Update src/ai_processor.py

src/ai_processor.py

@@ -1,58 +1,426 @@
-# src/ai_processor.py
+# smartheal_ai_processor.py
+# Verbose, instrumented version — preserves public class/function names
+# Turn on deep logging: export LOGLEVEL=DEBUG SMARTHEAL_DEBUG=1
+
 import os
 import logging
+from datetime import datetime
+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 datetime import datetime
+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()))
+
+# --- Spaces GPU decorator (REQUIRED) ---
+from spaces import GPU as _SPACES_GPU
+
+@_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
+GUIDELINE_PDFS = ["src/eHealth in Wound Care.pdf", "src/IWGDF Guideline.pdf", "src/evaluation.pdf"]
+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"))
+
+models_cache: Dict[str, object] = {}
+knowledge_base_cache: Dict[str, object] = {}
+
+# ---------- 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():
+    # 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")
+    from tensorflow.keras.models import load_model
+    return load_model
+
+def _import_hf_cls():
+    from transformers import pipeline
+    return pipeline
+
+def _import_embeddings():
+    from langchain_community.embeddings import HuggingFaceEmbeddings
+    return HuggingFaceEmbeddings
+
+def _import_langchain_pdf():
+    from langchain_community.document_loaders import PyPDFLoader
+    return PyPDFLoader
+
+def _import_langchain_faiss():
+    from langchain_community.vectorstores import FAISS
+    return FAISS
+
+def _import_hf_hub():
+    from huggingface_hub import HfApi, HfFolder
+    return HfApi, HfFolder
+
+# ---------- 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}
 
-# ---- Safe env defaults (do NOT init CUDA in main) ----
-os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
-os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")  # mask GPU in main
-HF_HUB_DISABLE_TELEMETRY = os.environ.get("HF_HUB_DISABLE_TELEMETRY", "1")
+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.
+"""
+
+# ---------- 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:
+        txt = out[0]["generated_text"][-1].get("content", "")
+    except Exception:
+        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": "system", "content": [{"type": "text", "text": SMARTHEAL_SYSTEM_PROMPT}]},
+        {"role": "user", "content": [
+            {"type": "image", "image": image_pil},
+            {"type": "text",  "text": uprompt},
+        ]},
+    ]
 
-import json
+    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"
+
+# ---------- Initialize CPU models ----------
+def load_yolo_model():
+    YOLO = _import_ultralytics()
+    # Construct model with CUDA masked to avoid auto-selecting cuda:0
+    with _no_cuda_env():
+        model = YOLO(YOLO_MODEL_PATH)
+    return modeldef load_segmentation_model():
+    import tensorflow as tf
+    load_model = _import_tf_loader()
+    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")
+
+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:
+    if HF_TOKEN:
+        try:
+            HfApi, HfFolder = _import_hf_hub()
+            HfFolder.save_token(HF_TOKEN)
+            logging.info("✅ HF token set")
+        except Exception as e:
+            logging.warning(f"HF token save failed: {e}")
 
-# Light imports that won't trigger CUDA
-from transformers import pipeline
-from ultralytics import YOLO
+    if "det" not in models_cache:
+        try:
+            models_cache["det"] = load_yolo_model()
+            logging.info("✅ YOLO loaded (CPU; CUDA masked in main)")
+        except Exception as e:
+            logging.error(f"YOLO load failed: {e}")
 
-# TensorFlow: keep on CPU in main process
-import tensorflow as tf
-try:
-    tf.config.set_visible_devices([], "GPU")
-except Exception:
-    pass
+    if "seg" not in models_cache:
+        try:
+            if os.path.exists(SEG_MODEL_PATH):
+                models_cache["seg"] = load_segmentation_model()
+                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 missing; skipping.")
+        except Exception as e:
+            models_cache["seg"] = None
+            logging.warning(f"Segmentation unavailable: {e}")
 
-# LangChain bits (match your old code; no function name change)
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
+    if "cls" not in models_cache:
+        try:
+            models_cache["cls"] = load_classification_pipeline()
+            logging.info("✅ Classifier loaded (CPU)")
+        except Exception as e:
+            models_cache["cls"] = None
+            logging.warning(f"Classifier unavailable: {e}")
 
-from huggingface_hub import HfApi, HfFolder
+    if "embedding_model" not in models_cache:
+        try:
+            models_cache["embedding_model"] = load_embedding_model()
+            logging.info("✅ Embeddings loaded (CPU)")
+        except Exception as e:
+            models_cache["embedding_model"] = None
+            logging.warning(f"Embeddings unavailable: {e}")
 
