Update src/ai_processor.py

src/ai_processor.py

@@ -1,42 +1,87 @@
 # smartheal_ai_processor.py
-# Full, functional module with conditional Spaces GPU support and CPU fallbacks.
+# Verbose, instrumented version — preserves public class/function names
+# Turn on deep logging: export LOGLEVEL=DEBUG SMARTHEAL_DEBUG=1
 
 import os
-import time
 import logging
 from datetime import datetime
-from typing import Optional, Dict, List
+from typing import Optional, Dict, List, Tuple
+
+# ---- Environment defaults (do NOT globally hint CUDA here) ----
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
+LOGLEVEL = os.getenv("LOGLEVEL", "INFO").upper()
+SMARTHEAL_DEBUG = os.getenv("SMARTHEAL_DEBUG", "0") == "1"
 
 import cv2
 import numpy as np
 from PIL import Image
+from PIL.ExifTags import TAGS
+
+# --- Logging config ---
+logging.basicConfig(
+    level=getattr(logging, LOGLEVEL, logging.INFO),
+    format="%(asctime)s - %(levelname)s - %(message)s",
+)
+
+def _log_kv(prefix: str, kv: Dict):
+    logging.debug(prefix + " | " + " | ".join(f"{k}={v}" for k, v in kv.items()))
 
-# =============== LOGGING SETUP ===============
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+# --- Spaces GPU decorator (REQUIRED) ---
+from spaces import GPU as _SPACES_GPU
 
-# =============== CONFIGURATION ===============
+@_SPACES_GPU(enable_queue=True)
+def smartheal_gpu_stub(ping: int = 0) -> str:
+    return "ready"
+
+# ---- Paths / constants ----
 UPLOADS_DIR = "uploads"
 os.makedirs(UPLOADS_DIR, exist_ok=True)
 
 HF_TOKEN = os.getenv("HF_TOKEN", None)
 YOLO_MODEL_PATH = "src/best.pt"
-SEG_MODEL_PATH = "src/segmentation_model.h5"  # optional
+SEG_MODEL_PATH = "src/segmentation_model.h5"   # optional
 GUIDELINE_PDFS = ["src/eHealth in Wound Care.pdf", "src/IWGDF Guideline.pdf", "src/evaluation.pdf"]
-DATASET_ID = "SmartHeal/wound-image-uploads"  # optional (set HF_TOKEN too)
-PIXELS_PER_CM = 38  # heuristic
+DATASET_ID = "SmartHeal/wound-image-uploads"
+DEFAULT_PX_PER_CM = 38.0
+PX_PER_CM_MIN, PX_PER_CM_MAX = 5.0, 1200.0
+
+# Segmentation preprocessing knobs
+SEG_EXPECTS_RGB = os.getenv("SEG_EXPECTS_RGB", "1") == "1"  # most TF models trained on RGB
+SEG_NORM = os.getenv("SEG_NORM", "0to1")                    # "0to1" | "imagenet"
+SEG_THRESH = float(os.getenv("SEG_THRESH", "0.5"))
 
-# =============== GLOBAL CACHES ===============
 models_cache: Dict[str, object] = {}
 knowledge_base_cache: Dict[str, object] = {}
 
-# ---------- Optional imports guarded ----------
+# ---------- Utilities to prevent CUDA in main process ----------
+from contextlib import contextmanager
+
+@contextmanager
+def _no_cuda_env():
+    """
+    Mask GPUs so any library imported/constructed in the main process
+    cannot see CUDA (required for Spaces Stateless GPU).
+    """
+    prev = os.environ.get("CUDA_VISIBLE_DEVICES")
+    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+    try:
+        yield
+    finally:
+        if prev is None:
+            os.environ.pop("CUDA_VISIBLE_DEVICES", None)
+        else:
+            os.environ["CUDA_VISIBLE_DEVICES"] = prev
+
+# ---------- Lazy imports (wrapped where needed) ----------
 def _import_ultralytics():
-    from ultralytics import YOLO
+    # Prevent Ultralytics from probing CUDA on import
+    with _no_cuda_env():
+        from ultralytics import YOLO
     return YOLO
 
 def _import_tf_loader():
     import tensorflow as tf
-    tf.config.set_visible_devices([], "GPU")  # force CPU
+    tf.config.set_visible_devices([], "GPU")
     from tensorflow.keras.models import load_model
     return load_model
 
@@ -60,143 +105,136 @@ def _import_hf_hub():
     from huggingface_hub import HfApi, HfFolder
     return HfApi, HfFolder
 
-# =============== SPACES GPU CONDITIONAL ===============
-def _spaces_gpu_available() -> bool:
-    try:
-        import torch
-        return bool(torch.cuda.is_available())
-    except Exception:
-        return False
+# ---------- SmartHeal prompts (system + user prefix) ----------
+SMARTHEAL_SYSTEM_PROMPT = """\
+You are SmartHeal Clinical Assistant, a wound-care decision-support system.
+You analyze wound photographs and brief patient context to produce careful,
+specific, guideline-informed recommendations WITHOUT diagnosing. You always:
+- Use the measurements calculated by the vision pipeline as ground truth.
+- Prefer concise, actionable steps tailored to exudate level, infection risk, and pain.
+- Flag uncertainties and red flags that need escalation to a clinician.
+- Avoid contraindicated advice; do not infer unseen comorbidities.
+- Keep under 300 words and use the requested headings exactly.
+- Tone: professional, clear, and conservative; no definitive medical claims.
+- Safety: remind the user to seek clinician review for changes or red flags.
+"""
+
+SMARTHEAL_USER_PREFIX = """\
+Patient: {patient_info}
+Visual findings: type={wound_type}, size={length_cm}x{breadth_cm} cm, area={area_cm2} cm^2,
+detection_conf={det_conf:.2f}, calibration={px_per_cm} px/cm.
+Guideline context (snippets you can draw principles from; do not quote at length):
+{guideline_context}
+Write a structured answer with these headings exactly:
+1. Clinical Summary (max 4 bullet points)
+2. Likely Stage/Type (if uncertain, say 'uncertain')
+3. Treatment Plan (specific dressing choices and frequency based on exudate/infection risk)
+4. Red Flags (what to escalate and when)
+5. Follow-up Cadence (days)
+6. Notes (assumptions/uncertainties)
+Keep to 220–300 words. Do NOT provide diagnosis. Avoid contraindicated advice.
+"""
 
