2626; font-weight:700;">&#9888; Insufficient lighting</span><br>
           <span style="color:#991b1b;">{lighting_warning}</span><br>
-          <span style="color:#6b7280; font-size:0.85em;">L* escena: {l_scene:.0f} (minimo recomendado: 35)</span>
+          <span style="color:#6b7280; font-size:0.85em;">L* scene: {l_scene:.0f} (recommended minimum: 35)</span>
         </div>"""
     elif lighting_quality == "low":
         warning_html = f"""
         <div style="background:#fffbeb; border:1px solid #fcd34d; border-radius:8px;
                     padding:12px 16px; margin-bottom:12px; font-size:0.9em;">
-          <span style="color:#d97706; font-weight:700;">&#9888; Iluminacion suboptima</span><br>
+          <span style="color:#d97706; font-weight:700;">&#9888; Suboptimal lighting</span><br>
           <span style="color:#92400e;">{lighting_warning}</span><br>
-          <span style="color:#6b7280; font-size:0.85em;">L* escena: {l_scene:.0f} (recomendado: >50)</span>
+          <span style="color:#6b7280; font-size:0.85em;">L* scene: {l_scene:.0f} (recommended: >50)</span>
         </div>"""
 
     return f"""
@@ -687,22 +689,22 @@ def build_fitz_html(fitz):
       <div style="background:{bg}; color:{fg}; border-radius:12px; padding:18px 28px;
                   font-size:1.5em; font-weight:700; min-width:120px; text-align:center;
                   border:2px solid rgba(0,0,0,0.1);">
-        Tipo {fitz.fitzpatrick_type}<br>
+        Type {fitz.fitzpatrick_type}<br>
         <span style="font-size:0.55em; font-weight:400;">{fitz.fitzpatrick_label}</span>
       </div>
       <div style="font-size:0.95em; line-height:1.8;">
         <b>ITA:</b> {fitz.ita_angle:.1f}&deg; &plusmn; {fitz.ita_std:.1f}&deg;<br>
-        <b>L* medio piel:</b> {fitz.l_skin_mean:.1f}<br>
-        <b>L* escena:</b> {l_scene:.1f}<br>
-        <b>Pixeles sanos:</b> {fitz.healthy_pixels:,}<br>
-        <b>Confianza:</b> {fitz.confidence:.0%}
+        <b>L* healthy skin:</b> {fitz.l_skin_mean:.1f}<br>
+        <b>L* scene:</b> {l_scene:.1f}<br>
+        <b>Healthy pixels:</b> {fitz.healthy_pixels:,}<br>
+        <b>Confidence:</b> {fitz.confidence:.0%}
       </div>
     </div>"""
 
 
 def build_pwat_html(pwat):
     if pwat is None or not pwat.scores_raw:
-        return "<p style='color:#6b7280;'>No se pudo estimar PWAT (ulcera no detectada o muy pequena).</p>"
+        return "<p style='color:#6b7280;'>PWAT not estimable (ulcer not detected or area too small).</p>"
     rows = ""
     for item in [3, 4, 5, 6, 7, 8]:
         name = ITEM_NAMES.get(item, f"Item {item}")
@@ -741,9 +743,9 @@ def build_pwat_html(pwat):
     <table style="width:100%; border-collapse:collapse; font-size:0.92em;">
       <thead>
         <tr style="border-bottom:2px solid #d1d5db;">
-          <th style="padding:10px 12px; text-align:left;">Item PWAT</th>
-          <th style="padding:10px 12px; text-align:center;">Score Raw</th>
-          <th style="padding:10px 12px; text-align:center;">Score Ajustado</th>
+          <th style="padding:10px 12px; text-align:left;">PWAT Item</th>
+          <th style="padding:10px 12px; text-align:center;">Raw Score</th>
+          <th style="padding:10px 12px; text-align:center;">Adjusted Score</th>
           <th style="padding:10px 12px; text-align:center;">&Delta;</th>
         </tr>
       </thead>
@@ -757,10 +759,10 @@ def build_pwat_html(pwat):
       </tbody>
     </table>
     <p style="font-size:0.82em; color:#6b7280; margin-top:8px;">
-      Escala: 0 (mejor) - 4 (peor) por item |
-      Correccion Fitzpatrick tipo {pwat.fitzpatrick_type} aplicada |
-      Items: 3=Tipo necrotico, 4=Cantidad necrotica, 5=Tipo granulacion,
-      6=Cantidad granulacion, 7=Bordes, 8=Piel periulceral
+      Scale: 0 (best) — 4 (worst) per item |
+      Fitzpatrick type {pwat.fitzpatrick_type} correction applied |
+      Items: 3=Necrotic Type, 4=Necrotic Amount, 5=Granulation Type,
+      6=Granulation Amount, 7=Edges, 8=Periulcer Skin
     </p>"""
 