-# Spaces (ZeroGPU)
-try:
-    import spaces
-    SPACES_AVAILABLE = True
-except Exception:
-    spaces = None
-    SPACES_AVAILABLE = False
+def setup_knowledge_base() -> None:
+    if "vector_store" in knowledge_base_cache:
+        return
+    docs: List = []
+    try:
+        PyPDFLoader = _import_langchain_pdf()
+        for pdf in GUIDELINE_PDFS:
+            if os.path.exists(pdf):
+                try:
+                    docs.extend(PyPDFLoader(pdf).load())
+                    logging.info(f"Loaded PDF: {pdf}")
+                except Exception as e:
+                    logging.warning(f"PDF load failed ({pdf}): {e}")
+    except Exception as e:
+        logging.warning(f"LangChain PDF loader unavailable: {e}")
 
-from .config import Config
+    if docs and models_cache.get("embedding_model"):
+        try:
+            from langchain.text_splitter import RecursiveCharacterTextSplitter
+            FAISS = _import_langchain_faiss()
+            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 ({len(chunks)} chunks)")
+        except Exception as e:
+            knowledge_base_cache["vector_store"] = None
+            logging.warning(f"KB build failed: {e}")
+    else:
+        knowledge_base_cache["vector_store"] = None
+        logging.warning("KB disabled (no docs or embeddings).")
+
+initialize_cpu_models()
+setup_knowledge_base()
+
+# ---------- 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
 
-# ----------------------------- utils -----------------------------
-def _largest_component(mask01: np.ndarray, min_area_px: int = 50) -> np.ndarray:
-    num, labels, stats, _ = cv2.connectedComponentsWithStats(mask01.astype(np.uint8), 8)
-    if num <= 1:
-        return (mask01 > 0).astype(np.uint8)
-    areas = stats[1:, cv2.CC_STAT_AREA]
-    if areas.size == 0 or areas.max() < min_area_px:
-        return (mask01 > 0).astype(np.uint8)
-    idx = 1 + int(np.argmax(areas))
-    return (labels == idx).astype(np.uint8)
+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]
@@ -64,610 +432,560 @@ def _fill_holes(mask01: np.ndarray) -> np.ndarray:
     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)
-    mask01 = _largest_component(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)
 
-def _grabcut_refine(bgr: np.ndarray, seed01: np.ndarray, iters: int = 3) -> np.ndarray:
-    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)
+# 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}"})
 
