Deploy SheGuard - Maternal Risk Assessment with Mamba3 SSM

.gitignore

@@ -0,0 +1,28 @@
+# Personal notes (not for deployment)
+NOTES.md
+post.md
+
+# Python
+__pycache__/
+*.py[cod]
+*.pyo
+*.egg-info/
+dist/
+build/
+*.egg
+
+# Virtual environments
+.venv/
+venv/
+env/
+
+# IDE / Editor
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS files
+.DS_Store
+Thumbs.db

Dockerfile

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+FROM python:3.11-slim
+
+# Install Tesseract OCR + system deps
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        tesseract-ocr \
+        libgl1-mesa-glx \
+        libglib2.0-0 && \
+    rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Install Python deps first (layer caching)
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy project files
+COPY . .
+
+# Train the model at build time (generates weights)
+RUN python -m src.train
+
+# HF Spaces expects port 7860
+EXPOSE 7860
+
+CMD ["uvicorn", "api.main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,11 +1,46 @@
 ---
 title: SheGuard
-emoji: 🐢
-colorFrom: indigo
-colorTo: green
+emoji: 🤱
+colorFrom: pink
+colorTo: purple
 sdk: docker
 pinned: false
-short_description: prevention is better than cure -- the motto are model follow
+short_description: Maternal mortality early warning using Mamba3 SSM AI
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# SheGuard — Maternal Risk Assessment
+
+AI-powered maternal mortality early warning system, built with **Mamba3 Sequential State-Space Models** and **WHO clinical safety rules**.
+
+## Features
+
+- **Mamba3 SSM model** — Temporal sequence analysis of prenatal vital signs
+- **3-tier alert system** — GREEN / AMBER / RED risk classification
+- **WHO clinical safety net** — Hard rule overrides for obvious danger signs
+- **OCR auto-fill** — Photograph a paper prenatal record card to auto-populate visit data
+- **Resource-aware routing** — Generates transfer orders when clinic lacks blood supply or staff
+- **Explainable AI** — Shows top contributing features for each prediction
+
+## How to Use
+
+1. Enter patient vitals from prenatal visits (or upload a photo of the record card)
+2. Click **"Assess maternal risk"**
+3. View the risk level, contributing factors, and recommended clinical actions
+
+## Architecture
+
+```
+Patient vitals → Mamba3 SSM (5-visit sequence) → Risk prediction
+                                                    ↓
+                                WHO Clinical Safety Rules
+                                                    ↓
+                                    GREEN / AMBER / RED alert
+```
+
+## Tech Stack
+
+- **Model:** PyTorch, Mamba3 SSM (Trapezoidal discretization + Complex state + MIMO)
+- **API:** FastAPI + Uvicorn
+- **Frontend:** HTML/CSS/JS dashboard
+- **OCR:** OpenCV + Pytesseract
+- **Dataset:** UCI Maternal Health Risk (1,014 samples)

api/alert_logic.py

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+"""
+MamaGuard — Alert Logic
+Three-tier alerting with suppression, clinical safety net, and resource-aware routing.
+"""
+
+from datetime import datetime, timedelta
+from typing import Optional, Dict
+from api.schemas import AlertTier
+
+# ─── Thresholds ────────────────────────────────────────────────────────────────
+RED_THRESHOLD   = 0.85
+AMBER_THRESHOLD = 0.65
+RED_CONSECUTIVE_VISITS = 2
+SUPPRESSION_HOURS = 48
+SUPPRESSION_DELTA = 0.15
+
+# In-memory alert store (use Redis/DB in production)
+_alert_history: Dict[str, dict] = {}
+
+
+# ─── Clinical rule safety net ─────────────────────────────────────────────────
+
+def apply_clinical_safety_net(visits_raw: list) -> tuple[str | None, str | None]:
+    """
+    Checks raw visit values against WHO clinical thresholds.
+    Returns (forced_tier, reason) if a rule fires, else (None, None).
+    """
+    max_systolic  = max((v.get("systolic_bp")  or 0) for v in visits_raw)
+    max_diastolic = max((v.get("diastolic_bp") or 0) for v in visits_raw)
+    max_bs        = max((v.get("blood_sugar")  or 0) for v in visits_raw)
+
+    latest     = visits_raw[-1]
+    latest_sys = latest.get("systolic_bp") or 0
+    latest_dia = latest.get("diastolic_bp") or 0
+
+    # ── RED RULES ──────────────────────────────────────────────────────────────
+
+    # Rule 1: Severe hypertension → RED
+    if max_systolic >= 160 or max_diastolic >= 110:
+        return "RED", (
+            f"SystolicBP {max_systolic} mmHg meets WHO severe "
+            f"hypertension threshold (≥160) — emergency referral required"
+        )
+
+    # Rule 5: Multi-vital simultaneous escalation → RED
+    if len(visits_raw) >= 3:
+        first = visits_raw[0]
+        last  = visits_raw[-1]
+
+        sys_rise  = (last.get("systolic_bp")  or 0) - (first.get("systolic_bp")  or 0)
+        dia_rise  = (last.get("diastolic_bp") or 0) - (first.get("diastolic_bp") or 0)
+        bs_rise   = (last.get("blood_sugar")  or 0) - (first.get("blood_sugar")  or 0)
+        hr_rise   = (last.get("heart_rate")   or 0) - (first.get("heart_rate")   or 0)
+        temp_rise = (last.get("body_temp")    or 0) - (first.get("body_temp")    or 0)
+
+        escalating_count = 0
+        if sys_rise  >= 15:  escalating_count += 1
+        if dia_rise  >= 10:  escalating_count += 1
+        if bs_rise   >= 3.0: escalating_count += 1
+        if hr_rise   >= 15:  escalating_count += 1
+        if temp_rise >= 0.5: escalating_count += 1
+
+        if escalating_count >= 3:
+            return "RED", (
+                f"{escalating_count} vitals escalating simultaneously "
+                f"(BP +{sys_rise:.0f} mmHg, HR +{hr_rise:.0f} bpm, "
+                f"BS +{bs_rise:.1f} mmol/L) — combined deterioration "
+                f"pattern, refer immediately"
+            )
+
+    # ── AMBER RULES ────────────────────────────────────────────────────────────
+
+    # Rule 2: Hypertension in pregnancy → AMBER
+    if latest_sys >= 140 or latest_dia >= 90:
+        return "AMBER", (
+            f"SystolicBP {latest_sys} mmHg meets WHO hypertension "
+            f"in pregnancy threshold (≥140)"
+        )
+
+    # Rule 3: Severe hyperglycaemia → AMBER
+    if max_bs > 11.1:
+        return "AMBER", (
+            f"Blood sugar {max_bs} mmol/L exceeds gestational "
+            f"diabetes threshold (>11.1)"
+        )
+
+    # Rule 4: BP escalation pattern → AMBER
+    if len(visits_raw) >= 2:
+        first_sys = visits_raw[0].get("systolic_bp") or 0
+        bp_rise   = latest_sys - first_sys
+        if bp_rise >= 20:
+            return "AMBER", (
+                f"SystolicBP rose {bp_rise:.0f} mmHg across visits "
+                f"— escalation pattern detected"
+            )
+
+    return None, None
+
+
+def compute_alert_tier(
+    probabilities: dict,
+    patient_id: str,
+    visit_importances: list,
+) -> tuple[AlertTier, bool]:
+    """
+    Computes the alert tier for a patient.
+    Returns: (alert_tier, is_suppressed)
+    """
+    high_risk_prob = probabilities.get("High risk", 0.0)
+
+    # Check suppression
+    if patient_id in _alert_history:
+        prev = _alert_history[patient_id]
+        time_since = datetime.now() - prev["last_alerted"]
+        score_delta = high_risk_prob - prev["last_score"]
+
+        if time_since < timedelta(hours=SUPPRESSION_HOURS):
+            if score_delta < SUPPRESSION_DELTA:
+                return prev["tier"], True
+
+    # Determine tier
+    if high_risk_prob >= RED_THRESHOLD:
+        recent_weight = sum(visit_importances[-RED_CONSECUTIVE_VISITS:])
+        if recent_weight > 0.4:
+            tier = AlertTier.RED
+        else:
+            tier = AlertTier.AMBER
+    elif high_risk_prob >= AMBER_THRESHOLD:
+        tier = AlertTier.AMBER
+    else:
+        tier = AlertTier.GREEN
+
+    # Store in history
+    _alert_history[patient_id] = {
+        "last_alerted": datetime.now(),
+        "last_score": high_risk_prob,
+        "tier": tier,
+    }
+
+    return tier, False
+
+
+def generate_action_text(
+    tier: AlertTier,
+    top_reasons: list,
+    data_quality: float,
+    staff_available: Optional[int],
+    blood_units: Optional[int],
+) -> tuple[str, Optional[str]]:
+    """Generates actionable text for the health worker and optional transfer order."""
+
+    transfer_order = None
+    reasons_text = "; ".join(top_reasons) if top_reasons else "multiple elevated indicators"
+
+    if tier == AlertTier.GREEN:
+        action = "Continue standard prenatal care schedule. No immediate action required."
+
+    elif tier == AlertTier.AMBER:
+        action = (
+            f"AMBER ALERT: Elevated risk detected ({reasons_text}). "
+            f"Schedule additional checkup within 72 hours. "
+            f"Increase monitoring frequency to weekly."
+        )
+        if data_quality < 0.7:
+            action += " NOTE: Data quality is low — ensure all readings are recorded at next visit."
+
+    else:  # RED
+        action = (
+            f"🔴 RED ALERT: High maternal risk detected ({reasons_text}). "
+            f"Refer patient to hospital immediately for full assessment."
+        )
+
+        # Resource-aware routing
+        resources_ok = True
+        resource_warnings = []
+
+        if staff_available is not None and staff_available == 0:
+            resources_ok = False
+            resource_warnings.append("no doctors currently on duty")
+        if blood_units is not None and blood_units < 2:
+            resources_ok = False
+            resource_warnings.append("insufficient blood supply (<2 units)")
+
+        if not resources_ok:
+            issues = " and ".join(resource_warnings)
+            transfer_order = (
+                f"TRANSFER ORDER: This clinic has {issues}. "
+                f"Patient must be transferred to nearest equipped facility. "
+                f"Call referral line: 1800-MAMA-REF. "
+                f"Document: Patient at high risk of {reasons_text}."
+            )
+            action += f" ⚠️ Resources insufficient — transfer order generated."
+
+        if data_quality < 0.7:
+            action += " NOTE: Prediction confidence is reduced due to missing readings."
+
+    return action, transfer_order