-def _spaces_lib_available() -> bool:
+# ---------- VLM (MedGemma replaced with Qwen2-VL) ----------
+@_SPACES_GPU(enable_queue=True)
+def _vlm_infer_gpu(messages, model_id: str, max_new_tokens: int, token: Optional[str]):
+    """
+    Runs entirely inside a Spaces GPU worker. It's the ONLY place we allow CUDA init.
+    """
+    from transformers import pipeline
+    import torch # Ensure torch is imported here
+    pipe = pipeline(
+        task="image-text-to-text",
+        model=model_id,
+        torch_dtype=torch.bfloat16, # Use torch_dtype from the working example
+        device_map="auto",            # CUDA init happens here, safely in GPU worker
+        token=token,
+        trust_remote_code=True,
+        model_kwargs={"low_cpu_mem_usage": True},
+    )
+    out = pipe(text=messages, max_new_tokens=max_new_tokens, do_sample=False, temperature=0.2)
     try:
-        import spaces  # noqa
-        return True
+        txt = out[0]["generated_text"][-1].get("content", "")
     except Exception:
-        return False
-
-HAVE_SPACES_GPU = _spaces_gpu_available() and _spaces_lib_available()
-
-if HAVE_SPACES_GPU:
-    import spaces  # define only if available & GPU present
-
-    @spaces.GPU(enable_queue=True, duration=90)
-    def generate_medgemma_report_with_timeout(
-        patient_info: str,
-        visual_results: Dict,
-        guideline_context: str,
-        image_pil: Image.Image,
-        max_new_tokens: Optional[int] = None,
-    ) -> str:
-        """Runs on Spaces GPU only; callers keep one signature on both paths."""
-        import torch
-        from transformers import pipeline
-        try:
-            torch.cuda.empty_cache()
-
-            prompt = f"""
-You are a medical AI assistant. Analyze this wound image and patient data.
-Patient: {patient_info}
-Wound: {visual_results.get('wound_type', 'Unknown')} - {visual_results.get('length_cm', 0)}×{visual_results.get('breadth_cm', 0)} cm
-Provide a structured report with:
-1. Clinical Summary
-2. Treatment Recommendations
-3. Risk Assessment
-4. Monitoring Plan
-""".strip()
-
-            pipe = pipeline(
-                "image-text-to-text",
-                model="google/medgemma-4b-it",
-                torch_dtype=torch.bfloat16,
-                device_map="auto",
-                token=HF_TOKEN,
-                model_kwargs={"low_cpu_mem_usage": True, "use_cache": True},
-            )
+        txt = out[0].get("generated_text", "")
+    return (txt or "").strip() or "⚠️ Empty response"
+
+def generate_medgemma_report(  # kept name so callers don't change
+    patient_info: str,
+    visual_results: Dict,
+    guideline_context: str,
+    image_pil: Image.Image,
+    max_new_tokens: Optional[int] = None,
+) -> str:
+    """
+    MedGemma replacement using Qwen/Qwen2-VL-2B-Instruct via image-text-to-text.
+    Loads & runs ONLY inside a GPU worker to satisfy Stateless GPU constraints.
+    """
+    if os.getenv("SMARTHEAL_ENABLE_VLM", "1") != "1":
+        return "⚠️ VLM disabled"
+
+    model_id = os.getenv("SMARTHEAL_VLM_MODEL", "Qwen/Qwen2-VL-2B-Instruct")
+    max_new_tokens = max_new_tokens or int(os.getenv("SMARTHEAL_VLM_MAX_TOKENS", "600"))
+
+    uprompt = SMARTHEAL_USER_PREFIX.format(
+        patient_info=patient_info,
+        wound_type=visual_results.get("wound_type", "Unknown"),
+        length_cm=visual_results.get("length_cm", 0),
+        breadth_cm=visual_results.get("breadth_cm", 0),
+        area_cm2=visual_results.get("surface_area_cm2", 0),
+        det_conf=float(visual_results.get("detection_confidence", 0.0)),
+        px_per_cm=visual_results.get("px_per_cm", "?"),
+        guideline_context=(guideline_context or "")[:900],
+    )
 
-            messages = [
-                {
-                    "role": "user",
-                    "content": [
-                        {"type": "image", "image": image_pil},
-                        {"type": "text", "text": prompt},
-                    ],
-                }
-            ]
-
-            t0 = time.time()
-            out = pipe(
-                text=messages,
-                max_new_tokens=max_new_tokens or 800,
-                do_sample=False,
-                temperature=0.7,
-                pad_token_id=pipe.tokenizer.eos_token_id,
-            )
-            logging.info(f"✅ MedGemma completed in {time.time() - t0:.2f}s")
+    messages = [
+        {"role": "system", "content": [{"type": "text", "text": SMARTHEAL_SYSTEM_PROMPT}]},
+        {"role": "user", "content": [
+            {"type": "image", "image": image_pil},
+            {"type": "text",  "text": uprompt},
+        ]},
+    ]
 
-            if out and len(out) > 0:
-                # Defensive extraction
-                try:
-                    return out[0]["generated_text"][-1].get("content", "").strip() or "⚠️ Empty response"
-                except Exception:
-                    return (out[0].get("generated_text", "") or "").strip() or "⚠️ Empty response"
-            return "⚠️ No output generated"
-        except Exception as e:
-            logging.error(f"❌ MedGemma generation error: {e}")
-            return f"❌ Report generation failed: {str(e)}"
-        finally:
-            try:
-                torch.cuda.empty_cache()
-            except Exception:
-                pass
-else:
-    def generate_medgemma_report_with_timeout(
-        patient_info: str,
-        visual_results: Dict,
-        guideline_context: str,
-        image_pil: Image.Image,
-        max_new_tokens: Optional[int] = None,
-    ) -> str:
-        """CPU-only path: return a warning so caller uses fallback."""
-        return "⚠️ GPU not available"
+    try:
+        # IMPORTANT: do not import transformers or touch CUDA here. Only call the GPU worker.
+        return _vlm_infer_gpu(messages, model_id, max_new_tokens, HF_TOKEN)
+    except Exception as e:
+        logging.error(f"VLM call failed: {e}")
+        return "⚠️ VLM error"
 
-# =============== MODEL INITIALIZATION (CPU-SAFE) ===============
+# ---------- Initialize CPU models ----------
 def load_yolo_model():
     YOLO = _import_ultralytics()
-    return YOLO(YOLO_MODEL_PATH)
-
+    # Construct model with CUDA masked to avoid auto-selecting cuda:0
+    with _no_cuda_env():
+        model = YOLO(YOLO_MODEL_PATH)
+    return model
 def load_segmentation_model():
+    import tensorflow as tf
     load_model = _import_tf_loader()
-    return load_model(SEG_MODEL_PATH, compile=False)
+    return load_model(SEG_MODEL_PATH, compile=False, custom_objects={'InputLayer': tf.keras.layers.InputLayer})
 
 def load_classification_pipeline():
     pipe = _import_hf_cls()
-    return pipe(
-        "image-classification",
-        model="Hemg/Wound-classification",
-        token=HF_TOKEN,
-        device="cpu",
-    )
+    return pipe("image-classification", model="Hemg/Wound-classification", token=HF_TOKEN, device="cpu")
 
 def load_embedding_model():
     Emb = _import_embeddings()
     return Emb(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"})
 
 def initialize_cpu_models() -> None:
-    """Initialize all CPU-only models once with robust fallbacks."""
-    # Hugging Face auth (optional)
     if HF_TOKEN:
         try:
             HfApi, HfFolder = _import_hf_hub()
             HfFolder.save_token(HF_TOKEN)
-            logging.info("✅ HuggingFace token set")
+            logging.info("✅ HF token set")
         except Exception as e:
             logging.warning(f"HF token save failed: {e}")
 
     if "det" not in models_cache:
         try:
             models_cache["det"] = load_yolo_model()
-            logging.info("✅ YOLO model loaded (CPU)")
+            logging.info("✅ YOLO loaded (CPU; CUDA masked in main)")
         except Exception as e:
             logging.error(f"YOLO load failed: {e}")
 