-def _fallback_segment(roi_bgr: np.ndarray) -> np.ndarray:
-    """Robust OpenCV fallback: Lab 2-cluster (maximize a*), then GrabCut grow + cleanup."""
-    Z = roi_bgr.reshape((-1, 3)).astype(np.float32)
+    # --- 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]))  # reddest cluster
-    seed01 = (labels.reshape(roi_bgr.shape[:2]) == wound_idx).astype(np.uint8)
-    gc01 = _grabcut_refine(roi_bgr, seed01, iters=3)
-    return _clean_mask(gc01)
-
-def _safe_load_seg_model(path: str):
-    """Try multiple loaders to survive Keras 3 / TF 2.15 / h5 mismatches."""
-    if not os.path.exists(path):
-        return None
-    try:
-        # Keras legacy API (present in TF 2.13+ with legacy shim)
-        from tensorflow import keras as tfk
-        if hasattr(tfk, "saving") and hasattr(tfk.saving, "legacy"):
-            return tfk.saving.legacy.load_model(path, compile=False)
-    except Exception:
-        pass
-    try:
-        # tf.keras standard loader
-        from tensorflow.keras.models import load_model as tf_load_model
-        return tf_load_model(path, compile=False)
-    except Exception as e:
-        logging.warning(f"Segmentation model failed to load with legacy + tf.keras: {e}")
-        return None
-
-
-# ----------------------------- main class -----------------------------
+    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 = {}
-        self.knowledge_base_cache = {}
-        self.config = Config()
-        self.px_per_cm = self.config.PIXELS_PER_CM
-        self._initialize_models()
-
-    def _initialize_models(self):
-        """Initialize all AI models except GPU VLM (that one loads inside the GPU worker)."""
-        try:
-            # HF token
-            if self.config.HF_TOKEN:
-                HfFolder.save_token(self.config.HF_TOKEN)
-                logging.info("✅ HF token set")
-
-            # YOLO (CPU-only in main)
-            try:
-                self.models_cache["det"] = YOLO(self.config.YOLO_MODEL_PATH)
-                logging.info("✅ YOLO loaded (CPU; CUDA masked in main)")
-            except Exception as e:
-                logging.warning(f"YOLO not available: {e}")
-
-            # Segmentation (safe loader, stays on CPU)
-            try:
-                seg = _safe_load_seg_model(self.config.SEG_MODEL_PATH)
-                if seg is None:
-                    raise RuntimeError("segmentation file missing or incompatible")
-                self.models_cache["seg"] = seg
-                logging.info("✅ Segmentation model loaded (CPU)")
-            except Exception as e:
-                self.models_cache["seg"] = None
-                logging.warning(f"Segmentation unavailable: {e}")
-
-            # Wound classifier (CPU)
-            try:
-                self.models_cache["cls"] = pipeline(
-                    "image-classification",
-                    model="Hemg/Wound-classification",
-                    token=self.config.HF_TOKEN,
-                    device="cpu",
-                )
-                logging.info("✅ Classifier loaded (CPU)")
-            except Exception as e:
-                self.models_cache["cls"] = None
-                logging.warning(f"Classifier unavailable: {e}")
-
-            # Embeddings for KB (CPU)
-            try:
-                self.models_cache["embedding_model"] = HuggingFaceEmbeddings(
-                    model_name="sentence-transformers/all-MiniLM-L6-v2",
-                    model_kwargs={"device": "cpu"},
-                )
-                logging.info("✅ Embeddings loaded (CPU)")
-            except Exception as e:
-                self.models_cache["embedding_model"] = None
-                logging.warning(f"Embeddings unavailable: {e}")
-
-            self._load_knowledge_base()
-        except Exception as e:
-            logging.error(f"Error initializing models: {e}")
-
-    def _load_knowledge_base(self):
-        """Load guideline PDFs into a FAISS vector store."""
+        self.models_cache = models_cache
+        self.knowledge_base_cache = knowledge_base_cache
+        self.uploads_dir = UPLOADS_DIR
+        self.dataset_id = DATASET_ID
+        self.hf_token = HF_TOKEN
+
+    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 → crop ROI → segment_wound(ROI) → clean mask →
+        minAreaRect measurement (cm) using EXIF px/cm → save outputs.
+        """
         try:
-            documents = []
-            for pdf_path in self.config.GUIDELINE_PDFS:
-                if os.path.exists(pdf_path):
-                    try:
-                        loader = PyPDFLoader(pdf_path)
-                        docs = loader.load()
-                        documents.extend(docs)
-                        logging.info(f"Loaded PDF: {pdf_path}")
-                    except Exception as e:
-                        logging.warning(f"PDF load failed ({pdf_path}): {e}")
-
-            if documents and self.models_cache.get("embedding_model"):
-                splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-                chunks = splitter.split_documents(documents)
-                vectorstore = FAISS.from_documents(chunks, self.models_cache["embedding_model"])
-                self.knowledge_base_cache["vectorstore"] = vectorstore
-                logging.info(f"✅ Knowledge base ready ({len(chunks)} chunks)")
-            else:
-                self.knowledge_base_cache["vectorstore"] = None
-                logging.warning("Knowledge base not available (no PDFs or embeddings).")
-        except Exception as e:
-            logging.warning(f"Knowledge base loading error: {e}")
-            self.knowledge_base_cache["vectorstore"] = None
+            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.")
 
-    # ------------------------ vision core ------------------------
-    def perform_visual_analysis(self, image_pil):
-        """Perform comprehensive visual analysis of wound image."""
-        try:
             image_cv = cv2.cvtColor(np.array(image_pil.convert("RGB")), cv2.COLOR_RGB2BGR)
 
-            # YOLO detection
-            if "det" not in self.models_cache or self.models_cache["det"] is None:
-                raise ValueError("YOLO detection model not available.")
-            results = self.models_cache["det"].predict(image_cv, verbose=False, device="cpu")
+            # --- Detection ---
+            det_model = self.models_cache.get("det")
+            if det_model is None:
+                raise RuntimeError("YOLO model not loaded")
+            # 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):
-                raise ValueError("No wound detected in the image.")
+                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.xyxy[0].cpu().numpy().astype(int)
+            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)
-            region_cv = image_cv[y1:y2, x1:x2].copy()
+            roi = image_cv[y1:y2, x1:x2].copy()
+            if roi.size == 0:
+                try:
+                    import gradio as gr
+                    raise gr.Error("Detected ROI is empty.")
+                except Exception:
+                    raise RuntimeError("Detected ROI is empty.")
 