api/extract_report.py

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+# api/extract_report.py
+"""
+MamaGuard — Report Extraction (OCR)
+Ensemble preprocessing × ensemble OCR for extracting prenatal data from images.
+Handles screenshots, paper photos, scans, low-light, rotated, colored forms, etc.
+"""
+
+import base64
+import io
+import re
+import pytesseract
+from PIL import Image
+import cv2
+import numpy as np
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from typing import Optional, List, Tuple
+
+router = APIRouter()
+
+# Tesseract path (Windows)
+pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
+
+
+# ── Schemas ───────────────────────────────────────────────────────────────────
+
+class ExtractRequest(BaseModel):
+    image_base64: str
+    image_type:   str = "image/jpeg"
+
+class ExtractedVisit(BaseModel):
+    age:          Optional[float] = None
+    systolic_bp:  Optional[float] = None
+    diastolic_bp: Optional[float] = None
+    blood_sugar:  Optional[float] = None
+    body_temp:    Optional[float] = None
+    heart_rate:   Optional[float] = None
+    visit_date:   Optional[str]   = None
+
+class ExtractResponse(BaseModel):
+    visits:     List[ExtractedVisit]
+    patient_id: Optional[str]  = None
+    notes:      Optional[str]  = None
+    confidence: float          = 1.0
+    raw_text:   Optional[str]  = None
+
+
+# ── Medical ranges (values outside = OCR errors, discard) ─────────────────────
+RANGES = {
+    "age":          (10,   60),
+    "systolic_bp":  (70,  200),
+    "diastolic_bp": (40,  130),
+    "blood_sugar":  (3.0, 20.0),
+    "body_temp":    (35.0, 42.0),
+    "heart_rate":   (40,  160),
+}
+
+# Field label aliases
+ROW_ALIASES = {
+    "age":          ["age", "years", "yr", "patient age", "age (yrs)"],
+    "systolic_bp":  ["systolic", "sbp", "sys", "systolic bp", "bp sys",
+                     "upper bp", "s.b.p", "s bp", "syst"],
+    "diastolic_bp": ["diastolic", "dbp", "dia", "diastolic bp", "bp dia",
+                     "lower bp", "d.b.p", "d bp", "diast"],
+    "blood_sugar":  ["blood sugar", "bs", "glucose", "bg", "blood glucose",
+                     "sugar", "b.s", "rbs", "fbs", "ppbs", "glu"],
+    "body_temp":    ["temp", "temperature", "body temp", "tmp", "fever",
+                     "body temperature", "b.temp", "t (c)", "t(c)"],
+    "heart_rate":   ["heart rate", "hr", "pulse", "bpm", "heartrate",
+                     "heart", "p/r", "pr", "pulse rate"],
+}
+
+# Lines containing these words are skipped during parsing
+SKIP_KEYWORDS = [
+    "field", "visit", "v1", "v2", "v3", "v 1", "v 2", "v 3",
+    "column", "header", "parameter", "reading", "measurement",
+    "no.", "sl.", "s.no", "item"
+]
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# IMAGE PREPROCESSING PIPELINES
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def to_gray(pil_image: Image.Image) -> np.ndarray:
+    """Convert PIL image to OpenCV grayscale array."""
+    return cv2.cvtColor(np.array(pil_image.convert("RGB")), cv2.COLOR_RGB2GRAY)
+
+
+def smart_upscale(gray: np.ndarray, target_width: int = 1400) -> np.ndarray:
+    """Upscale small images so Tesseract can read text reliably."""
+    h, w = gray.shape
+    if w < target_width:
+        scale = target_width / w
+        gray  = cv2.resize(gray, None, fx=scale, fy=scale,
+                           interpolation=cv2.INTER_CUBIC)
+    return gray
+
+
+def detect_and_invert_if_dark(gray: np.ndarray) -> np.ndarray:
+    """Invert dark-background images for Tesseract (trained on black-on-white)."""
+    if np.mean(gray) < 127:
+        return cv2.bitwise_not(gray)
+    return gray
+
+
+def remove_shadow(gray: np.ndarray) -> np.ndarray:
+    """Remove uneven lighting via background subtraction."""
+    dilated = cv2.dilate(gray, np.ones((7, 7), np.uint8))
+    bg      = cv2.medianBlur(dilated, 21)
+    diff    = 255 - cv2.absdiff(gray, bg)
+    norm    = cv2.normalize(diff, None, 0, 255, cv2.NORM_MINMAX)
+    return norm.astype(np.uint8)
+
+
+def enhance_contrast_clahe(gray: np.ndarray) -> np.ndarray:
+    """Apply CLAHE for adaptive local contrast enhancement."""
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    return clahe.apply(gray)
+
+
+def deskew(gray: np.ndarray) -> np.ndarray:
+    """Correct image rotation using minimum-area bounding rectangle."""
+    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    coords    = np.column_stack(np.where(binary > 0))
+    if len(coords) < 100:
+        return gray
+    angle = cv2.minAreaRect(coords)[-1]
+    if angle < -45:
+        angle = 90 + angle
+    if abs(angle) > 15:
+        return gray
+    (h, w) = gray.shape
+    M      = cv2.getRotationMatrix2D((w // 2, h // 2), angle, 1.0)
+    return cv2.warpAffine(gray, M, (w, h),
+                           flags=cv2.INTER_CUBIC,
+                           borderMode=cv2.BORDER_REPLICATE)
+
+
+def remove_ruled_lines(gray: np.ndarray) -> np.ndarray:
+    """Remove horizontal ruled lines that confuse text segmentation."""
+    kernel  = cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1))
+    lines   = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel, iterations=2)
+    return cv2.subtract(gray, lines)
+
+
+# ── 8 Preprocessing pipelines ─────────────────────────────────────────────────
+
+def pipeline_standard(gray: np.ndarray) -> np.ndarray:
+    """Pipeline A: Clean printed document, white paper, good lighting."""
+    gray = smart_upscale(gray)
+    gray = detect_and_invert_if_dark(gray)
+    gray = cv2.fastNlMeansDenoising(gray, h=10)
+    return cv2.adaptiveThreshold(gray, 255,
+        cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 10)
+
+
+def pipeline_shadow(gray: np.ndarray) -> np.ndarray:
+    """Pipeline B: Paper photo with shadow or uneven lighting."""
+    gray = smart_upscale(gray)
+    gray = detect_and_invert_if_dark(gray)
+    gray = remove_shadow(gray)
+    gray = cv2.fastNlMeansDenoising(gray, h=8)
+    return cv2.adaptiveThreshold(gray, 255,
+        cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 41, 12)
+
+
+def pipeline_low_contrast(gray: np.ndarray) -> np.ndarray:
+    """Pipeline C: Faded ink, old paper, poor scan quality."""
+    gray = smart_upscale(gray)
+    gray = detect_and_invert_if_dark(gray)
+    gray = enhance_contrast_clahe(gray)
+    gray = cv2.fastNlMeansDenoising(gray, h=12)
+    blurred = cv2.GaussianBlur(gray, (0, 0), 3)
+    gray    = cv2.addWeighted(gray, 1.5, blurred, -0.5, 0)
+    return cv2.adaptiveThreshold(gray, 255,
+        cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 21, 8)
+
+
+def pipeline_deskewed(gray: np.ndarray) -> np.ndarray:
+    """Pipeline D: Rotated or skewed image."""
+    gray = smart_upscale(gray)
+    gray = detect_and_invert_if_dark(gray)
+    gray = deskew(gray)
+    gray = cv2.fastNlMeansDenoising(gray, h=10)
+    return cv2.adaptiveThreshold(gray, 255,
+        cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 10)
+
+
+def pipeline_ruled_paper(gray: np.ndarray) -> np.ndarray:
+    """Pipeline E: Handwritten on ruled/lined paper."""
+    gray = smart_upscale(gray)
+    gray = detect_and_invert_if_dark(gray)
+    gray = remove_shadow(gray)
+    gray = remove_ruled_lines(gray)
+    gray = enhance_contrast_clahe(gray)
+    gray = cv2.fastNlMeansDenoising(gray, h=15)
+    return cv2.adaptiveThreshold(gray, 255,
+        cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 25, 8)
+
+
+def pipeline_high_noise(gray: np.ndarray) -> np.ndarray:
+    """Pipeline F: Low-light photo, dim room, heavily compressed JPEG."""
+    gray = smart_upscale(gray, target_width=1800)
+    gray = detect_and_invert_if_dark(gray)
+    gray = cv2.fastNlMeansDenoising(gray, h=20,
+                                     templateWindowSize=7, searchWindowSize=21)
+    gray = enhance_contrast_clahe(gray)
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
+    gray   = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel)
+    _, out = cv2.threshold(gray, 0, 255,
+                            cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    return out
+
+
+def pipeline_screenshot(gray: np.ndarray) -> np.ndarray:
+    """Pipeline G: Screenshot (pixel-perfect, minimal preprocessing needed)."""
+    gray = smart_upscale(gray)
+    gray = detect_and_invert_if_dark(gray)
+    _, out = cv2.threshold(gray, 0, 255,
+                            cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    return out
+
+
+def pipeline_color_form(pil_image: Image.Image) -> np.ndarray:
+    """Pipeline H: Colored background — uses LAB color space for contrast."""
+    img_bgr = cv2.cvtColor(np.array(pil_image.convert("RGB")), cv2.COLOR_RGB2BGR)
+    lab     = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LAB)
+    l_chan  = lab[:, :, 0]
+    l_chan  = smart_upscale(l_chan)
+    l_chan  = detect_and_invert_if_dark(l_chan)
+    l_chan  = enhance_contrast_clahe(l_chan)
+    l_chan  = cv2.fastNlMeansDenoising(l_chan, h=10)
+    return cv2.adaptiveThreshold(l_chan, 255,
+        cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 10)
+
+
+PIPELINE_FNS = {
+    "standard":     pipeline_standard,
+    "shadow":       pipeline_shadow,
+    "low_contrast": pipeline_low_contrast,
+    "deskewed":     pipeline_deskewed,
+    "ruled_paper":  pipeline_ruled_paper,
+    "high_noise":   pipeline_high_noise,
+    "screenshot":   pipeline_screenshot,
+}
+
+PIPELINE_ORDERS = {
+    "screenshot":   ["screenshot", "standard", "low_contrast"],
+    "dark_bg":      ["screenshot", "standard", "shadow"],
+    "low_contrast": ["low_contrast", "shadow", "standard", "high_noise"],
+    "colored_form": ["color_form", "low_contrast", "standard"],
+    "camera_photo": ["shadow", "deskewed", "ruled_paper", "standard"],
+    "standard":     ["standard", "shadow", "low_contrast", "deskewed"],
+}
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# IMAGE TYPE DETECTOR
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def detect_image_type(gray: np.ndarray, pil_image: Image.Image) -> str:
+    """Heuristically classify the image to select optimal pipeline order."""
+    mean_b = float(np.mean(gray))
+    std_b  = float(np.std(gray))