@@ -204,46 +242,46 @@ def initialize_cpu_models() -> None:
         try:
             if os.path.exists(SEG_MODEL_PATH):
                 models_cache["seg"] = load_segmentation_model()
-                logging.info("✅ Segmentation model loaded (CPU)")
+                m = models_cache["seg"]
+                ishape = getattr(m, "input_shape", None)
+                oshape = getattr(m, "output_shape", None)
+                logging.info(f"✅ Segmentation model loaded (CPU) | input_shape={ishape} output_shape={oshape}")
             else:
                 models_cache["seg"] = None
-                logging.warning("Segmentation model file not found; skipping seg.")
+                logging.warning("Segmentation model file missing; skipping.")
         except Exception as e:
             models_cache["seg"] = None
-            logging.warning(f"Segmentation model not available: {e}")
+            logging.warning(f"Segmentation unavailable: {e}")
 
     if "cls" not in models_cache:
         try:
             models_cache["cls"] = load_classification_pipeline()
-            logging.info("✅ Classification pipeline loaded (CPU)")
+            logging.info("✅ Classifier loaded (CPU)")
         except Exception as e:
             models_cache["cls"] = None
-            logging.warning(f"Classification pipeline not available: {e}")
+            logging.warning(f"Classifier unavailable: {e}")
 
     if "embedding_model" not in models_cache:
         try:
             models_cache["embedding_model"] = load_embedding_model()
-            logging.info("✅ Embedding model loaded (CPU)")
+            logging.info("✅ Embeddings loaded (CPU)")
         except Exception as e:
             models_cache["embedding_model"] = None
-            logging.warning(f"Embedding model not available: {e}")
+            logging.warning(f"Embeddings unavailable: {e}")
 
 def setup_knowledge_base() -> None:
-    """Load PDFs and create FAISS vector store (optional)."""
     if "vector_store" in knowledge_base_cache:
         return
-
-    docs = []
+    docs: List = []
     try:
         PyPDFLoader = _import_langchain_pdf()
         for pdf in GUIDELINE_PDFS:
             if os.path.exists(pdf):
                 try:
-                    loader = PyPDFLoader(pdf)
-                    docs.extend(loader.load())
+                    docs.extend(PyPDFLoader(pdf).load())
                     logging.info(f"Loaded PDF: {pdf}")
                 except Exception as e:
-                    logging.warning(f"Failed to load PDF {pdf}: {e}")
+                    logging.warning(f"PDF load failed ({pdf}): {e}")
     except Exception as e:
         logging.warning(f"LangChain PDF loader unavailable: {e}")
 
@@ -251,146 +289,536 @@ def setup_knowledge_base() -> None:
         try:
             from langchain.text_splitter import RecursiveCharacterTextSplitter
             FAISS = _import_langchain_faiss()
-            splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-            chunks = splitter.split_documents(docs)
+            chunks = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100).split_documents(docs)
             knowledge_base_cache["vector_store"] = FAISS.from_documents(chunks, models_cache["embedding_model"])
-            logging.info(f"✅ Knowledge base ready with {len(chunks)} chunks")
+            logging.info(f"✅ Knowledge base ready ({len(chunks)} chunks)")
         except Exception as e:
             knowledge_base_cache["vector_store"] = None
-            logging.warning(f"Knowledge base unavailable: {e}")
+            logging.warning(f"KB build failed: {e}")
     else:
         knowledge_base_cache["vector_store"] = None
-        logging.warning("Knowledge base disabled (no docs or embeddings).")
+        logging.warning("KB disabled (no docs or embeddings).")
 
-# Initialize on import
 initialize_cpu_models()
 setup_knowledge_base()
 