-            # Save detection vis
-            os.makedirs(os.path.join(self.config.UPLOADS_DIR, "analysis"), exist_ok=True)
+            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_image_path = os.path.join(self.config.UPLOADS_DIR, "analysis", f"detection_{ts}.png")
-            cv2.imwrite(detection_image_path, det_vis)
-            detection_image_pil = Image.fromarray(cv2.cvtColor(det_vis, cv2.COLOR_BGR2RGB))
-
-            # --- segmentation ---
-            length = breadth = area = 0.0
-            segmentation_image_pil = None
-            segmentation_image_path = None
-
-            mask01 = None
-            seg_model = self.models_cache.get("seg", None)
-            if seg_model is not None:
-                try:
-                    ishape = getattr(seg_model, "input_shape", None)
-                    th, tw = int(ishape[1]), int(ishape[2]) if ishape and len(ishape) >= 3 else (224, 224)
-                    resized = cv2.resize(region_cv, (tw, th), interpolation=cv2.INTER_LINEAR)
-                    x = np.expand_dims(resized.astype(np.float32) / 255.0, 0)
-                    pred = seg_model.predict(x, verbose=0)
-                    if isinstance(pred, (list, tuple)):
-                        pred = pred[0]
-                    p = np.squeeze(pred)
-                    # sigmoid if raw
-                    if p.max() > 1.0 or p.min() < 0.0:
-                        p = 1.0 / (1.0 + np.exp(-p))
-                    p = cv2.resize(p.astype(np.float32), (region_cv.shape[1], region_cv.shape[0]), interpolation=cv2.INTER_LINEAR)
-                    # adaptive threshold
-                    p255 = (np.clip(p, 0, 1) * 255).astype(np.uint8)
-                    thr_val, _ = cv2.threshold(p255, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-                    thr = float(np.clip(thr_val / 255.0, 0.25, 0.65))
-                    seed01 = (p >= thr).astype(np.uint8)
-                    if seed01.sum() == 0:
-                        seed01 = (p >= max(thr - 0.1, 0.15)).astype(np.uint8)
-                    gc01 = _grabcut_refine(region_cv, seed01, iters=3)
-                    mask01 = _clean_mask(gc01)
-                except Exception as e:
-                    logging.warning(f"Segmentation model failed; using OpenCV fallback: {e}")
-                    mask01 = _fallback_segment(region_cv)
-            else:
-                mask01 = _fallback_segment(region_cv)
-
-            # overlay + measurements
-            overlay = region_cv.copy()
-            red = overlay.copy(); red[:] = (0, 0, 255)
-            if mask01 is not None and mask01.any():
-                mask255 = (mask01 * 255).astype(np.uint8)
-                mask3 = cv2.merge([mask255, mask255, mask255])
-                tinted = cv2.addWeighted(region_cv, 0.45, red, 0.55, 0)
-                overlay = np.where(mask3 > 0, tinted, region_cv)
+            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)
-                if cnts:
-                    cnt = max(cnts, 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)
-                    cv2.drawContours(overlay, [cnt], -1, (255, 255, 255), 2)
-
-            segmentation_image_path = os.path.join(self.config.UPLOADS_DIR, "analysis", f"segmentation_{ts}.png")
+                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] = overlay
-            cv2.imwrite(segmentation_image_path, seg_full)
-            segmentation_image_pil = Image.fromarray(cv2.cvtColor(seg_full, cv2.COLOR_BGR2RGB))
+            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)
 
-            # classification
+            # --- Optional classification ---
             wound_type = "Unknown"
-            if self.models_cache.get("cls") is not None:
+            cls_pipe = self.models_cache.get("cls")
+            if cls_pipe is not None:
                 try:
-                    region_pil = Image.fromarray(cv2.cvtColor(region_cv, cv2.COLOR_BGR2RGB))
-                    preds = self.models_cache["cls"](region_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"Wound classification error: {e}")
-
-            conf = 0.0
-            try:
-                conf = float(results[0].boxes.conf[0].cpu().item())
-            except Exception:
-                pass
+                    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": float(length),
-                "breadth_cm": float(breadth),
-                "surface_area_cm2": float(area),
-                "detection_confidence": conf,
-                "bounding_box": [int(x1), int(y1), int(x2), int(y2)],
-                "detection_image_path": detection_image_path,
-                "detection_image_pil": detection_image_pil,
-                "segmentation_image_path": segmentation_image_path,
-                "segmentation_image_pil": segmentation_image_pil,
+                "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": 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 error: {e}", exc_info=True)
-            raise ValueError(f"Visual analysis failed: {str(e)}")
+            logging.error(f"Visual analysis failed: {e}", exc_info=True)
+            raise
 