+    h, w   = gray.shape
+
+    if std_b > 75 and (mean_b < 70 or mean_b > 185):
+        return "screenshot"
+
+    if mean_b < 85:
+        return "dark_bg"
+
+    if std_b < 35:
+        return "low_contrast"
+
+    img_hsv  = cv2.cvtColor(np.array(pil_image.convert("RGB")), cv2.COLOR_RGB2HSV)
+    mean_sat = float(np.mean(img_hsv[:, :, 1]))
+    if mean_sat > 40:
+        return "colored_form"
+
+    if w > 2000 or h > 2000:
+        return "camera_photo"
+
+    return "standard"
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# OCR ENGINE
+# ═══════════════════════════════════════════════════════════════════════════════
+
+OCR_CONFIGS = [
+    "--psm 6 --oem 3",
+    "--psm 4 --oem 3",
+    "--psm 11 --oem 3",
+    "--psm 3 --oem 3",
+]
+
+
+def run_ocr_best(processed_img: np.ndarray) -> str:
+    """Run Tesseract with all PSM configs and return the output with most digits."""
+    pil         = Image.fromarray(processed_img)
+    best_text   = ""
+    best_digits = 0
+    for config in OCR_CONFIGS:
+        try:
+            text   = pytesseract.image_to_string(pil, config=config)
+            digits = sum(c.isdigit() for c in text)
+            if digits > best_digits:
+                best_digits = digits
+                best_text   = text
+        except Exception:
+            continue
+    return best_text
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# PARSERS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def find_numbers(text: str) -> List[float]:
+    """Extract all integers and decimals from a string."""
+    return [float(m) for m in re.findall(r'\d+\.?\d*', text)]
+
+
+def match_label(line: str) -> Optional[str]:
+    """Check if a line contains a known field name alias. Returns field key or None."""
+    ll = line.lower()
+    for field, aliases in ROW_ALIASES.items():
+        for alias in aliases:
+            if alias in ll:
+                return field
+    return None
+
+
+def is_skip_line(line: str) -> bool:
+    """Returns True if this line is a header or label-only row to skip."""
+    ll = line.lower()
+    return any(kw in ll for kw in SKIP_KEYWORDS)
+
+
+def parse_table_format(raw_text: str) -> List[dict]:
+    """Parser A — Row-per-field table layout."""
+    lines      = [l.strip() for l in raw_text.split('\n') if l.strip()]
+    field_vals = {}
+    max_visits = 0
+
+    for line in lines:
+        if is_skip_line(line):
+            continue
+        field = match_label(line)
+        if field is None:
+            continue
+        numbers = find_numbers(line)
+        lo, hi  = RANGES[field]
+        valid   = [n for n in numbers if lo <= n <= hi]
+        if valid:
+            field_vals[field] = valid
+            max_visits = max(max_visits, len(valid))
+
+    if not field_vals:
+        return []
+
+    return [
+        {field: (values[i] if i < len(values) else None)
+         for field, values in field_vals.items()}
+        for i in range(max_visits)
+    ]
+
+
+def parse_column_format(raw_text: str) -> List[dict]:
+    """Parser B — Column-per-visit layout with header row."""
+    lines         = [l.strip() for l in raw_text.split('\n') if l.strip()]
+    header_idx    = -1
+    header_fields = []
+
+    for i, line in enumerate(lines):
+        parts = re.split(r'\s{2,}|\t', line)
+        known = [match_label(p) for p in parts]
+        if sum(f is not None for f in known) >= 3:
+            header_idx    = i
+            header_fields = known
+            break
+
+    if header_idx == -1:
+        return []
+
+    visits = []
+    for line in lines[header_idx + 1:]:
+        if is_skip_line(line):
+            continue
+        parts = re.split(r'\s{2,}|\t', line)
+        visit = {}
+        for col_idx, field in enumerate(header_fields):
+            if field is None or col_idx >= len(parts):
+                continue
+            nums = find_numbers(parts[col_idx])
+            if nums:
+                lo, hi = RANGES[field]
+                val    = nums[0]
+                if lo <= val <= hi:
+                    visit[field] = val
+        if visit:
+            visits.append(visit)
+    return visits
+
+
+def parse_key_value_format(raw_text: str) -> List[dict]:
+    """Parser C — Key-value format (one field per line)."""
+    lines = [l.strip() for l in raw_text.split('\n') if l.strip()]
+    visit = {}
+
+    for line in lines:
+        if is_skip_line(line):
+            continue
+        field = match_label(line)
+        if field is None:
+            continue
+
+        # Handle BP written as "112/74"
+        bp_match = re.search(r'(\d{2,3})\s*/\s*(\d{2,3})', line)
+        if bp_match and field in ("systolic_bp", "diastolic_bp"):
+            s, d = float(bp_match.group(1)), float(bp_match.group(2))
+            if 70 <= s <= 200: visit["systolic_bp"]  = s
+            if 40 <= d <= 130: visit["diastolic_bp"] = d
+            continue
+
+        nums   = find_numbers(line)
+        lo, hi = RANGES[field]
+        valid  = [n for n in nums if lo <= n <= hi]
+        if valid:
+            visit[field] = valid[0]
+
+    return [visit] if visit else []
+
+
+def score_visits(visits: List[dict]) -> int:
+    """Count total non-None extracted values across all visits."""
+    return sum(1 for v in visits for val in v.values() if val is not None)
+
+
+def best_parse(raw_text: str) -> Tuple[List[dict], str]:
+    """Try all parsers and return the one that extracted the most data."""
+    candidates = [
+        (parse_table_format(raw_text),    "row-per-field"),
+        (parse_column_format(raw_text),   "column-per-visit"),
+        (parse_key_value_format(raw_text),"key-value"),
+    ]
+    return max(candidates, key=lambda x: score_visits(x[0]))
+
+
+def extract_patient_id(raw_text: str) -> Optional[str]:
+    """Find common patient ID patterns in OCR text."""
+    patterns = [
+        r'PT[-\s]?\d{4}[-\s]?\d+',
+        r'Patient\s*(ID|No\.?|Number)[:\s]+([\w-]+)',
+        r'Reg(?:istration)?\s*(No\.?|#|:)\s*([\w-]+)',
+        r'ANC\s*(?:No\.?|#|:)?\s*([\w-]+)',
+        r'Card\s*(?:No\.?|#)[:\s]*([\w-]+)',
+        r'P/(\d+)',
+        r'MRN[:\s]+([\w-]+)',
+    ]
+    for pattern in patterns:
+        m = re.search(pattern, raw_text, re.IGNORECASE)
+        if m:
+            groups = [g for g in m.groups() if g]
+            return groups[-1] if groups else m.group(0)
+    return None
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# MAIN ENDPOINT
+# ═══════════════════════════════════════════════════════════════════════════════
+
+@router.post("/extract-report", response_model=ExtractResponse)
+async def extract_report(request: ExtractRequest):
+    """
+    POST /extract-report — Offline OCR extraction from prenatal report images.
+    Runs multiple preprocessing pipelines × OCR configs, returns best result.
+    """
+
+    # 1. Decode image
+    try:
+        image_bytes = base64.b64decode(request.image_base64)
+        pil_image   = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    except Exception as e:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Could not decode image: {e}"
+        )
+
+    # 2. Detect image type → get pipeline order
+    gray      = to_gray(pil_image)
+    img_type  = detect_image_type(gray, pil_image)
+    pipelines = PIPELINE_ORDERS.get(img_type, PIPELINE_ORDERS["standard"])
+
+    # 3. Run each pipeline → OCR → parse → score
+    best_visits   = []
+    best_score    = 0
+    best_text     = ""
+    best_pipeline = "none"
+    best_parser   = "none"
+    all_texts     = []
+
+    for pipeline_name in pipelines:
+        try:
+            if pipeline_name == "color_form":
+                processed = pipeline_color_form(pil_image)
+            else:
+                fn        = PIPELINE_FNS.get(pipeline_name, pipeline_standard)
+                processed = fn(to_gray(pil_image))
+        except Exception:
+            continue
+
+        try:
+            raw_text = run_ocr_best(processed)
+        except Exception:
+            continue
+
+        all_texts.append(raw_text[:200])
+
+        visits, parser_name = best_parse(raw_text)
+        score               = score_visits(visits)
+
+        if score > best_score:
+            best_score    = score
+            best_visits   = visits
+            best_text     = raw_text
+            best_pipeline = pipeline_name
+            best_parser   = parser_name
+
+    # 4. Handle complete failure
+    if not best_visits or best_score == 0:
+        fallback_text = all_texts[0][:300] if all_texts else "No text extracted"
+        return ExtractResponse(
+            visits     = [ExtractedVisit()],
+            patient_id = None,
+            notes      = (
+                f"Image type detected: {img_type}. "
+                "No structured data could be extracted. "
+                "Likely causes: very blurry, pure handwriting, or unusual layout. "
+                f"Raw OCR text: {fallback_text}"
+            ),
+            confidence = 0.0,
+            raw_text   = fallback_text,
+        )
+
+    # 5. Build ExtractedVisit objects
+    extracted = [
+        ExtractedVisit(
+            age          = v.get("age"),
+            systolic_bp  = v.get("systolic_bp"),
+            diastolic_bp = v.get("diastolic_bp"),
+            blood_sugar  = v.get("blood_sugar"),
+            body_temp    = v.get("body_temp"),
+            heart_rate   = v.get("heart_rate"),
+        )
+        for v in best_visits
+    ]
+
+    total_possible = len(extracted) * 6
+    total_filled   = sum(
+        1 for v in extracted
+        for val in [v.age, v.systolic_bp, v.diastolic_bp,
+                    v.blood_sugar, v.body_temp, v.heart_rate]
+        if val is not None
+    )
+    confidence = round(total_filled / max(total_possible, 1), 2)
+
+    notes = (
+        f"Image type: {img_type} | "
+        f"Pipeline: {best_pipeline} | "
+        f"Parser: {best_parser} | "
+        f"{total_filled}/{total_possible} fields extracted."
+    )
+
+    return ExtractResponse(
+        visits     = extracted,
+        patient_id = extract_patient_id(best_text),
+        notes      = notes,
+        confidence = confidence,
+        raw_text   = best_text[:500],
+    )