-# =============== AI PROCESSOR ===============
+# ---------- Calibration helpers ----------
+def _exif_to_dict(pil_img: Image.Image) -> Dict[str, object]:
+    out = {}
+    try:
+        exif = pil_img.getexif()
+        if not exif:
+            return out
+        for k, v in exif.items():
+            tag = TAGS.get(k, k)
+            out[tag] = v
+    except Exception:
+        pass
+    return out
+
+def _to_float(val) -> Optional[float]:
+    try:
+        if val is None:
+            return None
+        if isinstance(val, tuple) and len(val) == 2:
+            num, den = float(val[0]), float(val[1]) if float(val[1]) != 0 else 1.0
+            return num / den
+        return float(val)
+    except Exception:
+        return None
+
+def _estimate_sensor_width_mm(f_mm: Optional[float], f35: Optional[float]) -> Optional[float]:
+    if f_mm and f35 and f35 > 0:
+        return 36.0 * f_mm / f35
+    return None
+
+def estimate_px_per_cm_from_exif(pil_img: Image.Image, default_px_per_cm: float = DEFAULT_PX_PER_CM) -> Tuple[float, Dict]:
+    meta = {"used": "default", "f_mm": None, "f35": None, "sensor_w_mm": None, "distance_m": None}
+    try:
+        exif = _exif_to_dict(pil_img)
+        f_mm = _to_float(exif.get("FocalLength"))
+        f35 = _to_float(exif.get("FocalLengthIn35mmFilm") or exif.get("FocalLengthIn35mm"))
+        subj_dist_m = _to_float(exif.get("SubjectDistance"))
+        sensor_w_mm = _estimate_sensor_width_mm(f_mm, f35)
+        meta.update({"f_mm": f_mm, "f35": f35, "sensor_w_mm": sensor_w_mm, "distance_m": subj_dist_m})
+
+        if f_mm and sensor_w_mm and subj_dist_m and subj_dist_m > 0:
+            w_px = pil_img.width
+            field_w_mm = sensor_w_mm * (subj_dist_m * 1000.0) / f_mm
+            field_w_cm = field_w_mm / 10.0
+            px_per_cm = w_px / max(field_w_cm, 1e-6)
+            px_per_cm = float(np.clip(px_per_cm, PX_PER_CM_MIN, PX_PER_CM_MAX))
+            meta["used"] = "exif"
+            return px_per_cm, meta
+        return float(default_px_per_cm), meta
+    except Exception:
+        return float(default_px_per_cm), meta
+
+# ---------- Segmentation helpers ----------
+def _imagenet_norm(arr: np.ndarray) -> np.ndarray:
+    mean = np.array([123.675, 116.28, 103.53], dtype=np.float32)
+    std  = np.array([58.395, 57.12, 57.375], dtype=np.float32)
+    return (arr.astype(np.float32) - mean) / std
+
+def _preprocess_for_seg(bgr_roi: np.ndarray, target_hw: Tuple[int, int]) -> np.ndarray:
+    H, W = target_hw
+    resized = cv2.resize(bgr_roi, (W, H), interpolation=cv2.INTER_LINEAR)
+    if SEG_EXPECTS_RGB:
+        resized = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
+    if SEG_NORM.lower() == "imagenet":
+        x = _imagenet_norm(resized)
+    else:
+        x = resized.astype(np.float32) / 255.0
+    x = np.expand_dims(x, axis=0)  # (1,H,W,3)
+    return x
+
+def _to_prob(pred: np.ndarray) -> np.ndarray:
+    p = np.squeeze(pred)
+    pmin, pmax = float(p.min()), float(p.max())
+    if pmax > 1.0 or pmin < 0.0:
+        p = 1.0 / (1.0 + np.exp(-p))
+    return p.astype(np.float32)
+
+# ---- Adaptive threshold + GrabCut grow ----
+def _adaptive_prob_threshold(p: np.ndarray) -> float:
+    """
+    Choose a threshold that avoids tiny blobs while not swallowing skin.
+    Try Otsu and the 90th percentile, clamp to [0.25, 0.65], pick by area heuristic.
+    """
+    p01 = np.clip(p.astype(np.float32), 0, 1)
+    p255 = (p01 * 255).astype(np.uint8)
+
+    ret_otsu, _ = cv2.threshold(p255, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    thr_otsu = float(np.clip(ret_otsu / 255.0, 0.25, 0.65))
+    thr_pctl = float(np.clip(np.percentile(p01, 90), 0.25, 0.65))
+
+    def area_frac(thr: float) -> float:
+        return float((p01 >= thr).sum()) / float(p01.size)
+
+    af_otsu = area_frac(thr_otsu)
+    af_pctl = area_frac(thr_pctl)
+
+    def score(af: float) -> float:
+        target_low, target_high = 0.03, 0.10
+        if af < target_low: return abs(af - target_low) * 3.0
+        if af > target_high: return abs(af - target_high) * 1.5
+        return 0.0
+
+    return thr_otsu if score(af_otsu) <= score(af_pctl) else thr_pctl
+
+def _grabcut_refine(bgr: np.ndarray, seed01: np.ndarray, iters: int = 3) -> np.ndarray:
+    """Grow from a confident core into low-contrast margins."""
+    h, w = bgr.shape[:2]
+    gc = np.full((h, w), cv2.GC_PR_BGD, np.uint8)
+    k = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    seed_dil = cv2.dilate(seed01, k, iterations=1)
+    gc[seed01.astype(bool)] = cv2.GC_PR_FGD
+    gc[seed_dil.astype(bool)] = cv2.GC_FGD
+    gc[0, :], gc[-1, :], gc[:, 0], gc[:, 1] = cv2.GC_BGD, cv2.GC_BGD, cv2.GC_BGD, cv2.GC_BGD
+    bgdModel = np.zeros((1, 65), np.float64)
+    fgdModel = np.zeros((1, 65), np.float64)
+    cv2.grabCut(bgr, gc, None, bgdModel, fgdModel, iters, cv2.GC_INIT_WITH_MASK)
+    return np.where((gc == cv2.GC_FGD) | (gc == cv2.GC_PR_FGD), 1, 0).astype(np.uint8)
+
+def _fill_holes(mask01: np.ndarray) -> np.ndarray:
+    h, w = mask01.shape[:2]
+    ff = np.zeros((h + 2, w + 2), np.uint8)
+    m = (mask01 * 255).astype(np.uint8).copy()
+    cv2.floodFill(m, ff, (0, 0), 255)
+    m_inv = cv2.bitwise_not(m)
+    out = ((mask01 * 255) | m_inv) // 255
+    return out.astype(np.uint8)
+
+def _clean_mask(mask01: np.ndarray) -> np.ndarray:
+    """Open → Close → Fill holes → Largest component (no dilation)."""
+    mask01 = (mask01 > 0).astype(np.uint8)
+    k3 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+    k5 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask01 = cv2.morphologyEx(mask01, cv2.MORPH_OPEN, k3, iterations=1)
+    mask01 = cv2.morphologyEx(mask01, cv2.MORPH_CLOSE, k5, iterations=1)
+    mask01 = _fill_holes(mask01)
+    # Keep largest component only
+    num, labels, stats, _ = cv2.connectedComponentsWithStats(mask01, 8)
+    if num > 1:
+        areas = stats[1:, cv2.CC_STAT_AREA]
+        if areas.size:
+            largest_idx = 1 + int(np.argmax(areas))
+            mask01 = (labels == largest_idx).astype(np.uint8)
+    return (mask01 > 0).astype(np.uint8)
+
+# Global last debug dict (per-process)
+_last_seg_debug: Dict[str, object] = {}
+
+def segment_wound(image_bgr: np.ndarray, ts: str, out_dir: str) -> Tuple[np.ndarray, Dict[str, object]]:
+    """
+    TF model → adaptive threshold on prob → GrabCut grow → cleanup.
+    Fallback: KMeans-Lab.
+    Returns (mask_uint8_0_255, debug_dict)
+    """
+    debug = {"used": None, "reason": None, "positive_fraction": 0.0,
+             "thr": None, "heatmap_path": None, "roi_seen_by_model": None}
+
+    seg_model = models_cache.get("seg", None)
+
+    # --- Model path ---
+    if seg_model is not None:
+        try:
+            ishape = getattr(seg_model, "input_shape", None)
+            if not ishape or len(ishape) < 4:
+                raise ValueError(f"Bad seg input_shape: {ishape}")
+            th, tw = int(ishape[1]), int(ishape[2])
+
+            x = _preprocess_for_seg(image_bgr, (th, tw))
+            roi_seen_path = None
+            if SMARTHEAL_DEBUG:
+                roi_seen_path = os.path.join(out_dir, f"roi_for_seg_{ts}.png")
+                cv2.imwrite(roi_seen_path, image_bgr)
+
+            pred = seg_model.predict(x, verbose=0)
+            if isinstance(pred, (list, tuple)): pred = pred[0]
+            p = _to_prob(pred)
+            p = cv2.resize(p, (image_bgr.shape[1], image_bgr.shape[0]), interpolation=cv2.INTER_LINEAR)