 
@@ -771,7 +773,7 @@ def build_seg_stats_html(result):
     for cls_name in ["foot", "perilesion", "ulcer"]:
         pct = dist.get(cls_name, 0)
         color = colors.get(cls_name, "#6b7280")
-        label = {"foot": "Pie", "perilesion": "Perilesion", "ulcer": "Ulcera"}.get(cls_name, cls_name)
+        label = {"foot": "Foot", "perilesion": "Perilesional", "ulcer": "Ulcer"}.get(cls_name, cls_name)
         bars += f"""
         <div style="margin-bottom:6px;">
           <div style="display:flex; justify-content:space-between; font-size:0.9em; margin-bottom:2px;">
@@ -785,7 +787,7 @@ def build_seg_stats_html(result):
     return f"""
     <div style="padding:4px 0;">
       <p style="font-size:0.85em; color:#6b7280; margin-bottom:10px;">
-        Imagen: {result.image_size[1]}x{result.image_size[0]} | Device: {result.device}
+        Image: {result.image_size[1]}x{result.image_size[0]} | Device: {result.device}
       </p>
       {bars}
     </div>"""
@@ -822,99 +824,110 @@ with gr.Blocks(
 
     with gr.Tabs():
         # ══════════════════════════════════════════════════════════════════════
-        # TAB 1: Analisis (upload o galeria)
+        # TAB 1: DFU Analysis (upload or gallery)
         # ══════════════════════════════════════════════════════════════════════
-        with gr.Tab("Analisis DFU"):
+        with gr.Tab("DFU Analysis"):
             with gr.Row():
                 with gr.Column(scale=1):
-                    input_image = gr.Image(label="Imagen DFU", type="numpy",
+                    input_image = gr.Image(label="DFU Image", type="numpy",
                                            sources=["upload", "clipboard"])
-                    analyze_btn = gr.Button("Analizar", variant="primary", size="lg")
+                    analyze_btn = gr.Button("Analyze", variant="primary", size="lg")
                     gr.HTML("""
                     <div style="font-size:0.82em; color:#6b7280; margin-top:8px; line-height:1.6;">
-                      <b>Pipeline:</b> La imagen pasa por 4 etapas secuenciales.<br>
-                      <b>Modelo:</b> WoundNetB7 con Combo Loss + Small Object Loss.
-                      Atencion: CBAM, CoordAttention, TAM (fractal + Euler).<br>
-                      <b>TTA:</b> 6 augmentaciones en inferencia.
+                      <b>Pipeline:</b> the image goes through 4 sequential stages.<br>
+                      <b>Model:</b> WoundNetB7 with Combo Loss + Small Object Loss.
+                      Attention modules: CBAM, CoordAttention, TAM (fractal + Euler).<br>
+                      <b>TTA:</b> 6-fold test-time augmentation.
                     </div>
                     """)
 