api/main.py

@@ -0,0 +1,224 @@
+"""
+MamaGuard -- FastAPI Server
+Maternal risk prediction API with clinical safety net and explainability.
+"""
+
+import torch
+import numpy as np
+import pickle
+import os
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+
+from api.schemas import PredictionRequest, PredictionResponse, AlertTier
+from api.alert_logic import compute_alert_tier, generate_action_text
+from src.model import MamaGuardMamba3
+from src.explainability import explain_prediction
+from api.extract_report import router as extract_router
+
+# --- App setup ----------------------------------------------------------------
+app = FastAPI(
+    title="SheGuard -- Maternal Risk API",
+    description=(
+        "Predicts maternal mortality risk from prenatal visit sequences. "
+        "Built with Mamba3 SSM for deployment in low-resource clinics."
+    ),
+    version="1.0.0",
+)
+
+app.include_router(extract_router)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ─── Model loading ────────────────────────────────────────────────────────────
+
+MODEL_PATH  = "models/mamaguard_mamba3.pt"
+SCALER_PATH = "models/scaler.pkl"
+
+model  = None
+scaler = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+FEATURE_ORDER = ['Age', 'SystolicBP', 'DiastolicBP', 'BS', 'BodyTemp', 'HeartRate']
+FEATURE_DEFAULTS = {
+    'Age': 30.0, 'SystolicBP': 120.0, 'DiastolicBP': 80.0,
+    'BS': 7.5, 'BodyTemp': 36.8, 'HeartRate': 76.0
+}
+
+
+@app.on_event("startup")
+async def load_model():
+    """Load model and scaler at server startup."""
+    global model, scaler
+
+    if not os.path.exists(MODEL_PATH):
+        print(f"WARNING: Model not found at {MODEL_PATH}. Run training first.")
+        return
+
+    model = MamaGuardMamba3(input_dim=6, d_model=64, n_layers=4, n_classes=3, d_state=32)
+    model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
+    model.to(device)
+    model.eval()
+
+    with open(SCALER_PATH, "rb") as f:
+        scaler = pickle.load(f)
+
+    print(f"MamaGuard model loaded on {device}")
+
+
+# --- Routes -------------------------------------------------------------------
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint for load balancers."""
+    return {
+        "status":       "healthy",
+        "model_loaded": model is not None,
+        "device":       device,
+        "version":      "1.0.0",
+    }
+
+
+@app.post("/predict", response_model=PredictionResponse)
+async def predict(request: PredictionRequest):
+    """
+    Process patient visit data and return risk prediction with explanation.
+    """
+
+    if model is None or scaler is None:
+        raise HTTPException(
+            status_code=503,
+            detail="Model not loaded. Contact system administrator."
+        )
+
+    # Prepare visit data
+    visits = request.visits
+    visit_map = {
+        'Age': 'age', 'SystolicBP': 'systolic_bp', 'DiastolicBP': 'diastolic_bp',
+        'BS': 'blood_sugar', 'BodyTemp': 'body_temp', 'HeartRate': 'heart_rate'
+    }
+
+    raw_array = []
+    missing_counts = []
+
+    for visit in visits:
+        row = []
+        missing = 0
+        for feat in FEATURE_ORDER:
+            attr = visit_map[feat]
+            val  = getattr(visit, attr, None)
+            if val is None:
+                val = FEATURE_DEFAULTS[feat]
+                missing += 1
+            row.append(val)
+        raw_array.append(row)
+        missing_counts.append(missing)
+
+    raw_np = np.array(raw_array, dtype=np.float32)
+
+    # Data quality score
+    total_fields  = len(visits) * len(FEATURE_ORDER)
+    missing_total = sum(missing_counts)
+    data_quality  = round(1.0 - missing_total / total_fields, 3)
+
+    # Scale
+    scaled_np = scaler.transform(raw_np)
+
+    # Pad or truncate to SEQ_LEN=5
+    SEQ_LEN = 5
+    n = len(scaled_np)
+    if n < SEQ_LEN:
+        pad = np.zeros((SEQ_LEN - n, scaled_np.shape[1]), dtype=np.float32)
+        scaled_np = np.vstack([pad, scaled_np])
+    elif n > SEQ_LEN:
+        scaled_np = scaled_np[-SEQ_LEN:]
+
+    # Clinical safety net (hard WHO rules)
+    from api.alert_logic import apply_clinical_safety_net, AlertTier
+
+    visits_raw = [v.model_dump() for v in request.visits]
+    forced_tier, forced_reason = apply_clinical_safety_net(visits_raw)
+
+    # Run model and explain
+    explanation = explain_prediction(model, scaled_np, scaler, device)
+
+    if forced_tier is not None:
+        explanation["top_reasons"].insert(0, f"[WHO guideline] {forced_reason}")
+
+    # Alert tier
+    alert_tier, suppressed = compute_alert_tier(
+        probabilities    = explanation["probabilities"],
+        patient_id       = request.patient_id,
+        visit_importances= explanation["visit_importance"],
+    )
+
+    # Override with clinical rule minimum if needed
+    tier_order = {"GREEN": 0, "AMBER": 1, "RED": 2}
+    if forced_tier is not None:
+        if tier_order[forced_tier] > tier_order[alert_tier.value]:
+            alert_tier = AlertTier(forced_tier)
+            suppressed = False
+
+    # Action text
+    action, transfer_order = generate_action_text(
+        tier             = alert_tier,
+        top_reasons      = explanation["top_reasons"],
+        data_quality     = data_quality,
+        staff_available  = request.staff_available,
+        blood_units      = request.blood_units,
+    )
+
+    return PredictionResponse(
+        patient_id        = request.patient_id,
+        risk_level        = explanation["risk_level"],
+        alert_tier        = alert_tier,
+        confidence        = explanation["confidence"],
+        data_quality      = data_quality,
+        top_reasons       = explanation["top_reasons"],
+        feature_importance= explanation["feature_importance"],
+        visit_importance  = explanation["visit_importance"],
+        action_required   = action,
+        transfer_order    = transfer_order,
+        suppressed        = suppressed,
+        probabilities     = explanation["probabilities"],
+    )
+
+
+@app.get("/stats")
+async def get_stats():
+    """Returns patient assessment statistics."""
+    from api.alert_logic import _alert_history
+    total   = len(_alert_history)
+    by_tier = {"GREEN": 0, "AMBER": 0, "RED": 0}
+    for v in _alert_history.values():
+        by_tier[v["tier"].value] += 1
+
+    return {
+        "patients_assessed": total,
+        "by_tier": by_tier,
+        "model_version": "SheGuard-Mamba3-v1",
+    }
+
+
+# --- Dashboard static files ---------------------------------------------------
+
+DASHBOARD_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "dashboard")
+
+if os.path.isdir(DASHBOARD_DIR):
+    app.mount("/static", StaticFiles(directory=DASHBOARD_DIR), name="dashboard-static")
+
+
+@app.get("/")
+async def serve_dashboard():
+    """Serve the dashboard HTML at root."""
+    index_path = os.path.join(DASHBOARD_DIR, "index.html")
+    if os.path.exists(index_path):
+        return FileResponse(index_path, media_type="text/html")
+    return {"status": "ok", "model_loaded": model is not None, "device": device}

api/schemas.py

@@ -0,0 +1,66 @@
+"""
+MamaGuard — API Schemas
+Pydantic models for request/response validation.
+"""
+
+from pydantic import BaseModel, Field, field_validator
+from typing import Optional, List
+from enum import Enum
+
+
+class RiskLevelEnum(str, Enum):
+    LOW    = "Low risk"
+    MEDIUM = "Medium risk"
+    HIGH   = "High risk"
+
+
+class PrenatalVisit(BaseModel):
+    """One prenatal checkup reading."""
+    age:           float = Field(...,  ge=10,  le=60,  description="Patient age in years")
+    systolic_bp:   float = Field(...,  ge=70,  le=200, description="Systolic blood pressure mmHg")
+    diastolic_bp:  float = Field(...,  ge=40,  le=130, description="Diastolic blood pressure mmHg")
+    blood_sugar:   Optional[float] = Field(None, ge=3.0, le=20.0, description="Blood glucose mmol/L")
+    body_temp:     Optional[float] = Field(None, ge=35.0, le=42.0, description="Temperature °C")
+    heart_rate:    Optional[float] = Field(None, ge=40,  le=160,  description="Heart rate bpm")
+    visit_date:    Optional[str]   = Field(None, description="ISO date of visit e.g. 2024-03-15")
+
+
+class PredictionRequest(BaseModel):
+    """Request body for POST /predict."""
+    patient_id:      str            = Field(..., description="Clinic's own patient ID")
+    visits:          List[PrenatalVisit] = Field(
+        ..., min_length=1, max_length=10,
+        description="Prenatal visit readings in chronological order (oldest first)"
+    )
+    staff_available: Optional[int]  = Field(None, ge=0, description="Doctors on duty right now")
+    blood_units:     Optional[int]  = Field(None, ge=0, description="Units of O-negative blood in stock")
+    clinic_name:     Optional[str]  = Field(None, description="Clinic name for alert routing")
+
+    @field_validator("visits")
+    @classmethod
+    def at_least_one_visit(cls, v):
+        if len(v) < 1:
+            raise ValueError("At least 1 prenatal visit is required")
+        return v
+
+
+class AlertTier(str, Enum):
+    GREEN  = "GREEN"   # < 65% high risk confidence
+    AMBER  = "AMBER"   # 65–85% — watch closely
+    RED    = "RED"     # > 85% + 2+ consecutive high-risk visits
+
+
+class PredictionResponse(BaseModel):
+    """Response from POST /predict."""
+    patient_id:       str
+    risk_level:       RiskLevelEnum
+    alert_tier:       AlertTier
+    confidence:       float
+    data_quality:     float
+    top_reasons:      List[str]
+    feature_importance: dict
+    visit_importance: List[float]
+    action_required:  str
+    transfer_order:   Optional[str] = None
+    suppressed:       bool = False
+    probabilities:    dict