+
+            heatmap_path = None
+            if SMARTHEAL_DEBUG:
+                hm = (np.clip(p, 0, 1) * 255).astype(np.uint8)
+                heat = cv2.applyColorMap(hm, cv2.COLORMAP_JET)
+                heatmap_path = os.path.join(out_dir, f"seg_pred_heatmap_{ts}.png")
+                cv2.imwrite(heatmap_path, heat)
+
+            thr = _adaptive_prob_threshold(p)
+            core01 = (p >= thr).astype(np.uint8)
+            core_frac = float(core01.sum()) / float(core01.size)
+
+            if core_frac < 0.005:
+                thr2 = max(thr - 0.10, 0.15)
+                core01 = (p >= thr2).astype(np.uint8)
+                thr = thr2
+                core_frac = float(core01.sum()) / float(core01.size)
+
+            if core01.any():
+                gc01 = _grabcut_refine(image_bgr, core01, iters=3)
+                mask01 = _clean_mask(gc01)
+            else:
+                mask01 = np.zeros(core01.shape, np.uint8)
+
+            pos_frac = float(mask01.sum()) / float(mask01.size)
+            logging.info(f"SegModel USED | thr={float(thr):.2f} core_frac={core_frac:.4f} final_frac={pos_frac:.4f}")
+
+            debug.update({
+                "used": "tf_model",
+                "reason": "ok",
+                "positive_fraction": pos_frac,
+                "thr": float(thr),
+                "heatmap_path": heatmap_path,
+                "roi_seen_by_model": roi_seen_path
+            })
+            return (mask01 * 255).astype(np.uint8), debug
+
+        except Exception as e:
+            logging.warning(f"⚠️ Segmentation model failed → fallback. Reason: {e}")
+            debug.update({"used": "fallback_kmeans", "reason": f"model_failed: {e}"})
+
+    # --- Fallback: KMeans in Lab (reddest cluster as wound) ---
+    Z = image_bgr.reshape((-1, 3)).astype(np.float32)
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+    _, labels, centers = cv2.kmeans(Z, 2, None, criteria, 5, cv2.KMEANS_PP_CENTERS)
+    centers_u8 = centers.astype(np.uint8).reshape(1, 2, 3)
+    centers_lab = cv2.cvtColor(centers_u8, cv2.COLOR_BGR2LAB)[0]
+    wound_idx = int(np.argmax(centers_lab[:, 1]))  # maximize a* (red)
+    mask01 = (labels.reshape(image_bgr.shape[:2]) == wound_idx).astype(np.uint8)
+    mask01 = _clean_mask(mask01)
+
+    pos_frac = float(mask01.sum()) / float(mask01.size)
+    logging.info(f"KMeans USED | final_frac={pos_frac:.4f}")
+
+    debug.update({
+        "used": "fallback_kmeans",
+        "reason": debug.get("reason") or "no_model",
+        "positive_fraction": pos_frac,
+        "thr": None
+    })
+    return (mask01 * 255).astype(np.uint8), debug
+
+# ---------- Measurement + overlay helpers ----------
+def largest_component_mask(binary01: np.ndarray, min_area_px: int = 50) -> np.ndarray:
+    num, labels, stats, _ = cv2.connectedComponentsWithStats(binary01.astype(np.uint8), connectivity=8)
+    if num <= 1:
+        return binary01.astype(np.uint8)
+    areas = stats[1:, cv2.CC_STAT_AREA]
+    if areas.size == 0 or areas.max() < min_area_px:
+        return binary01.astype(np.uint8)
+    largest_idx = 1 + int(np.argmax(areas))
+    return (labels == largest_idx).astype(np.uint8)
+
+def measure_min_area_rect(mask01: np.ndarray, px_per_cm: float) -> Tuple[float, float, Tuple]:
+    contours, _ = cv2.findContours(mask01.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return 0.0, 0.0, (None, None)
+    cnt = max(contours, key=cv2.contourArea)
+    rect = cv2.minAreaRect(cnt)
+    (w_px, h_px) = rect[1]
+    length_px, breadth_px = (max(w_px, h_px), min(w_px, h_px))
+    length_cm = round(length_px / max(px_per_cm, 1e-6), 2)
+    breadth_cm = round(breadth_px / max(px_per_cm, 1e-6), 2)
+    box = cv2.boxPoints(rect).astype(int)
+    return length_cm, breadth_cm, (box, rect[0])
+
+def area_cm2_from_contour(mask01: np.ndarray, px_per_cm: float) -> Tuple[float, Optional[np.ndarray]]:
+    """Area from largest polygon (sub-pixel); returns (area_cm2, contour)."""
+    m = (mask01 > 0).astype(np.uint8)
+    contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return 0.0, None
+    cnt = max(contours, key=cv2.contourArea)
+    poly_area_px2 = float(cv2.contourArea(cnt))
+    area_cm2 = round(poly_area_px2 / (max(px_per_cm, 1e-6) ** 2), 2)
+    return area_cm2, cnt
+
+def clamp_area_with_minrect(cnt: np.ndarray, px_per_cm: float, area_cm2_poly: float) -> float:
+    rect = cv2.minAreaRect(cnt)
+    (w_px, h_px) = rect[1]
+    rect_area_px2 = float(max(w_px, 0.0) * max(h_px, 0.0))
+    rect_area_cm2 = rect_area_px2 / (max(px_per_cm, 1e-6) ** 2)
+    return round(min(area_cm2_poly, rect_area_cm2 * 1.05), 2)
+
+def draw_measurement_overlay(
+    base_bgr: np.ndarray,
+    mask01: np.ndarray,
+    rect_box: np.ndarray,
+    length_cm: float,
+    breadth_cm: float,
+    thickness: int = 2
+) -> np.ndarray:
+    """
+    1) Strong red mask overlay + white contour
+    2) Min-area rectangle
+    3) Double-headed arrows labeled Length/Width
+    """
+    overlay = base_bgr.copy()
+
+    # Mask tint
+    mask255 = (mask01 * 255).astype(np.uint8)
+    mask3 = cv2.merge([mask255, mask255, mask255])
+    red = np.zeros_like(overlay); red[:] = (0, 0, 255)
+    alpha = 0.55
+    tinted = cv2.addWeighted(overlay, 1 - alpha, red, alpha, 0)
+    overlay = np.where(mask3 > 0, tinted, overlay)
+
+    # Contour
+    cnts, _ = cv2.findContours(mask255, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if cnts:
+        cv2.drawContours(overlay, cnts, -1, (255, 255, 255), 2)
+
+    if rect_box is not None:
+        cv2.polylines(overlay, [rect_box], True, (255, 255, 255), thickness)
+        pts = rect_box.reshape(-1, 2)
+
+        def midpoint(a, b): return (int((a[0] + b[0]) / 2), int((a[1] + b[1]) / 2))
+        e = [np.linalg.norm(pts[i] - pts[(i + 1) % 4]) for i in range(4)]
+        long_edge_idx = int(np.argmax(e))
+        mids = [midpoint(pts[i], pts[(i + 1) % 4]) for i in range(4)]
+        long_pair = (long_edge_idx, (long_edge_idx + 2) % 4)
+        short_pair = ((long_edge_idx + 1) % 4, (long_edge_idx + 3) % 4)
+
+        def draw_double_arrow(img, p1, p2):
+            cv2.arrowedLine(img, p1, p2, (0, 0, 0), thickness + 2, tipLength=0.05)
+            cv2.arrowedLine(img, p2, p1, (0, 0, 0), thickness + 2, tipLength=0.05)
+            cv2.arrowedLine(img, p1, p2, (255, 255, 255), thickness, tipLength=0.05)
+            cv2.arrowedLine(img, p2, p1, (255, 255, 255), thickness, tipLength=0.05)
+
+        def put_label(text, anchor):
+            org = (anchor[0] + 6, anchor[1] - 6)
+            cv2.putText(overlay, text, org, cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 4, cv2.LINE_AA)
+            cv2.putText(overlay, text, org, cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2, cv2.LINE_AA)
+
+        draw_double_arrow(overlay, mids[long_pair[0]], mids[long_pair[1]])
+        draw_double_arrow(overlay, mids[short_pair[0]], mids[short_pair[1]])
+        put_label(f"Length: {length_cm:.2f} cm", mids[long_pair[0]])
+        put_label(f"Width:  {breadth_cm:.2f} cm", mids[short_pair[0]])
+
+    return overlay
+
+# ---------- AI PROCESSOR ----------
 class AIProcessor:
     def __init__(self):
         self.models_cache = models_cache
         self.knowledge_base_cache = knowledge_base_cache
-        self.px_per_cm = PIXELS_PER_CM
         self.uploads_dir = UPLOADS_DIR
         self.dataset_id = DATASET_ID
         self.hf_token = HF_TOKEN
 