+            # Integrated clinical dashboard (primary nurse-facing output)
+            gr.HTML("""<div class="step-header" style="margin-top:8px;">
+              <div class="step-number" style="background:#0e7490;">&#9733;</div>
+              <div class="step-title">Integrated Clinical Assessment Report</div></div>
+            <p style="font-size:0.88em; color:#6b7280; margin-bottom:8px;">
+              Single-page summary combining segmentation, Fitzpatrick / ITA estimation
+              and Fitzpatrick-adjusted PWAT scoring. Designed for clinical staff.
+            </p>""")
+            output_dashboard = gr.Image(label="Integrated DFU Assessment Report",
+                                        show_download_button=True, height=720)
+
             # Step 1
             gr.HTML("""<div class="step-header"><div class="step-number">1</div>
-              <div class="step-title">Segmentacion Binaria de la Ulcera</div></div>""")
+              <div class="step-title">Binary Ulcer Segmentation</div></div>""")
             with gr.Row():
                 with gr.Column(scale=1):
-                    output_binary = gr.Image(label="Mascara Binaria Ulcera (WoundNetB7)")
+                    output_binary = gr.Image(label="Binary Ulcer Mask (WoundNetB7)")
                 with gr.Column(scale=1):
-                    output_seg_stats = gr.HTML(label="Estadisticas de Segmentacion")
+                    output_seg_stats = gr.HTML(label="Segmentation Statistics")
 
             # Step 2
             gr.HTML("""<div class="step-header" style="margin-top:12px;"><div class="step-number">2</div>
-              <div class="step-title">Segmentacion Multiclase (4 clases)</div></div>""")
+              <div class="step-title">Multi-Class Segmentation (4 classes)</div></div>""")
             with gr.Row():
                 with gr.Column(scale=1):
-                    output_multiclass = gr.Image(label="Overlay Multiclase")
+                    output_multiclass = gr.Image(label="Multi-Class Overlay")
                 with gr.Column(scale=1):
                     gr.HTML("""
                     <div style="padding:12px;">
-                      <p style="font-weight:600; margin-bottom:10px;">Leyenda de clases:</p>
+                      <p style="font-weight:600; margin-bottom:10px;">Class legend:</p>
                       <div style="display:flex; flex-direction:column; gap:8px;">
                         <div style="display:flex; align-items:center; gap:8px;">
                           <div style="width:20px; height:20px; background:#22c55e; border-radius:4px;"></div>
-                          <span><b>Pie</b> — tejido sano</span>
+                          <span><b>Foot</b> — healthy tissue</span>
                         </div>
                         <div style="display:flex; align-items:center; gap:8px;">
                           <div style="width:20px; height:20px; background:#f97316; border-radius:4px;"></div>
-                          <span><b>Perilesion</b> — zona periulceral</span>
+                          <span><b>Perilesional</b> — periulcer area</span>
                         </div>
                         <div style="display:flex; align-items:center; gap:8px;">
                           <div style="width:20px; height:20px; background:#ef4444; border-radius:4px;"></div>
-                          <span><b>Ulcera</b> — lecho de la herida</span>
+                          <span><b>Ulcer</b> — wound bed</span>
                         </div>
                       </div>
                     </div>""")
 
             # Step 3
             gr.HTML("""<div class="step-header" style="margin-top:12px;"><div class="step-number">3</div>
-              <div class="step-title">Estimacion Fitzpatrick / ITA</div></div>""")
+              <div class="step-title">Fitzpatrick / ITA Skin Type Estimation</div></div>""")
             output_fitz = gr.HTML()
 
             # Step 4
             gr.HTML("""<div class="step-header" style="margin-top:12px;"><div class="step-number">4</div>
-              <div class="step-title">PWAT — Scores Raw vs Ajustados por Fitzpatrick</div></div>""")
+              <div class="step-title">PWAT — Raw vs Fitzpatrick-Adjusted Scores</div></div>""")
             output_pwat = gr.HTML()
 