-    # ------------------------ KB / RAG ------------------------
-    def query_guidelines(self, query: str):
-        """Query the knowledge base for relevant guidelines"""
+    # ---------- Knowledge base + reporting ----------
+    def query_guidelines(self, query: str) -> str:
         try:
-            vector_store = self.knowledge_base_cache.get("vectorstore")
-            if not vector_store:
-                return "Knowledge base unavailable - clinical guidelines not loaded"
-
-            retriever = vector_store.as_retriever(search_kwargs={"k": 10})
-            try:
-                docs = retriever.invoke(query)
-            except Exception:
-                # old API fallback
-                docs = retriever.get_relevant_documents(query)
-
-            if not docs:
-                return "No relevant guidelines found for the query"
-
-            out = []
+            vs = self.knowledge_base_cache.get("vector_store")
+            if not vs:
+                return "Knowledge base is not available."
+            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:
-                meta = d.metadata or {}
-                src = meta.get("source", "Unknown")
-                page = meta.get("page", "N/A")
-                content = (d.page_content or "").strip()
-                out.append(f"Source: {src}, Page: {page}\nContent: {content}")
-            return "\n\n".join(out)
+                src = (d.metadata or {}).get("source", "N/A")
+                txt = (d.page_content or "")[:300]
+                lines.append(f"Source: {src}\nContent: {txt}...")
+            return "\n\n".join(lines) if lines else "No relevant guideline snippets found."
         except Exception as e:
-            logging.error(f"Guidelines query error: {e}")
-            return f"Error querying guidelines: {str(e)}"
-
-    # ------------------------ Reporting (VLM + fallback) ------------------------
-    def generate_final_report(self, patient_info, visual_results, guideline_context, image_pil, max_new_tokens=None):
-        """Generate comprehensive medical report using a VLM if available (loaded inside GPU worker), else fallback."""
-        try:
-            # If medgemma/qwen pipeline wasn't cached by GPU worker, fallback right away.
-            if "medgemma_pipe" not in self.models_cache or self.models_cache["medgemma_pipe"] is None:
-                return self._generate_fallback_report(patient_info, visual_results, guideline_context)
-
-            max_tokens = max_new_tokens or self.config.MAX_NEW_TOKENS
-            detection_path = visual_results.get("detection_image_path", "")
-            segmentation_path = visual_results.get("segmentation_image_path", "")
+            logging.warning(f"Guidelines query failed: {e}")
+            return f"Guidelines query failed: {str(e)}"
 
-            prompt = f"""
-# Wound Care Report
+    def _generate_fallback_report(self, patient_info: str, visual_results: Dict, guideline_context: str) -> str:
+        return f"""# 🩺 SmartHeal AI - Comprehensive Wound Analysis Report
 
-## Patient Information
+## 📋 Patient Information
 {patient_info}
 
-## Visual Analysis Summary
-- Wound Type: {visual_results.get('wound_type', 'Unknown')}
-- Length: {visual_results.get('length_cm', 0)} cm
-- Breadth: {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):.2f}
-
-## Clinical Reference
-{guideline_context}
-
-You are SmartHeal-AI Agent, a world-class wound care AI specialist trained in clinical wound assessment and guideline-based treatment planning.
-Generate a concise, actionable, evidence-based report with: Clinical Summary, Dressing/Medication Recommendations, Key Risk Factors, and Prognosis & Monitoring. Avoid generic advice; tailor to the data above.
-""".strip()
-
-            content_list = [{"type": "text", "text": prompt}]
-            if image_pil:
-                content_list.insert(0, {"type": "image", "image": image_pil})
-            if visual_results.get("detection_image_pil"):
-                content_list.append({"type": "image", "image": visual_results["detection_image_pil"]})
-            if visual_results.get("segmentation_image_pil"):
-                content_list.append({"type": "image", "image": visual_results["segmentation_image_pil"]})
-
-            messages = [
-                {
-                    "role": "system",
-                    "content": [{"type": "text", "text": "You are a medical AI assistant specializing in wound care. Be precise, objective, and recommendation-focused."}],
-                },
-                {
-                    "role": "user",
-                    "content": content_list,
-                },
-            ]
-
-            out = self.models_cache["medgemma_pipe"](
-                text=messages,
-                max_new_tokens=int(max_tokens),
-                do_sample=False,
+## 🔍 Visual Analysis Results
+- **Wound Type**: {visual_results.get('wound_type', 'Unknown')}
+- **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
+- Cleanse wound gently; select dressing per exudate/infection risk
+- 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)
+- Weekly progress review
+
+## 📚 Guideline Context
+{(guideline_context or '')[:800]}{"..." if guideline_context and len(guideline_context) > 800 else ''}
+
+**Disclaimer:** Automated, for decision support only. Verify clinically.
+"""
+
+    def generate_final_report(
+        self,
+        patient_info: str,
+        visual_results: Dict,
+        guideline_context: str,
+        image_pil: Image.Image,
+        max_new_tokens: Optional[int] = None,
+    ) -> str:
+        try:
+            report = generate_medgemma_report(
+                patient_info, visual_results, guideline_context, image_pil, max_new_tokens
             )
-            generated = ""
-            try:
-                generated = (out[0]["generated_text"][-1].get("content", "") or "").strip()
-            except Exception:
-                generated = (out[0].get("generated_text", "") or "").strip()
-
-            if generated:
-                images_sec = f"\n\n## Analysis Images\n- Detection: {detection_path}\n- Segmentation: {segmentation_path}\n"
-                return images_sec + generated
-
+            if report and report.strip() and not report.startswith(("⚠️", "❌")):
+                return report
+            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"VLM report generation error: {e}")
+            logging.error(f"Report generation failed: {e}")
             return self._generate_fallback_report(patient_info, visual_results, guideline_context)
 