-    # ---------- Image utilities ----------
     def _ensure_analysis_dir(self) -> str:
         out_dir = os.path.join(self.uploads_dir, "analysis")
         os.makedirs(out_dir, exist_ok=True)
         return out_dir
 
     def perform_visual_analysis(self, image_pil: Image.Image) -> Dict:
-        """YOLO detect → (optional) Keras seg → (optional) HF classifier → save visuals."""
+        """
+        YOLO detect → crop ROI → segment_wound(ROI) → clean mask →
+        minAreaRect measurement (cm) using EXIF px/cm → save outputs.
+        """
         try:
+            px_per_cm, exif_meta = estimate_px_per_cm_from_exif(image_pil, DEFAULT_PX_PER_CM)
+            # Guardrails for calibration to avoid huge area blow-ups
+            px_per_cm = float(np.clip(px_per_cm, 20.0, 350.0))
+            if (exif_meta or {}).get("used") != "exif":
+                logging.warning(f"Calibration fallback used: px_per_cm={px_per_cm:.2f} (default). Prefer ruler/Aruco for accuracy.")
+
             image_cv = cv2.cvtColor(np.array(image_pil.convert("RGB")), cv2.COLOR_RGB2BGR)
 
-            det = self.models_cache.get("det")
-            if det is None:
+            # --- Detection ---
+            det_model = self.models_cache.get("det")
+            if det_model is None:
                 raise RuntimeError("YOLO model not loaded")
-
-            # YOLO on CPU
-            results = det.predict(image_cv, verbose=False, device="cpu")
-            if not results or not getattr(results[0], "boxes", None) or len(results[0].boxes) == 0:
-                raise ValueError("No wound could be detected.")
+            # Force CPU inference and avoid CUDA touch
+            results = det_model.predict(image_cv, verbose=False, device="cpu")
+            if (not results) or (not getattr(results[0], "boxes", None)) or (len(results[0].boxes) == 0):
+                try:
+                    import gradio as gr
+                    raise gr.Error("No wound could be detected.")
+                except Exception:
+                    raise RuntimeError("No wound could be detected.")
 
             box = results[0].boxes[0].xyxy[0].cpu().numpy().astype(int)
             x1, y1, x2, y2 = [int(v) for v in box]
             x1, y1 = max(0, x1), max(0, y1)
             x2, y2 = min(image_cv.shape[1], x2), min(image_cv.shape[0], y2)
-            detected_region_cv = image_cv[y1:y2, x1:x2]
-
-            # Optional segmentation
-            seg_model = self.models_cache.get("seg")
-            length = breadth = area = 0.0
-            seg_path = None
-            if seg_model is not None and detected_region_cv.size > 0:
+            roi = image_cv[y1:y2, x1:x2].copy()
+            if roi.size == 0:
                 try:
-                    input_size = seg_model.input_shape[1:3]
-                    resized = cv2.resize(detected_region_cv, (input_size[1], input_size[0]))
-                    mask_pred = seg_model.predict(np.expand_dims(resized / 255.0, 0), verbose=0)[0]
-                    mask_np = (mask_pred[:, :, 0] > 0.5).astype(np.uint8)
-
-                    contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-                    if contours:
-                        cnt = max(contours, key=cv2.contourArea)
-                        x, y, w, h = cv2.boundingRect(cnt)
-                        length = round(h / self.px_per_cm, 2)
-                        breadth = round(w / self.px_per_cm, 2)
-                        area = round(cv2.contourArea(cnt) / (self.px_per_cm ** 2), 2)
-
-                        # overlay visualization
-                        mask_resized = cv2.resize(
-                            mask_np * 255,
-                            (detected_region_cv.shape[1], detected_region_cv.shape[0]),
-                            interpolation=cv2.INTER_NEAREST,
-                        )
-                        overlay = detected_region_cv.copy()
-                        overlay[mask_resized > 127] = [0, 0, 255]
-                        seg_vis = cv2.addWeighted(detected_region_cv, 0.7, overlay, 0.3, 0)
-
-                        ts = datetime.now().strftime("%Y%m%d_%H%M%S")
-                        out_dir = self._ensure_analysis_dir()
-                        seg_path = os.path.join(out_dir, f"segmentation_{ts}.png")
-                        cv2.imwrite(seg_path, seg_vis)
-                except Exception as e:
-                    logging.warning(f"Segmentation step skipped: {e}")
+                    import gradio as gr
+                    raise gr.Error("Detected ROI is empty.")
+                except Exception:
+                    raise RuntimeError("Detected ROI is empty.")
+
+            out_dir = self._ensure_analysis_dir()
+            ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+
+            # --- Segmentation (model-first + KMeans fallback) ---
+            mask_u8_255, seg_debug = segment_wound(roi, ts, out_dir)
+            mask01 = (mask_u8_255 > 127).astype(np.uint8)
+
+            if mask01.any():
+                mask01 = _clean_mask(mask01)
+                logging.debug(f"Mask postproc: px_after={int(mask01.sum())}")
+
+            # --- Measurement (accurate & conservative) ---
+            if mask01.any():
+                length_cm, breadth_cm, (box_pts, _) = measure_min_area_rect(mask01, px_per_cm)
+                area_poly_cm2, largest_cnt = area_cm2_from_contour(mask01, px_per_cm)
+                if largest_cnt is not None:
+                    surface_area_cm2 = clamp_area_with_minrect(largest_cnt, px_per_cm, area_poly_cm2)
+                else:
+                    surface_area_cm2 = area_poly_cm2
+
+                anno_roi = draw_measurement_overlay(roi, mask01, box_pts, length_cm, breadth_cm)
+                segmentation_empty = False
+            else:
+                # Fallback if seg failed: use ROI dimensions
+                h_px = max(0, y2 - y1); w_px = max(0, x2 - x1)
+                length_cm = round(max(h_px, w_px) / px_per_cm, 2)
+                breadth_cm = round(min(h_px, w_px) / px_per_cm, 2)
+                surface_area_cm2 = round((h_px * w_px) / (px_per_cm ** 2), 2)
+                anno_roi = roi.copy()
+                cv2.rectangle(anno_roi, (2, 2), (anno_roi.shape[1]-3, anno_roi.shape[0]-3), (0, 0, 255), 3)
+                cv2.line(anno_roi, (0, 0), (anno_roi.shape[1]-1, anno_roi.shape[0]-1), (0, 0, 255), 2)
+                cv2.line(anno_roi, (anno_roi.shape[1]-1, 0), (0, anno_roi.shape[0]-1), (0, 0, 255), 2)
+                box_pts = None
+                segmentation_empty = True
+
+            # --- Save visualizations ---
+            original_path = os.path.join(out_dir, f"original_{ts}.png")
+            cv2.imwrite(original_path, image_cv)
+
+            det_vis = image_cv.copy()
+            cv2.rectangle(det_vis, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            detection_path = os.path.join(out_dir, f"detection_{ts}.png")
+            cv2.imwrite(detection_path, det_vis)
+
+            roi_mask_path = os.path.join(out_dir, f"roi_mask_{ts}.png")
+            cv2.imwrite(roi_mask_path, (mask01 * 255).astype(np.uint8))
+
+            # ROI overlay (mask tint + contour, without arrows)
+            mask255 = (mask01 * 255).astype(np.uint8)
+            mask3   = cv2.merge([mask255, mask255, mask255])
+            red     = np.zeros_like(roi); red[:] = (0, 0, 255)
+            alpha   = 0.55
+            tinted  = cv2.addWeighted(roi, 1 - alpha, red, alpha, 0)
+            if mask255.any():
+                roi_overlay = np.where(mask3 > 0, tinted, roi)
+                cnts, _ = cv2.findContours(mask255, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                cv2.drawContours(roi_overlay, cnts, -1, (255, 255, 255), 2)
+            else:
+                roi_overlay = anno_roi
+
+            seg_full = image_cv.copy()
+            seg_full[y1:y2, x1:x2] = roi_overlay
+            segmentation_path = os.path.join(out_dir, f"segmentation_{ts}.png")
+            cv2.imwrite(segmentation_path, seg_full)
+
+            segmentation_roi_path = os.path.join(out_dir, f"segmentation_roi_{ts}.png")
+            cv2.imwrite(segmentation_roi_path, roi_overlay)
+
+            # Annotated (mask + arrows + labels) in full-frame
+            anno_full = image_cv.copy()
+            anno_full[y1:y2, x1:x2] = anno_roi
+            annotated_seg_path = os.path.join(out_dir, f"segmentation_annotated_{ts}.png")
+            cv2.imwrite(annotated_seg_path, anno_full)
 