             # Download
             gr.HTML("""<div class="step-header" style="margin-top:16px;">
               <div class="step-number" style="background:#059669;">&#8681;</div>
-              <div class="step-title">Descargar Informe Clinico</div></div>
+              <div class="step-title">Download Clinical Report</div></div>
             <p style="font-size:0.88em; color:#6b7280; margin-bottom:8px;">
-              Genera un informe PDF con todas las visualizaciones y datos estructurados.
-              Primero analiza una imagen.</p>""")
-            download_btn = gr.Button("Descargar Informe PDF", variant="secondary", size="lg")
-            output_files = gr.File(label="Archivos del Informe (PDF + JSON)", file_count="multiple")
+              Generates a PDF report with all visualizations and structured data.
+              Run an analysis first.</p>""")
+            download_btn = gr.Button("Download PDF Report", variant="secondary", size="lg")
+            output_files = gr.File(label="Report Files (PDF + JSON)", file_count="multiple")
 
-            with gr.Accordion("JSON completo (para integracion)", open=False):
+            with gr.Accordion("Full JSON (for integration)", open=False):
                 output_json = gr.Code(label="JSON Output", language="json")
 
             analyze_btn.click(
                 fn=analyze_image,
                 inputs=[input_image],
-                outputs=[output_binary, output_multiclass, output_seg_stats,
+                outputs=[output_dashboard, output_binary, output_multiclass, output_seg_stats,
                          output_fitz, output_pwat, output_json],
             )
             download_btn.click(fn=download_report, inputs=[], outputs=[output_files])
 
         # ══════════════════════════════════════════════════════════════════════
-        # TAB 2: Captura Guiada (webcam con guia de pie)
+        # TAB 2: Guided Capture (webcam with foot guide overlay)
         # ══════════════════════════════════════════════════════════════════════
-        with gr.Tab("Captura Guiada"):
+        with gr.Tab("Guided Capture"):
             gr.HTML("""
             <div style="padding:16px 0;">
-              <h2 style="font-size:1.4em; margin:0 0 8px;">Captura Guiada para Personal Sanitario</h2>
+              <h2 style="font-size:1.4em; margin:0 0 8px;">Guided Capture for Clinical Staff</h2>
               <p style="color:#6b7280; line-height:1.6;">
-                Use la camara del dispositivo para capturar una imagen del pie diabetico.
-                La silueta verde guia la posicion correcta del pie para un analisis optimo.
+                Use the device camera to capture an image of the diabetic foot.
+                The green silhouette guides correct foot positioning for optimal analysis.
               </p>
             </div>
             """)
@@ -922,15 +935,15 @@ with gr.Blocks(
             with gr.Row():
                 with gr.Column(scale=3):
                     camera_input = gr.Image(
-                        label="Camara — Posicione el pie dentro de la guia",
+                        label="Camera — Position the foot inside the guide",
                         type="numpy",
                         sources=["webcam"],
                     )
-                    camera_analyze_btn = gr.Button("Capturar y Analizar", variant="primary", size="lg")
+                    camera_analyze_btn = gr.Button("Capture and Analyze", variant="primary", size="lg")
 