dashboard/app.js

@@ -0,0 +1,332 @@
+/* ══════════════════════════════════════════════════════════════════════════
+   MamaGuard — Dashboard Application Logic
+   ══════════════════════════════════════════════════════════════════════════ */
+
+const API_BASE = "";
+
+/* ── State ─────────────────────────────────────────────────────────────── */
+
+let visitCount = 0;
+let currentImageBase64 = null;
+let currentImageType = null;
+
+
+/* ── Visit Management ──────────────────────────────────────────────────── */
+
+function addVisit(prefill = {}) {
+  visitCount++;
+  const container = document.getElementById("visits-container");
+  const block = document.createElement("div");
+  block.className = "visit-block";
+  block.id = `visit-${visitCount}`;
+
+  const v = visitCount;
+  block.innerHTML = `
+    <h4>Visit ${v}
+      ${prefill.visit_date
+        ? `<span style="font-weight:normal;color:var(--text-muted);font-size:0.78rem;margin-left:8px;">${prefill.visit_date}</span>`
+        : ""}
+    </h4>
+    <div class="grid-3">
+      <div>
+        <label>Age *</label>
+        <input type="number" id="v${v}_age" placeholder="e.g. 28" min="10" max="60">
+      </div>
+      <div>
+        <label>Systolic BP * (mmHg)</label>
+        <input type="number" id="v${v}_sbp" placeholder="e.g. 118" min="70" max="200">
+      </div>
+      <div>
+        <label>Diastolic BP * (mmHg)</label>
+        <input type="number" id="v${v}_dbp" placeholder="e.g. 75" min="40" max="130">
+      </div>
+      <div>
+        <label>Blood sugar (mmol/L)</label>
+        <input type="number" id="v${v}_bs" placeholder="e.g. 7.2" min="3" max="20" step="0.1">
+      </div>
+      <div>
+        <label>Body temp (&deg;C)</label>
+        <input type="number" id="v${v}_bt" placeholder="e.g. 36.8" min="35" max="42" step="0.1">
+      </div>
+      <div>
+        <label>Heart rate (bpm)</label>
+        <input type="number" id="v${v}_hr" placeholder="e.g. 78" min="40" max="160">
+      </div>
+    </div>`;
+  container.appendChild(block);
+
+  if (Object.keys(prefill).length > 0) {
+    fillVisit(v, prefill);
+  }
+}
+
+function fillVisit(v, data) {
+  const fieldMap = {
+    age:          `v${v}_age`,
+    systolic_bp:  `v${v}_sbp`,
+    diastolic_bp: `v${v}_dbp`,
+    blood_sugar:  `v${v}_bs`,
+    body_temp:    `v${v}_bt`,
+    heart_rate:   `v${v}_hr`,
+  };
+
+  for (const [key, inputId] of Object.entries(fieldMap)) {
+    const val = data[key];
+    const el = document.getElementById(inputId);
+    if (el && val !== null && val !== undefined) {
+      el.value = val;
+      el.classList.add("autofilled");
+      el.addEventListener("input", () => el.classList.remove("autofilled"), { once: true });
+    }
+  }
+}
+
+
+/* ── File Upload ───────────────────────────────────────────────────────── */
+
+function handleFileSelect(input) {
+  const file = input.files[0];
+  if (!file) return;
+
+  const reader = new FileReader();
+  reader.onload = (e) => {
+    const dataUrl = e.target.result;
+    const [header, base64] = dataUrl.split(",");
+    currentImageBase64 = base64;
+    currentImageType = file.type || "image/jpeg";
+
+    document.getElementById("preview-img").src = dataUrl;
+    document.getElementById("preview-wrap").style.display = "block";
+    document.getElementById("btn-extract").style.display = "block";
+    document.getElementById("extract-status").className = "extract-status";
+    document.getElementById("extract-status").textContent = "";
+  };
+  reader.readAsDataURL(file);
+}
+
+function clearUpload() {
+  currentImageBase64 = null;
+  currentImageType = null;
+  document.getElementById("file-input").value = "";
+  document.getElementById("preview-wrap").style.display = "none";
+  document.getElementById("btn-extract").style.display = "none";
+  document.getElementById("extract-status").className = "extract-status";
+  document.getElementById("extract-status").textContent = "";
+  document.getElementById("confidence-strip").style.display = "none";
+}
+
+
+/* ── Drag & Drop ───────────────────────────────────────────────────────── */
+
+document.addEventListener("DOMContentLoaded", () => {
+  const zone = document.getElementById("upload-zone");
+
+  zone.addEventListener("dragover", (e) => {
+    e.preventDefault();
+    zone.classList.add("drag-over");
+  });
+
+  zone.addEventListener("dragleave", () => {
+    zone.classList.remove("drag-over");
+  });
+
+  zone.addEventListener("drop", (e) => {
+    e.preventDefault();
+    zone.classList.remove("drag-over");
+    const file = e.dataTransfer.files[0];
+    if (file) {
+      const dt = new DataTransfer();
+      dt.items.add(file);
+      document.getElementById("file-input").files = dt.files;
+      handleFileSelect(document.getElementById("file-input"));
+    }
+  });
+
+  // Add first visit on load
+  addVisit();
+});
+
+
+/* ── Image Extraction ──────────────────────────────────────────────────── */
+
+async function extractFromImage() {
+  if (!currentImageBase64) return;
+
+  const statusEl = document.getElementById("extract-status");
+  const extractBtn = document.getElementById("btn-extract");
+
+  statusEl.className = "extract-status loading";
+  statusEl.textContent = "Reading the report... (2-5 seconds)";
+  extractBtn.disabled = true;
+
+  try {
+    const resp = await fetch(`${API_BASE}/extract-report`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({
+        image_base64: currentImageBase64,
+        image_type: currentImageType,
+      }),
+    });
+
+    if (!resp.ok) {
+      const err = await resp.json();
+      throw new Error(err.detail || `Server error ${resp.status}`);
+    }
+
+    const data = await resp.json();
+
+    if (data.patient_id) {
+      const pidField = document.getElementById("patient_id");
+      if (!pidField.value) {
+        pidField.value = data.patient_id;
+        pidField.classList.add("autofilled");
+      }
+    }
+
+    document.getElementById("visits-container").innerHTML = "";
+    visitCount = 0;
+
+    const visits = data.visits || [];
+    if (visits.length === 0) {
+      throw new Error("No visit data found in the image. Try a clearer photo.");
+    }
+
+    visits.forEach(visit => addVisit(visit));
+
+    const confPct = Math.round((data.confidence || 1.0) * 100);
+    const confColor = confPct >= 80 ? "var(--green)" : confPct >= 60 ? "var(--amber)" : "var(--red)";
+    const confEl = document.getElementById("confidence-strip");
+    confEl.style.display = "block";
+    confEl.innerHTML = `
+      <span style="color:${confColor};font-weight:600;">
+        Extraction confidence: ${confPct}%
+      </span>
+      ${confPct < 80 ? " -- please verify highlighted fields before submitting" : " -- all fields extracted successfully"}
+      ${data.notes ? `<br><span style="color:var(--text-muted);">${data.notes}</span>` : ""}
+    `;
+
+    statusEl.className = "extract-status success";
+    statusEl.textContent = `Extracted ${visits.length} visit${visits.length > 1 ? "s" : ""} -- highlighted fields are auto-filled. Verify before submitting.`;
+
+  } catch (err) {
+    statusEl.className = "extract-status error";
+    statusEl.textContent = `Extraction failed: ${err.message}`;
+  } finally {
+    extractBtn.disabled = false;
+  }
+}
+
+
+/* ── Prediction Submission ─────────────────────────────────────────────── */
+
+function getVal(id) {
+  const v = document.getElementById(id)?.value;
+  return (v === "" || v === null || v === undefined) ? null : parseFloat(v);
+}
+
+async function submitPrediction() {
+  document.getElementById("result-card").style.display = "none";
+  document.getElementById("error-box").style.display = "none";
+  document.getElementById("loader").style.display = "block";
+
+  const patientId = document.getElementById("patient_id").value.trim();
+  if (!patientId) { showError("Please enter a Patient ID"); return; }
+
+  const visits = [];
+  for (let i = 1; i <= visitCount; i++) {
+    const age = getVal(`v${i}_age`);
+    const sbp = getVal(`v${i}_sbp`);
+    const dbp = getVal(`v${i}_dbp`);
+    if (age === null || sbp === null || dbp === null) continue;
+    visits.push({
+      age, systolic_bp: sbp, diastolic_bp: dbp,
+      blood_sugar: getVal(`v${i}_bs`),
+      body_temp:   getVal(`v${i}_bt`),
+      heart_rate:  getVal(`v${i}_hr`),
+    });
+  }
+
+  if (visits.length === 0) {
+    showError("Please fill in at least one visit (Age, Systolic BP, Diastolic BP are required)");
+    return;
+  }
+
+  const payload = {
+    patient_id: patientId,
+    visits,
+    staff_available: getVal("staff_available"),
+    blood_units:     getVal("blood_units"),
+  };
+
+  try {
+    const resp = await fetch(`${API_BASE}/predict`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(payload),
+    });
+    if (!resp.ok) {
+      const err = await resp.json();
+      throw new Error(err.detail || `Server error ${resp.status}`);
+    }
+    showResult(await resp.json());
+  } catch (err) {
+    showError(`Could not reach the server: ${err.message}. Is the API running?`);
+  } finally {
+    document.getElementById("loader").style.display = "none";
+  }
+}
+
+
+/* ── Result Display ────────────────────────────────────────────────────── */
+
+function showResult(data) {
+  const tierClass  = { GREEN: "alert-green", AMBER: "alert-amber", RED: "alert-red" };
+  const badgeClass = { GREEN: "badge-green", AMBER: "badge-amber", RED: "badge-red" };
+  const tierLabel  = {
+    GREEN: "Low risk",
+    AMBER: "Medium risk -- monitor closely",
+    RED:   "High risk -- refer immediately"
+  };
+
+  const card = document.getElementById("result-card");
+  const tier = data.alert_tier;
+
+  card.className = `card result ${tierClass[tier]}`;
+  card.style.display = "block";
+
+  document.getElementById("risk-badge").textContent = tierLabel[tier];
+  document.getElementById("risk-badge").className = `risk-badge ${badgeClass[tier]}`;
+
+  document.getElementById("confidence-text").textContent =
+    `Model confidence: ${(data.confidence * 100).toFixed(1)}%` +
+    (data.suppressed ? " (alert suppressed -- already sent within 48h)" : "");
+
+  const qPct = Math.round(data.data_quality * 100);
+  document.getElementById("quality-bar").style.width = `${qPct}%`;
+  document.getElementById("quality-bar").style.background = qPct >= 70 ? "var(--green)" : "var(--amber)";
+  document.getElementById("quality-text").textContent =
+    `Data quality: ${qPct}%` + (qPct < 70 ? " -- low confidence, collect missing readings" : " -- good");
+
+  document.getElementById("reasons-list").innerHTML =
+    data.top_reasons.map(r => `<div class="reason-item">${r}</div>`).join("");
+
+  document.getElementById("action-text").textContent = data.action_required;
+
+  const transferBox = document.getElementById("transfer-box");
+  if (data.transfer_order) {
+    document.getElementById("transfer-text").textContent = data.transfer_order;
+    transferBox.style.display = "block";
+  } else {
+    transferBox.style.display = "none";
+  }
+
+  card.scrollIntoView({ behavior: "smooth" });
+}
+
+function showError(msg) {
+  document.getElementById("loader").style.display = "none";
+  const box = document.getElementById("error-box");
+  box.textContent = msg;
+  box.style.display = "block";
+}