-    def _generate_fallback_report(self, patient_info, visual_results, guideline_context):
-        detection_path = visual_results.get("detection_image_path", "Not available")
-        segmentation_path = visual_results.get("segmentation_image_path", "Not available")
-        return f"""
-# Wound Analysis Report
-## Patient Information
-{patient_info}
-
-## Visual Analysis Results
-- **Wound Type**: {visual_results.get('wound_type', 'Unknown')}
-- **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):.2f}
-
-## Analysis Images
-- **Detection Image**: {detection_path}
-- **Segmentation Image**: {segmentation_path}
-
-## Assessment
-Automated measurements provided. Verify via clinical exam.
-
-## Recommendations
-- Cleanse wound; choose dressing per moisture/infection risk
-- Consider debridement if indicated
-- Document with serial photos & measurements
-
-## Clinical Guidelines
-{(guideline_context or '')[:500]}...
-
-*Note: Decision support only; not a diagnosis.*
-""".strip()
-
-    # ------------------------ I/O ------------------------
-    def save_and_commit_image(self, image_pil):
-        """Save image locally and optionally upload to HuggingFace dataset"""
+    def save_and_commit_image(self, image_pil: Image.Image) -> str:
         try:
-            os.makedirs(self.config.UPLOADS_DIR, exist_ok=True)
-            filename = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}.png"
-            local_path = os.path.join(self.config.UPLOADS_DIR, filename)
-            image_pil.convert("RGB").save(local_path)
-            logging.info(f"✅ Image saved locally: {local_path}")
+            os.makedirs(self.uploads_dir, exist_ok=True)
+            ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+            filename = f"{ts}.png"
+            path = os.path.join(self.uploads_dir, filename)
+            image_pil.convert("RGB").save(path)
+            logging.info(f"✅ Image saved locally: {path}")
 
-            if self.config.HF_TOKEN and self.config.DATASET_ID:
+            if HF_TOKEN and DATASET_ID:
                 try:
-                    HfFolder.save_token(self.config.HF_TOKEN)
+                    HfApi, HfFolder = _import_hf_hub()
+                    HfFolder.save_token(HF_TOKEN)
                     api = HfApi()
                     api.upload_file(
-                        path_or_fileobj=local_path,
+                        path_or_fileobj=path,
                         path_in_repo=f"images/{filename}",
-                        repo_id=self.config.DATASET_ID,
+                        repo_id=DATASET_ID,
                         repo_type="dataset",
+                        token=HF_TOKEN,
                         commit_message=f"Upload wound image: {filename}",
                     )
                     logging.info("✅ Image committed to HF dataset")
                 except Exception as e:
                     logging.warning(f"HF upload failed: {e}")
 
-            return local_path
+            return path
         except Exception as e:
-            logging.error(f"Image saving error: {e}")
-            return None
+            logging.error(f"Failed to save/commit image: {e}")
+            return ""
 