-            # Optional classification
+            # --- Optional classification ---
             wound_type = "Unknown"
             cls_pipe = self.models_cache.get("cls")
             if cls_pipe is not None:
                 try:
-                    detected_image_pil = Image.fromarray(cv2.cvtColor(detected_region_cv, cv2.COLOR_BGR2RGB))
-                    preds = cls_pipe(detected_image_pil)
+                    preds = cls_pipe(Image.fromarray(cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)))
                     if preds:
                         wound_type = max(preds, key=lambda x: x.get("score", 0)).get("label", "Unknown")
                 except Exception as e:
-                    logging.warning(f"Classification step failed: {e}")
-
-            # Save detection & original
-            out_dir = self._ensure_analysis_dir()
-            ts = datetime.now().strftime("%Y%m%d_%H%M%S")
-            det_vis = image_cv.copy()
-            cv2.rectangle(det_vis, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            det_path = os.path.join(out_dir, f"detection_{ts}.png")
-            cv2.imwrite(det_path, det_vis)
-
-            original_path = os.path.join(out_dir, f"original_{ts}.png")
-            cv2.imwrite(original_path, image_cv)
+                    logging.warning(f"Classification failed: {e}")
+
+            # Log end-of-seg summary
+            seg_summary = {
+                "seg_used": seg_debug.get("used"),
+                "seg_reason": seg_debug.get("reason"),
+                "positive_fraction": round(float(seg_debug.get("positive_fraction", 0.0)), 6),
+                "threshold": seg_debug.get("thr"),
+                "segmentation_empty": segmentation_empty,
+                "exif_px_per_cm": round(px_per_cm, 3),
+            }
+            _log_kv("SEG_SUMMARY", seg_summary)
 
             return {
                 "wound_type": wound_type,
-                "length_cm": length,
-                "breadth_cm": breadth,
-                "surface_area_cm2": area,
+                "length_cm": length_cm,
+                "breadth_cm": breadth_cm,
+                "surface_area_cm2": surface_area_cm2,
+                "px_per_cm": round(px_per_cm, 2),
+                "calibration_meta": exif_meta,
                 "detection_confidence": float(results[0].boxes.conf[0].cpu().item())
-                if getattr(results[0].boxes, "conf", None) is not None
-                else 0.0,
-                "detection_image_path": det_path,
-                "segmentation_image_path": seg_path,
+                    if getattr(results[0].boxes, "conf", None) is not None else 0.0,
+                "detection_image_path": detection_path,
+                "segmentation_image_path": annotated_seg_path,
+                "segmentation_annotated_path": annotated_seg_path,
+                "segmentation_roi_path": segmentation_roi_path,
+                "roi_mask_path": roi_mask_path,
+                "segmentation_empty": segmentation_empty,
+                "segmentation_debug": seg_debug,
                 "original_image_path": original_path,
             }
         except Exception as e:
-            logging.error(f"Visual analysis failed: {e}")
+            logging.error(f"Visual analysis failed: {e}", exc_info=True)
             raise
 
+    # ---------- Knowledge base + reporting ----------
     def query_guidelines(self, query: str) -> str:
-        """Query the knowledge base (optional)."""
         try:
             vs = self.knowledge_base_cache.get("vector_store")
             if not vs:
                 return "Knowledge base is not available."
-            # support both old and new retriever APIs
-            try:
-                retriever = vs.as_retriever(search_kwargs={"k": 5})
-                docs = retriever.get_relevant_documents(query)  # LC >= 0.2
-            except Exception:
-                retriever = vs.as_retriever(search_kwargs={"k": 5})
-                # older invoke API
-                docs = retriever.invoke(query)
+            retriever = vs.as_retriever(search_kwargs={"k": 5})
+            # Modern API (avoid get_relevant_documents deprecation)
+            docs = retriever.invoke(query)
             lines: List[str] = []
             for d in docs:
                 src = (d.metadata or {}).get("source", "N/A")
@@ -401,9 +829,7 @@ class AIProcessor:
             logging.warning(f"Guidelines query failed: {e}")
             return f"Guidelines query failed: {str(e)}"
 