dashboard/index.html

@@ -0,0 +1,113 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <meta name="description" content="MamaGuard maternal risk assessment dashboard powered by Mamba3 AI">
+  <title>MamaGuard &mdash; Maternal Risk Assessment</title>
+  <link rel="stylesheet" href="/static/styles.css">
+</head>
+
+<body>
+  <div class="app-container">
+
+    <!-- Header -->
+    <header class="app-header">
+      <h1>MamaGuard</h1>
+      <p class="subtitle">Maternal risk assessment &mdash; powered by Mamba3 AI</p>
+    </header>
+
+    <!-- Patient Details -->
+    <section class="card" id="patient-section">
+      <h3 class="card-title">Patient details</h3>
+      <p class="card-desc">Fields marked * are required. Leave others blank if not measured.</p>
+
+      <label for="patient_id">Patient ID *</label>
+      <input type="text" id="patient_id" placeholder="e.g. PT-2024-0045" required>
+
+      <label for="staff_available">Clinic staff on duty</label>
+      <input type="number" id="staff_available" placeholder="e.g. 2" min="0">
+
+      <label for="blood_units">O-negative blood units in stock</label>
+      <input type="number" id="blood_units" placeholder="e.g. 4" min="0">
+    </section>
+
+    <!-- Upload Zone -->
+    <section class="card" id="upload-section">
+      <h3 class="card-title">Auto-fill from report photo</h3>
+      <p class="card-desc">
+        Upload a photo or scan of the prenatal record card.
+        AI will read it and fill in all visit fields automatically.
+      </p>
+
+      <div class="upload-zone" id="upload-zone" onclick="document.getElementById('file-input').click()">
+        <span class="upload-icon">&#128247;</span>
+        <div class="upload-title">Tap to upload or drag a photo here</div>
+        <div class="upload-sub">JPEG, PNG, PDF &mdash; photo of paper report, printed form, or screenshot</div>
+      </div>
+
+      <input type="file" id="file-input" accept="image/*,.pdf" onchange="handleFileSelect(this)">
+
+      <div class="preview-wrap" id="preview-wrap">
+        <img id="preview-img" src="" alt="Report preview">
+        <button class="preview-remove" onclick="clearUpload()" title="Remove">&times;</button>
+      </div>
+
+      <div class="confidence-strip" id="confidence-strip"></div>
+
+      <button class="btn-extract" id="btn-extract" onclick="extractFromImage()">
+        Extract data from image
+      </button>
+
+      <div class="extract-status" id="extract-status"></div>
+    </section>
+
+    <!-- Visits -->
+    <section class="card" id="visits-section">
+      <h3 class="card-title">Prenatal visits (oldest first)</h3>
+      <p class="card-desc">Add visits manually, or use the photo upload above to auto-fill.</p>
+
+      <div id="visits-container"></div>
+      <button class="btn-add" onclick="addVisit()">+ Add another visit</button>
+    </section>
+
+    <!-- Submit -->
+    <button class="btn-primary" id="btn-submit" onclick="submitPrediction()">
+      Assess maternal risk
+    </button>
+
+    <div class="loader" id="loader">Analysing patient data...</div>
+    <div class="error-box" id="error-box"></div>
+
+    <!-- Results -->
+    <section class="card result" id="result-card">
+      <div id="alert-header">
+        <span id="risk-badge" class="risk-badge"></span>
+        <p id="confidence-text" style="font-size:0.84rem;color:var(--text-muted);margin-bottom:10px;"></p>
+      </div>
+
+      <p class="section-label">Data quality</p>
+      <div class="quality-bar-wrap">
+        <div class="quality-bar" id="quality-bar" style="background:var(--primary);width:0%"></div>
+      </div>
+      <p id="quality-text" class="section-label-sm"></p>
+
+      <p class="section-label" style="margin-top:14px;margin-bottom:6px;">Why this risk level:</p>
+      <div id="reasons-list"></div>
+
+      <p class="section-label" style="margin-top:14px;margin-bottom:6px;">Action required:</p>
+      <p id="action-text" style="font-size:0.92rem;line-height:1.5;"></p>
+
+      <div class="transfer-box" id="transfer-box" style="display:none;">
+        <strong>Transfer order:</strong>
+        <p id="transfer-text" style="margin-top:6px;"></p>
+      </div>
+    </section>
+
+  </div>
+
+  <script src="/static/app.js"></script>
+</body>
+
+</html>