-    # ------------------------ Pipeline (GPU-safe) ------------------------
-    # decoration is evaluated at import; only the selected branch executes
-    @spaces.GPU(enable_queue=True, duration=120) if SPACES_AVAILABLE else (lambda f: f)
-    def full_analysis_pipeline(self, image, questionnaire_data):
-        """Complete analysis pipeline. VLM loads here (inside GPU worker) to avoid main-process CUDA init."""
+    def full_analysis_pipeline(self, image_pil: Image.Image, questionnaire_data: Dict) -> Dict:
         try:
-            # Try to build a VLM inside the worker. If ZeroGPU fails, we fallback to CPU.
-            if "medgemma_pipe" not in self.models_cache or self.models_cache["medgemma_pipe"] is None:
-                vlm_loaded = False
-                # Prefer a small VLM (Qwen2-VL 2B) for Spaces; if it fails on GPU, retry on CPU.
-                try:
-                    self.models_cache["medgemma_pipe"] = pipeline(
-                        "image-text-to-text",
-                        model=os.environ.get("SMARTHEAL_VLM", "Qwen/Qwen2-VL-2B-Instruct"),
-                        token=self.config.HF_TOKEN,
-                        device_map="cuda",  # we're inside ZeroGPU worker now
-                        torch_dtype="auto",
-                        max_new_tokens=self.config.MAX_NEW_TOKENS,
-                        trust_remote_code=True,
-                    )
-                    vlm_loaded = True
-                    logging.info("✅ VLM loaded on GPU worker")
-                except Exception as e:
-                    logging.warning(f"GPU VLM failed; falling back to CPU: {e}")
-                    try:
-                        self.models_cache["medgemma_pipe"] = pipeline(
-                            "image-text-to-text",
-                            model=os.environ.get("SMARTHEAL_VLM", "Qwen/Qwen2-VL-2B-Instruct"),
-                            token=self.config.HF_TOKEN,
-                            device_map="cpu",
-                            torch_dtype="auto",
-                            max_new_tokens=self.config.MAX_NEW_TOKENS,
-                            trust_remote_code=True,
-                        )
-                        vlm_loaded = True
-                        logging.info("✅ VLM loaded on CPU")
-                    except Exception as e2:
-                        self.models_cache["medgemma_pipe"] = None
-                        logging.error(f"❌ Could not load any VLM: {e2}")
-
-            # Save image
-            saved_path = self.save_and_commit_image(image)
-
-            # Visual analysis
-            visual_results = self.perform_visual_analysis(image)
-
-            # Patient info string
+            saved_path = self.save_and_commit_image(image_pil)
+            visual_results = self.perform_visual_analysis(image_pil)
+
             pi = questionnaire_data or {}
-            patient_info = ", ".join([f"{k}: {v}" for k, v in pi.items() if str(v).strip() != ""])
-
-            # KB query
-            wound_type = visual_results.get("wound_type", "wound")
-            moisture = pi.get("moisture", "unknown")
-            infection = pi.get("infection", "unknown")
-            diabetic = pi.get("diabetic", "unknown")
-            query = f"best practices for managing a {wound_type} with moisture level '{moisture}' and signs of infection '{infection}' in a patient who is diabetic '{diabetic}'"
+            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')}"
+            )
+
+            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')}' "
+                f"in a diabetic status '{pi.get('diabetic','unknown')}'"
+            )
             guideline_context = self.query_guidelines(query)
 
-            # Report
-            final_report = self.generate_final_report(patient_info, visual_results, guideline_context, image)
+            report = self.generate_final_report(patient_info, visual_results, guideline_context, image_pil)
 
             return {
                 "success": True,
                 "visual_analysis": visual_results,
-                "report": final_report,
+                "report": report,
                 "saved_image_path": saved_path,
-                "timestamp": datetime.now().isoformat(),
+                "guideline_context": (guideline_context or "")[:500] + (
+                    "..." if guideline_context and len(guideline_context) > 500 else ""
+                ),
             }
-
         except Exception as e:
-            logging.error(f"Full analysis pipeline error: {e}", exc_info=True)
+            logging.error(f"Pipeline error: {e}")
             return {
                 "success": False,
                 "error": str(e),
-                "timestamp": datetime.now().isoformat(),
+                "visual_analysis": {},
+                "report": f"Analysis failed: {str(e)}",
+                "saved_image_path": None,
+                "guideline_context": "",
             }
 