-    # ---------- Report builders ----------
     def _generate_fallback_report(self, patient_info: str, visual_results: Dict, guideline_context: str) -> str:
-        """Plaintext/markdown fallback when MedGemma is unavailable."""
         return f"""# 🩺 SmartHeal AI - Comprehensive Wound Analysis Report
 ## 📋 Patient Information
 {patient_info}
@@ -412,10 +838,12 @@ class AIProcessor:
 - **Dimensions**: {visual_results.get('length_cm', 0)} cm × {visual_results.get('breadth_cm', 0)} cm
 - **Surface Area**: {visual_results.get('surface_area_cm2', 0)} cm²
 - **Detection Confidence**: {visual_results.get('detection_confidence', 0):.1%}
+- **Calibration**: {visual_results.get('px_per_cm','?')} px/cm ({(visual_results.get('calibration_meta') or {}).get('used','default')})
 ## 📊 Analysis Images
 - **Original**: {visual_results.get('original_image_path', 'N/A')}
 - **Detection**: {visual_results.get('detection_image_path', 'N/A')}
 - **Segmentation**: {visual_results.get('segmentation_image_path', 'N/A')}
+- **Annotated**: {visual_results.get('segmentation_annotated_path', 'N/A')}
 ## 🎯 Clinical Summary
 Automated analysis provides quantitative measurements; verify via clinical examination.
 ## 💊 Recommendations
@@ -423,10 +851,10 @@ Automated analysis provides quantitative measurements; verify via clinical exami
 - Debride necrotic tissue if indicated (clinical decision)
 - Document with serial photos and measurements
 ## 📅 Monitoring
-- Daily in week 1, then every 2-3 days (or as indicated)
+- Daily in week 1, then every 2–3 days (or as indicated)
 - Weekly progress review
 ## 📚 Guideline Context
-{(guideline_context or '')[:800]}{'...' if guideline_context and len(guideline_context) > 800 else ''}
+{(guideline_context or '')[:800]}{"..." if guideline_context and len(guideline_context) > 800 else ''}
 **Disclaimer:** Automated, for decision support only. Verify clinically.
 """
 
@@ -438,22 +866,19 @@ Automated analysis provides quantitative measurements; verify via clinical exami
         image_pil: Image.Image,
         max_new_tokens: Optional[int] = None,
     ) -> str:
-        """Try MedGemma (GPU) → fallback report."""
         try:
-            report = generate_medgemma_report_with_timeout(
+            report = generate_medgemma_report(
                 patient_info, visual_results, guideline_context, image_pil, max_new_tokens
             )
             if report and report.strip() and not report.startswith(("⚠️", "❌")):
                 return report
-            logging.warning("MedGemma unavailable/invalid; using fallback.")
+            logging.warning("VLM unavailable/invalid; using fallback.")
             return self._generate_fallback_report(patient_info, visual_results, guideline_context)
         except Exception as e:
             logging.error(f"Report generation failed: {e}")
             return self._generate_fallback_report(patient_info, visual_results, guideline_context)
 
-    # ---------- HF dataset commit ----------
     def save_and_commit_image(self, image_pil: Image.Image) -> str:
-        """Save image locally and optionally upload to HF dataset."""
         try:
             os.makedirs(self.uploads_dir, exist_ok=True)
             ts = datetime.now().strftime("%Y%m%d_%H%M%S")
@@ -462,17 +887,17 @@ Automated analysis provides quantitative measurements; verify via clinical exami
             image_pil.convert("RGB").save(path)
             logging.info(f"✅ Image saved locally: {path}")
 
-            if self.hf_token and self.dataset_id:
+            if HF_TOKEN and DATASET_ID:
                 try:
                     HfApi, HfFolder = _import_hf_hub()
-                    HfFolder.save_token(self.hf_token)
+                    HfFolder.save_token(HF_TOKEN)
                     api = HfApi()
                     api.upload_file(
                         path_or_fileobj=path,
                         path_in_repo=f"images/{filename}",
-                        repo_id=self.dataset_id,
+                        repo_id=DATASET_ID,
                         repo_type="dataset",
-                        token=self.hf_token,
+                        token=HF_TOKEN,
                         commit_message=f"Upload wound image: {filename}",
                     )
                     logging.info("✅ Image committed to HF dataset")
@@ -484,28 +909,23 @@ Automated analysis provides quantitative measurements; verify via clinical exami
             logging.error(f"Failed to save/commit image: {e}")
             return ""
 
-    # ---------- Orchestrator ----------
     def full_analysis_pipeline(self, image_pil: Image.Image, questionnaire_data: Dict) -> Dict:
-        """End-to-end analysis with robust fallbacks."""
         try:
             saved_path = self.save_and_commit_image(image_pil)
-
             visual_results = self.perform_visual_analysis(image_pil)
 
-            # Patient info summary text
             pi = questionnaire_data or {}
             patient_info = (
-                f"Age: {pi.get('age', 'N/A')}, "
-                f"Diabetic: {pi.get('diabetic', 'N/A')}, "
-                f"Allergies: {pi.get('allergies', 'N/A')}, "
-                f"Date of Wound: {pi.get('date_of_injury', 'N/A')}, "
-                f"Professional Care: {pi.get('professional_care', 'N/A')}, "
-                f"Oozing/Bleeding: {pi.get('oozing_bleeding', 'N/A')}, "
-                f"Infection: {pi.get('infection', 'N/A')}, "
-                f"Moisture: {pi.get('moisture', 'N/A')}"
+                f"Age: {pi.get('age','N/A')}, "
+                f"Diabetic: {pi.get('diabetic','N/A')}, "
+                f"Allergies: {pi.get('allergies','N/A')}, "
+                f"Date of Wound: {pi.get('date_of_injury','N/A')}, "
+                f"Professional Care: {pi.get('professional_care','N/A')}, "
+                f"Oozing/Bleeding: {pi.get('oozing_bleeding','N/A')}, "
+                f"Infection: {pi.get('infection','N/A')}, "
+                f"Moisture: {pi.get('moisture','N/A')}"
             )
 
-            # Query guidelines
             query = (
                 f"best practices for managing a {visual_results.get('wound_type','Unknown')} "
                 f"with moisture '{pi.get('moisture','unknown')}' and infection '{pi.get('infection','unknown')}' "
@@ -513,18 +933,16 @@ Automated analysis provides quantitative measurements; verify via clinical exami
             )
             guideline_context = self.query_guidelines(query)
 
-            # Generate final report
-            report = self.generate_final_report(patient_info=patient_info,
-                                                visual_results=visual_results,
-                                                guideline_context=guideline_context,
-                                                image_pil=image_pil)
+            report = self.generate_final_report(patient_info, visual_results, guideline_context, image_pil)
 
             return {
                 "success": True,
                 "visual_analysis": visual_results,
                 "report": report,
                 "saved_image_path": saved_path,
-                "guideline_context": (guideline_context or "")[:500] + ("..." if guideline_context and len(guideline_context) > 500 else ""),
+                "guideline_context": (guideline_context or "")[:500] + (
+                    "..." if guideline_context and len(guideline_context) > 500 else ""
+                ),
             }
         except Exception as e:
             logging.error(f"Pipeline error: {e}")
@@ -538,7 +956,6 @@ Automated analysis provides quantitative measurements; verify via clinical exami
             }
 
     def analyze_wound(self, image, questionnaire_data: Dict) -> Dict:
-        """Public entrypoint used by your UI."""
         try:
             if isinstance(image, str):
                 if not os.path.exists(image):
@@ -561,4 +978,4 @@ Automated analysis provides quantitative measurements; verify via clinical exami
                 "report": f"Analysis initialization failed: {str(e)}",
                 "saved_image_path": None,
                 "guideline_context": "",
-            }
+            }