                 with gr.Column(scale=2):
                     guide_image = gr.Image(
-                        label="Guia de Posicionamiento",
+                        label="Positioning Guide",
                         value=generate_static_guide(),
                         interactive=False,
                     )
@@ -939,28 +952,35 @@ with gr.Blocks(
             <div style="background:#f0fdf4; border:1px solid #bbf7d0; border-radius:10px;
                         padding:16px; margin:12px 0;">
               <p style="font-weight:700; color:#166534; margin:0 0 8px;">
-                Instrucciones para el personal sanitario:
+                Instructions for clinical staff:
               </p>
               <div style="display:grid; grid-template-columns:1fr 1fr; gap:8px 24px; font-size:0.92em; color:#15803d;">
-                <div>1. Planta del pie mirando a la camara</div>
-                <div>2. Distancia: 30-40 cm del lente</div>
-                <div>3. Iluminacion uniforme, sin sombras</div>
-                <div>4. Fondo neutro (sabana blanca/azul)</div>
-                <div>5. Incluir toda la ulcera + piel sana periferica</div>
-                <div>6. Evitar flash directo (causa reflejos)</div>
-                <div>7. Mantener el dispositivo estable</div>
-                <div>8. Limpiar el lente antes de capturar</div>
+                <div>1. Plantar surface facing the camera</div>
+                <div>2. Distance: 30-40 cm from the lens</div>
+                <div>3. Uniform lighting, no shadows</div>
+                <div>4. Neutral background (white/blue sheet)</div>
+                <div>5. Include the full ulcer + surrounding healthy skin</div>
+                <div>6. Avoid direct flash (causes glare)</div>
+                <div>7. Keep the device steady</div>
+                <div>8. Clean the lens before capturing</div>
               </div>
             </div>
             """)
 
+            # Integrated dashboard for camera capture
+            gr.HTML("""<div class="step-header" style="margin-top:16px;">
+              <div class="step-number" style="background:#0e7490;">&#9733;</div>
+              <div class="step-title">Integrated Clinical Assessment Report</div></div>""")
+            cam_dashboard = gr.Image(label="Integrated DFU Assessment Report",
+                                     show_download_button=True, height=720)
+
             # Results from camera capture
             gr.HTML("""<div class="step-header" style="margin-top:16px;">
               <div class="step-number">1</div>
-              <div class="step-title">Resultado de Segmentacion</div></div>""")
+              <div class="step-title">Segmentation Result</div></div>""")
             with gr.Row():
-                cam_binary = gr.Image(label="Mascara Binaria Ulcera")
-                cam_multiclass = gr.Image(label="Overlay Multiclase")
+                cam_binary = gr.Image(label="Binary Ulcer Mask")
+                cam_multiclass = gr.Image(label="Multi-Class Overlay")
 
             with gr.Row():
                 cam_seg_stats = gr.HTML()
@@ -970,16 +990,16 @@ with gr.Blocks(
             cam_fitz = gr.HTML()
             cam_pwat = gr.HTML()
 
-            cam_download_btn = gr.Button("Descargar Informe PDF", variant="secondary", size="lg")
-            cam_files = gr.File(label="Archivos del Informe", file_count="multiple")
+            cam_download_btn = gr.Button("Download PDF Report", variant="secondary", size="lg")
+            cam_files = gr.File(label="Report Files", file_count="multiple")
 
-            with gr.Accordion("JSON completo", open=False):
+            with gr.Accordion("Full JSON", open=False):
                 cam_json = gr.Code(label="JSON Output", language="json")
 
             camera_analyze_btn.click(
                 fn=analyze_from_camera,
                 inputs=[camera_input],
-                outputs=[cam_binary, cam_multiclass, cam_seg_stats,
+                outputs=[cam_dashboard, cam_binary, cam_multiclass, cam_seg_stats,
                          cam_fitz, cam_pwat, cam_json],
             )
             cam_download_btn.click(fn=download_report, inputs=[], outputs=[cam_files])
@@ -987,8 +1007,8 @@ with gr.Blocks(
     gr.HTML("""
     <div style="text-align:center; padding:16px 0; font-size:0.82em; color:#9ca3af;
                 border-top:1px solid #e5e7eb; margin-top:20px;">
-      WoundNetB7 &bull; Tesis Doctoral &bull; Marcelo Marquez-Murillo &bull;
-      Ulcer Dice 0.927 (CI 95%: [0.917, 0.936]) &bull;
+      WoundNetB7 &bull; Doctoral Thesis &bull; Marcelo Marquez-Murillo &bull;
+      Ulcer Dice 0.927 (95% CI: [0.917, 0.936]) &bull;
       Debiasing: 46.6% max group gap reduction (p &lt; 10<sup>-55</sup>)
     </div>
     """)