dashboard/styles.css

@@ -0,0 +1,517 @@
+/* ══════════════════════════════════════════════════════════════════════════
+   MamaGuard — Dashboard Styles
+   ══════════════════════════════════════════════════════════════════════════ */
+
+/* ── Reset & Custom Properties ─────────────────────────────────────────── */
+
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
+
+:root {
+  --primary:       #8B2252;
+  --primary-light: #a83a6a;
+  --primary-pale:  #f9eff4;
+  --primary-bg:    #fdf8fb;
+  --accent:        #5b1a6e;
+  --surface:       rgba(255, 255, 255, 0.72);
+  --surface-solid: #ffffff;
+  --bg:            #f3eef0;
+  --text:          #2d2d2d;
+  --text-muted:    #777;
+  --border:        #e8dce2;
+  --green:         #2e7d32;
+  --green-bg:      #e8f5e9;
+  --amber:         #e65100;
+  --amber-bg:      #fff3e0;
+  --red:           #c62828;
+  --red-bg:        #ffebee;
+  --radius:        14px;
+  --radius-sm:     8px;
+  --shadow:        0 4px 24px rgba(139, 34, 82, 0.08);
+  --shadow-hover:  0 8px 32px rgba(139, 34, 82, 0.14);
+  --transition:    0.25s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+*,
+*::before,
+*::after {
+  box-sizing: border-box;
+  margin: 0;
+  padding: 0;
+}
+
+body {
+  font-family: 'Inter', system-ui, -apple-system, sans-serif;
+  background: var(--bg);
+  color: var(--text);
+  line-height: 1.5;
+  min-height: 100vh;
+}
+
+/* ── Layout Container ──────────────────────────────────────────────────── */
+
+.app-container {
+  max-width: 720px;
+  margin: 0 auto;
+  padding: 24px 16px 48px;
+}
+
+/* ── Header ────────────────────────────────────────────────────────────── */
+
+.app-header {
+  text-align: center;
+  margin-bottom: 28px;
+}
+
+.app-header h1 {
+  font-size: 1.75rem;
+  font-weight: 700;
+  color: var(--primary);
+  letter-spacing: -0.02em;
+}
+
+.app-header .subtitle {
+  color: var(--text-muted);
+  font-size: 0.9rem;
+  margin-top: 4px;
+  font-weight: 400;
+}
+
+/* ── Cards (glassmorphism) ─────────────────────────────────────────────── */
+
+.card {
+  background: var(--surface);
+  backdrop-filter: blur(12px);
+  -webkit-backdrop-filter: blur(12px);
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  padding: 24px;
+  margin-bottom: 20px;
+  box-shadow: var(--shadow);
+  transition: box-shadow var(--transition);
+  animation: fadeSlideUp 0.4s ease both;
+}
+
+.card:hover {
+  box-shadow: var(--shadow-hover);
+}
+
+.card-title {
+  color: var(--primary);
+  font-size: 1rem;
+  font-weight: 600;
+  margin-bottom: 4px;
+}
+
+.card-desc {
+  font-size: 0.8rem;
+  color: var(--text-muted);
+  margin-bottom: 16px;
+}
+
+/* ── Form Elements ─────────────────────────────────────────────────────── */
+
+label {
+  display: block;
+  font-weight: 500;
+  font-size: 0.82rem;
+  margin-bottom: 4px;
+  color: #555;
+  margin-top: 14px;
+}
+
+input {
+  width: 100%;
+  padding: 10px 14px;
+  border: 1.5px solid var(--border);
+  border-radius: var(--radius-sm);
+  font-family: inherit;
+  font-size: 0.92rem;
+  background: var(--surface-solid);
+  transition: border-color var(--transition), box-shadow var(--transition);
+}
+
+input:focus {
+  outline: none;
+  border-color: var(--primary);
+  box-shadow: 0 0 0 3px rgba(139, 34, 82, 0.12);
+}
+
+input::placeholder {
+  color: #bbb;
+}
+
+/* ── Visit Blocks ──────────────────────────────────────────────────────── */
+
+.visit-block {
+  border: 1.5px solid var(--border);
+  border-radius: var(--radius-sm);
+  padding: 16px;
+  margin-top: 14px;
+  background: var(--primary-bg);
+  position: relative;
+  animation: fadeSlideUp 0.3s ease both;
+}
+
+.visit-block h4 {
+  color: var(--primary);
+  font-size: 0.88rem;
+  font-weight: 600;
+  margin-bottom: 10px;
+}
+
+.grid-3 {
+  display: grid;
+  grid-template-columns: 1fr 1fr 1fr;
+  gap: 12px;
+}
+
+/* ── Buttons ───────────────────────────────────────────────────────────── */
+
+button {
+  font-family: inherit;
+  padding: 12px 24px;
+  border: none;
+  border-radius: var(--radius-sm);
+  cursor: pointer;
+  font-size: 0.95rem;
+  font-weight: 600;
+  transition: all var(--transition);
+}
+
+.btn-primary {
+  background: linear-gradient(135deg, var(--primary), var(--accent));
+  color: white;
+  width: 100%;
+  margin-top: 20px;
+  padding: 14px;
+  font-size: 1rem;
+  border-radius: var(--radius);
+  box-shadow: 0 4px 16px rgba(139, 34, 82, 0.25);
+}
+
+.btn-primary:hover {
+  transform: translateY(-1px);
+  box-shadow: 0 6px 24px rgba(139, 34, 82, 0.35);
+}
+
+.btn-primary:active {
+  transform: translateY(0);
+}
+
+.btn-add {
+  background: var(--primary-pale);
+  color: var(--primary);
+  margin-top: 10px;
+  font-size: 0.82rem;
+  padding: 10px 18px;
+}
+
+.btn-add:hover {
+  background: #f0dde6;
+}
+
+/* ── Upload Zone ───────────────────────────────────────────────────────── */
+
+.upload-zone {
+  border: 2px dashed #c9a0b4;
+  border-radius: var(--radius);
+  padding: 32px 20px;
+  text-align: center;
+  cursor: pointer;
+  transition: all var(--transition);
+  background: var(--primary-bg);
+  margin-bottom: 16px;
+}
+
+.upload-zone:hover,
+.upload-zone.drag-over {
+  border-color: var(--primary);
+  background: var(--primary-pale);
+  transform: scale(1.01);
+}
+
+.upload-zone .upload-icon {
+  font-size: 2.5rem;
+  display: block;
+  margin-bottom: 8px;
+}
+
+.upload-zone .upload-title {
+  font-weight: 600;
+  color: var(--primary);
+  font-size: 1rem;
+  margin-bottom: 4px;
+}
+
+.upload-zone .upload-sub {
+  font-size: 0.78rem;
+  color: var(--text-muted);
+}
+
+#file-input {
+  display: none;
+}
+
+/* ── Image Preview ─────────────────────────────────────────────────────── */
+
+.preview-wrap {
+  display: none;
+  margin-top: 14px;
+  position: relative;
+}
+
+.preview-wrap img {
+  max-width: 100%;
+  max-height: 200px;
+  border-radius: var(--radius-sm);
+  border: 1.5px solid var(--border);
+  display: block;
+  margin: 0 auto;
+}
+
+.preview-remove {
+  position: absolute;
+  top: 6px;
+  right: 6px;
+  background: var(--red);
+  color: white;
+  border: none;
+  border-radius: 50%;
+  width: 28px;
+  height: 28px;
+  font-size: 0.8rem;
+  cursor: pointer;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  padding: 0;
+  transition: transform var(--transition);
+}
+
+.preview-remove:hover {
+  transform: scale(1.1);
+}
+
+/* ── Extract Button & Status ───────────────────────────────────────────── */
+
+.btn-extract {
+  background: linear-gradient(135deg, var(--accent), #7b2d8e);
+  color: white;
+  width: 100%;
+  margin-top: 12px;
+  display: none;
+  font-size: 0.88rem;
+  padding: 12px;
+  border-radius: var(--radius-sm);
+}
+
+.btn-extract:hover {
+  transform: translateY(-1px);
+}
+
+.extract-status {
+  display: none;
+  margin-top: 12px;
+  padding: 12px 16px;
+  border-radius: var(--radius-sm);
+  font-size: 0.84rem;
+}
+
+.extract-status.loading {
+  background: #f3e5f5;
+  color: #6a1b9a;
+  display: block;
+}
+
+.extract-status.success {
+  background: var(--green-bg);
+  color: var(--green);
+  display: block;
+}
+
+.extract-status.error {
+  background: var(--red-bg);
+  color: var(--red);
+  display: block;
+}
+
+/* ── Autofilled Indicator ──────────────────────────────────────────────── */
+
+.autofilled {
+  border-color: var(--primary) !important;
+  background: #fdf0f5 !important;
+  box-shadow: 0 0 0 2px rgba(139, 34, 82, 0.1) !important;
+}
+
+.confidence-strip {
+  display: none;
+  margin-top: 10px;
+  padding: 10px 14px;
+  background: var(--primary-pale);
+  border-radius: var(--radius-sm);
+  font-size: 0.8rem;
+  color: #555;
+}
+
+/* ── Results Card ──────────────────────────────────────────────────────── */
+
+.result {
+  display: none;
+}
+
+.alert-green {
+  border-left: 5px solid var(--green);
+  background: rgba(232, 245, 233, 0.7);
+  backdrop-filter: blur(8px);
+}
+
+.alert-amber {
+  border-left: 5px solid var(--amber);
+  background: rgba(255, 243, 224, 0.7);
+  backdrop-filter: blur(8px);
+}
+
+.alert-red {
+  border-left: 5px solid var(--red);
+  background: rgba(255, 235, 238, 0.7);
+  backdrop-filter: blur(8px);
+}
+
+.risk-badge {
+  display: inline-block;
+  padding: 8px 20px;
+  border-radius: 24px;
+  font-weight: 700;
+  font-size: 1.05rem;
+  margin-bottom: 14px;
+  letter-spacing: -0.01em;
+}
+
+.badge-green {
+  background: #c8e6c9;
+  color: #1b5e20;
+}
+
+.badge-amber {
+  background: #ffe0b2;
+  color: #bf360c;
+}
+
+.badge-red {
+  background: #ffcdd2;
+  color: #b71c1c;
+}
+
+/* ── Quality Bar ───────────────────────────────────────────────────────── */
+
+.quality-bar-wrap {
+  background: #eee;
+  border-radius: 4px;
+  height: 8px;
+  margin: 6px 0 10px;
+  overflow: hidden;
+}
+
+.quality-bar {
+  height: 8px;
+  border-radius: 4px;
+  transition: width 0.6s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+/* ── Reason Items ──────────────────────────────────────────────────────── */
+
+.reason-item {
+  padding: 8px 12px;
+  background: var(--primary-pale);
+  border-radius: var(--radius-sm);
+  margin-bottom: 6px;
+  font-size: 0.88rem;
+  line-height: 1.4;
+}
+
+/* ── Transfer Box ──────────────────────────────────────────────────────── */
+
+.transfer-box {
+  background: var(--amber-bg);
+  border: 1.5px solid #ff9800;
+  border-radius: var(--radius-sm);
+  padding: 14px;
+  margin-top: 14px;
+  font-size: 0.88rem;
+}
+
+/* ── Loader & Error ────────────────────────────────────────────────────── */
+
+.loader {
+  display: none;
+  text-align: center;
+  padding: 24px;
+  color: var(--primary);
+  font-size: 0.95rem;
+  font-weight: 500;
+}
+
+.error-box {
+  background: var(--red-bg);
+  border: 1.5px solid #ef9a9a;
+  border-radius: var(--radius-sm);
+  padding: 14px;
+  color: var(--red);
+  display: none;
+  margin-top: 16px;
+  font-size: 0.9rem;
+}
+
+/* ── Section Labels ────────────────────────────────────────────────────── */
+
+.section-label {
+  font-size: 0.82rem;
+  font-weight: 600;
+  color: #555;
+}
+
+.section-label-sm {
+  font-size: 0.78rem;
+  color: var(--text-muted);
+  margin-bottom: 8px;
+}
+
+/* ── Animations ────────────────────────────────────────────────────────── */
+
+@keyframes fadeSlideUp {
+  from {
+    opacity: 0;
+    transform: translateY(12px);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+
+.card:nth-child(1) { animation-delay: 0s; }
+.card:nth-child(2) { animation-delay: 0.08s; }
+.card:nth-child(3) { animation-delay: 0.16s; }
+
+/* ── Responsive ────────────────────────────────────────────────────────── */
+
+@media (max-width: 640px) {
+  .grid-3 {
+    grid-template-columns: 1fr 1fr;
+    gap: 10px;
+  }
+
+  .app-container {
+    padding: 16px 12px 32px;
+  }
+
+  .card {
+    padding: 18px;
+  }
+}
+
+@media (max-width: 400px) {
+  .grid-3 {
+    grid-template-columns: 1fr;
+  }
+}

data/maternal_health.csv

@@ -0,0 +1,1015 @@
+Age,SystolicBP,DiastolicBP,BS,BodyTemp,HeartRate,RiskLevel
+25,130,80,15,98,86,high risk
+35,140,90,13,98,70,high risk
+29,90,70,8,100,80,high risk
+30,140,85,7,98,70,high risk
+35,120,60,6.1,98,76,low risk
+23,140,80,7.01,98,70,high risk
+23,130,70,7.01,98,78,mid risk
+35,85,60,11,102,86,high risk
+32,120,90,6.9,98,70,mid risk
+42,130,80,18,98,70,high risk
+23,90,60,7.01,98,76,low risk
+19,120,80,7,98,70,mid risk
+25,110,89,7.01,98,77,low risk
+20,120,75,7.01,100,70,mid risk
+48,120,80,11,98,88,mid risk
+15,120,80,7.01,98,70,low risk
+50,140,90,15,98,90,high risk
+25,140,100,7.01,98,80,high risk
+30,120,80,6.9,101,76,mid risk
+10,70,50,6.9,98,70,low risk
+40,140,100,18,98,90,high risk
+50,140,80,6.7,98,70,mid risk
+21,90,65,7.5,98,76,low risk
+18,90,60,7.5,98,70,low risk
+21,120,80,7.5,98,76,low risk
+16,100,70,7.2,98,80,low risk
+19,120,75,7.2,98,66,low risk
+22,100,65,7.2,98,70,low risk
+49,120,90,7.2,98,77,low risk
+28,90,60,7.2,98,82,low risk
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+23,100,85,7.1,98,66,low risk
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+21,120,80,7.1,98,77,low risk
+21,75,50,6.1,98,70,low risk
+12,95,60,6.1,102,60,low risk
+60,120,80,6.1,98,75,low risk
+55,100,65,6.1,98,66,low risk
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+32,120,90,6.4,98,70,low risk
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+23,90,60,6.4,98,76,low risk
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+15,120,80,7.2,98,70,low risk
+15,80,60,7,98,80,low risk
+12,95,60,7.2,98,77,low risk
+29,90,70,6.7,98,80,mid risk
+31,120,60,6.1,98,76,mid risk
+29,130,70,6.7,98,78,mid risk
+17,85,60,9,102,86,mid risk
+19,120,80,7,98,70,mid risk
+20,110,60,7,100,70,mid risk
+32,120,65,6,101,76,mid risk
+26,85,60,6,101,86,mid risk
+29,130,70,7.7,98,78,mid risk
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+54,130,70,12,98,67,mid risk
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+55,120,90,12,98,70,mid risk
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+16,90,65,6.9,98,76,mid risk
+33,115,65,7,98,70,mid risk
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+48,140,90,15,98,90,high risk
+25,140,100,6.8,98,80,high risk
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deployment/ngos_guide.md