-    # ------------------------ Legacy API ------------------------
-    def analyze_wound(self, image, questionnaire_data):
-        """Legacy method for backward compatibility"""
+    def analyze_wound(self, image, questionnaire_data: Dict) -> Dict:
         try:
             if isinstance(image, str):
-                try:
-                    image = Image.open(image)
-                    logging.info("Converted path to PIL Image")
-                except Exception as e:
-                    logging.error(f"Error opening image: {e}")
-            if not isinstance(image, Image.Image):
-                # file-like?
-                if hasattr(image, "read"):
-                    try:
-                        if hasattr(image, "seek"):
-                            image.seek(0)
-                        image = Image.open(image)
-                    except Exception as e:
-                        logging.error(f"Error reading file-like image: {e}")
-                        raise ValueError(f"Invalid image format: {type(image)}")
-
-            result = self.full_analysis_pipeline(image, questionnaire_data)
-
-            if result.get("success"):
-                return {
-                    "timestamp": result["timestamp"],
-                    "summary": f"Analysis completed for {questionnaire_data.get('patient_name', 'patient')}",
-                    "recommendations": result["report"],
-                    "wound_detection": {
-                        "status": "success",
-                        "detections": [result["visual_analysis"]],
-                        "total_wounds": 1,
-                    },
-                    "segmentation_result": {
-                        "status": "success",
-                        "wound_area_percentage": result["visual_analysis"].get("surface_area_cm2", 0),
-                    },
-                    "risk_assessment": self._assess_risk_legacy(questionnaire_data),
-                    "guideline_recommendations": [result["report"][:200] + "..."],
-                }
+                if not os.path.exists(image):
+                    raise ValueError(f"Image file not found: {image}")
+                image_pil = Image.open(image)
+            elif isinstance(image, Image.Image):
+                image_pil = image
+            elif isinstance(image, np.ndarray):
+                image_pil = Image.fromarray(image)
             else:
-                return {
-                    "timestamp": result["timestamp"],
-                    "summary": f"Analysis failed: {result.get('error','unknown')}",
-                    "recommendations": "Please consult with a healthcare professional.",
-                    "wound_detection": {"status": "error", "message": result.get("error", "")},
-                    "segmentation_result": {"status": "error", "message": result.get("error", "")},
-                    "risk_assessment": {"risk_score": 0, "risk_level": "Unknown", "risk_factors": []},
-                    "guideline_recommendations": ["Analysis unavailable due to error"],
-                }
+                raise ValueError(f"Unsupported image type: {type(image)}")
 
+            return self.full_analysis_pipeline(image_pil, questionnaire_data or {})
         except Exception as e:
-            logging.error(f"Legacy analyze_wound error: {e}")
+            logging.error(f"Wound analysis error: {e}")
             return {
-                "timestamp": datetime.now().isoformat(),
-                "summary": f"Analysis error: {str(e)}",
-                "recommendations": "Please consult with a healthcare professional.",
-                "wound_detection": {"status": "error", "message": str(e)},
-                "segmentation_result": {"status": "error", "message": str(e)},
-                "risk_assessment": {"risk_score": 0, "risk_level": "Unknown", "risk_factors": []},
-                "guideline_recommendations": ["Analysis unavailable due to error"],
-            }
-
-    def _assess_risk_legacy(self, questionnaire_data):
-        """Legacy risk assessment for backward compatibility"""
-        risk_factors = []
-        risk_score = 0
-        try:
-            age = int(questionnaire_data.get("patient_age", 0) or 0)
-            if age > 65:
-                risk_factors.append("Advanced age (>65)")
-                risk_score += 2
-            elif age > 50:
-                risk_factors.append("Older adult (50-65)")
-                risk_score += 1
-
-            duration = str(questionnaire_data.get("wound_duration", "")).lower()
-            if any(t in duration for t in ["month", "months", "year"]):
-                risk_factors.append("Chronic wound (>4 weeks)")
-                risk_score += 3
-
-            pain_level = int(questionnaire_data.get("pain_level", 0) or 0)
-            if pain_level >= 7:
-                risk_factors.append("High pain level")
-                risk_score += 2
-
-            medical_history = str(questionnaire_data.get("medical_history", "")).lower()
-            if "diabetes" in medical_history:
-                risk_factors.append("Diabetes mellitus")
-                risk_score += 3
-            if "circulation" in medical_history or "vascular" in medical_history:
-                risk_factors.append("Vascular/circulation issues")
-                risk_score += 2
-            if "immune" in medical_history:
-                risk_factors.append("Immune system compromise")
-                risk_score += 2
-
-            risk_level = "High" if risk_score >= 7 else ("Moderate" if risk_score >= 4 else "Low")
-            return {"risk_score": risk_score, "risk_level": risk_level, "risk_factors": risk_factors}
-        except Exception as e:
-            logging.error(f"Risk assessment error: {e}")
-            return {"risk_score": 0, "risk_level": "Unknown", "risk_factors": []}
+                "success": False,
+                "error": str(e),
+                "visual_analysis": {},
+                "report": f"Analysis initialization failed: {str(e)}",
+                "saved_image_path": None,
+                "guideline_context": "",
+            }