@@ -0,0 +1,51 @@
+# Deploying MamaGuard at your clinic or NGO
+
+## What you need
+
+- Any laptop or desktop computer (even a 2015 model works)
+- Python 3.10 or higher (free download at python.org)
+- The clinic's local WiFi network (so multiple devices can use the dashboard)
+
+## Setup for a new clinic (one-time, ~30 minutes)
+
+1. Copy the entire mamaGuard/ folder onto the clinic computer
+2. Open a terminal (Command Prompt on Windows)
+3. cd into the mamaGuard folder
+4. Run: pip install -r requirements.txt
+5. Run: python -m src.train
+6. Run: uvicorn api.main:app --host 0.0.0.0 --port 8000
+   (--host 0.0.0.0 means any device on the network can reach it)
+
+## Using the dashboard (for health workers)
+
+1. On any phone or tablet connected to the clinic WiFi, open a browser
+2. Go to: http://[clinic-computer-IP]:8000/dashboard
+   (The IT person can find the IP in the computer's network settings)
+3. Enter the patient's ID and their prenatal visit readings
+4. Press "Assess maternal risk"
+5. Follow the action instruction shown
+
+## Understanding the three alert levels
+
+🟢 GREEN — Continue standard care. Schedule next visit as normal.
+🟡 AMBER — Elevated risk. Add an extra checkup within 72 hours.
+🔴 RED   — High risk. Refer to hospital. If a transfer order is shown, call
+           the referral line immediately.
+
+## What to do about LOW CONFIDENCE alerts
+
+If you see "Data quality: low" on any result, record the missing readings
+at the next visit. The model's accuracy improves significantly when all
+six fields (Age, Systolic BP, Diastolic BP, Blood Sugar, Temperature,
+Heart Rate) are filled in.
+
+## Weekly reporting
+
+Go to http://[clinic-computer-IP]:8000/stats to see a summary of all
+patients assessed this week, broken down by risk level. This can be
+emailed to district health authorities.
+
+## If the server is not running
+
+The health worker will see "Could not reach the server."
+Ask the IT contact to re-run: uvicorn api.main:app --host 0.0.0.0 --port 8000

models/evaluation_report.txt

@@ -0,0 +1,107 @@
+
+╔══════════════════════════════════════════════════════════════════════════╗
+║              MAMAGUARD — MODEL EVALUATION REPORT                        ║
+║              Generated automatically by src/evaluate.py                 ║
+╚══════════════════════════════════════════════════════════════════════════╝
+
+MODEL
+  Architecture : MamaGuard-Mamba3 (Trapezoidal SSM + MIMO + Complex state)
+  Parameters   : ~287,815
+  Input        : Sequence of prenatal visits (up to 5) × 6 vital signs
+  Output       : 3-class risk prediction (Low / Medium / High)
+  Dataset      : UCI Maternal Health Risk (1,014 rows)
+  Training set : 1525 sequences
+  Validation set: 191 sequences
+
+──────────────────────────────────────────────────────────────────────────
+PERFORMANCE METRICS (on held-out validation set)
+──────────────────────────────────────────────────────────────────────────
+
+  Overall accuracy     : 0.880 (88.0%)
+
+  ROC-AUC per class:
+    Low risk           : 0.977
+    Medium risk        : 0.925
+    High risk          : 0.995
+
+  ** HIGH RISK RECALL (most critical for patient safety):
+    1.000 — of all truly high-risk patients, 100.0% were correctly flagged
+
+  ** HIGH RISK PRECISION (alarm fatigue indicator):
+    0.895 — of all patients flagged high-risk, 89.5% truly were
+
+  Detailed per-class report:
+              precision    recall  f1-score   support
+
+    Low risk      0.917     0.857     0.886        77
+ Medium risk      0.823     0.810     0.816        63
+   High risk      0.895     1.000     0.944        51
+
+    accuracy                          0.880       191
+   macro avg      0.878     0.889     0.882       191
+weighted avg      0.880     0.880     0.878       191
+
+
+──────────────────────────────────────────────────────────────────────────
+CONFUSION MATRIX (raw counts)
+──────────────────────────────────────────────────────────────────────────
+  Rows = true label, Columns = predicted label
+
+                 Pred: Low   Pred: Mid   Pred: High
+  True: Low           66          11            0
+  True: Mid            6          51            6
+  True: High           0           0           51
+
+  Most dangerous mistakes (False Negatives for High Risk):
+    High-risk patients predicted as Low risk  : 0
+    High-risk patients predicted as Mid risk  : 0
+
+──────────────────────────────────────────────────────────────────────────
+HYBRID SYSTEM NOTE
+──────────────────────────────────────────────────────────────────────────
+  MamaGuard uses a HYBRID architecture:
+    1. Mamba3 model: handles subtle temporal patterns (learned from data)
+    2. WHO clinical rules: hard overrides for obvious danger signs
+       - Rule 1: SystolicBP >= 160 -> RED
+       - Rule 2: SystolicBP >= 140 -> AMBER minimum
+       - Rule 3: Blood sugar > 11.1 -> AMBER minimum
+       - Rule 4: BP rise >= 20 mmHg -> AMBER minimum
+       - Rule 5: 3+ vitals escalating simultaneously -> RED
+
+  The metrics above reflect the NEURAL MODEL ONLY (without clinical rules).
+  In deployment, the clinical rules provide an additional safety floor,
+  meaning real-world recall for high-risk cases is higher than shown above.
+
+──────────────────────────────────────────────────────────────────────────
+LIMITATIONS
+──────────────────────────────────────────────────────────────────────────
+  1. SMALL DATASET: Trained on 1,014 rows from a single UCI dataset.
+     Real-world clinical models typically require 10,000–100,000+ samples.
+
+  2. SYNTHETIC SEQUENCES: The UCI dataset has no patient IDs or timestamps.
+     We created artificial 5-visit sequences by sorting rows by age.
+     These do not represent real patient trajectories.
+
+  3. NOT CLINICALLY VALIDATED: This model has NOT been validated against
+     real patient outcomes in a clinical setting. It must NOT be used
+     for actual medical decisions without proper clinical validation trials.
+
+  4. POPULATION BIAS: The UCI dataset was collected from a specific
+     population. Performance may differ on patients from different
+     regions, ethnicities, or healthcare contexts.
+
+  5. RESEARCH PROTOTYPE: This is a proof-of-concept demonstrating the
+     application of Mamba3 SSMs to maternal health risk prediction.
+     The system design (alarm fatigue mitigation, resource-aware routing,
+     OCR auto-fill) represents the primary contribution of this work.
+
+──────────────────────────────────────────────────────────────────────────
+CITATION
+──────────────────────────────────────────────────────────────────────────
+  If you use this work, please cite:
+    MamaGuard: Maternal Mortality Early Warning using Mamba3 Sequential
+    State-Space Models with Clinical Safety Rules.
+    [Your Name], 2025. GitHub: [your-repo-url]
+    Based on: Gu & Dao (2023) Mamba; UCI Maternal Health Risk dataset.
+
+══════════════════════════════════════════════════════════════════════════

models/mamaguard_mamba3.pt

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+oid sha256:ad79f8d1baea4ba994bba405e0e849cb5a19d0960ad4d2b19eb7a0cbec985873
+size 1179269

models/scaler.pkl

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+oid sha256:90559a3874dfc35c38feea7d5b08faf2a42eb023ef6e2dc23e9c8e42e1a77cee
+size 560

requirements.txt

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+torch>=2.1.0
+numpy>=1.24.0
+pandas>=2.0.0
+scikit-learn>=1.3.0
+fastapi>=0.104.0
+uvicorn>=0.24.0
+pydantic>=2.0.0
+shap>=0.44.0
+einops>=0.7.0
+matplotlib>=3.7.0
+opencv-python-headless>=4.8.0
+pytesseract>=0.3.10
+Pillow>=10.0.0
+python-multipart>=0.0.6

src/data_pipeline.py

@@ -0,0 +1,281 @@
+"""
+MamaGuard — Data Pipeline
+Loads UCI CSV, cleans, scales, builds sequences, and splits into train/val.
+"""
+
+import pandas as pd
+import numpy as np
+import torch
+from torch.utils.data import Dataset, DataLoader
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+
+# ─── Constants ────────────────────────────────────────────────────────────────
+
+FEATURE_COLS = ['Age', 'SystolicBP', 'DiastolicBP', 'BS', 'BodyTemp', 'HeartRate']
+LABEL_COL    = 'RiskLevel'
+LABEL_MAP    = {'low risk': 0, 'mid risk': 1, 'high risk': 2}
+SEQ_LEN      = 5
+
+
+# ─── Data quality scorer ─────────────────────────────────────────────────────
+
+def compute_data_quality(row: pd.Series) -> float:
+    """Returns 0.0–1.0 indicating how complete and plausible the data is."""
+    score, total = 0.0, 0
+
+    for col in FEATURE_COLS:
+        total += 1
+        if pd.notna(row.get(col)):
+            score += 1.0
+
+    plausibility = {
+        'Age': (10, 60), 'SystolicBP': (70, 200), 'DiastolicBP': (40, 130),
+        'BS': (3.0, 20.0), 'BodyTemp': (35.0, 42.0), 'HeartRate': (40, 160),
+    }
+    for col, (lo, hi) in plausibility.items():
+        total += 1
+        val = row.get(col)
+        if pd.notna(val) and lo <= val <= hi:
+            score += 1.0
+
+    return round(score / total, 3)
+
+
+# ─── Sequence builders ────────────────────────────────────────────────────────
+
+def build_type_a_sequences(X: np.ndarray, y: np.ndarray,
+                            quality: np.ndarray) -> tuple:
+    """Type A — Within-risk-group sliding window sequences."""
+    X_seq, y_seq, q_seq = [], [], []
+
+    for risk_label in [0, 1, 2]:
+        idx = np.where(y == risk_label)[0]
+        if len(idx) < SEQ_LEN:
+            continue
+
+        age_order  = np.argsort(X[idx, 0])
+        idx_sorted = idx[age_order]
+
+        for i in range(len(idx_sorted) - SEQ_LEN + 1):
+            seq_idx = idx_sorted[i : i + SEQ_LEN]
+            X_seq.append(X[seq_idx].copy())
+            y_seq.append(risk_label)
+            q_seq.append(quality[seq_idx].mean())
+
+    return X_seq, y_seq, q_seq
+
+
+def build_type_b_sequences(X: np.ndarray, y: np.ndarray,
+                            rng: np.random.Generator) -> tuple:
+    """Type B — Synthetic escalation sequences for mid and high risk."""
+    X_seq, y_seq, q_seq = [], [], []
+
+    for risk_label in [1, 2]:
+        indices = np.where(y == risk_label)[0]
+        for idx in indices:
+            anchor = X[idx]
+            seq = []
+            for step in range(SEQ_LEN):
+                fraction = 0.30 + 0.70 * (step / (SEQ_LEN - 1))
+                noise = rng.normal(0, 0.05, anchor.shape)
+                seq.append(anchor * fraction + noise)
+            X_seq.append(np.array(seq, dtype=np.float32))
+            y_seq.append(risk_label)
+            q_seq.append(1.0)
+
+    return X_seq, y_seq, q_seq
+
+
+def build_type_c_sequences(X: np.ndarray, y: np.ndarray,
+                            rng: np.random.Generator,
+                            n_per_transition: int = 80) -> tuple:
+    """Type C — Cross-risk deterioration sequences (low->mid, low->high, mid->high)."""
+    X_seq, y_seq, q_seq = [], [], []
+
+    low_idx  = np.where(y == 0)[0]
+    mid_idx  = np.where(y == 1)[0]
+    high_idx = np.where(y == 2)[0]
+
+    if len(low_idx) == 0 or len(mid_idx) == 0 or len(high_idx) == 0:
+        return X_seq, y_seq, q_seq
+
+    def blend(a: np.ndarray, b: np.ndarray, frac: float = 0.5) -> np.ndarray:
+        """Linear interpolation between two rows + small noise."""
+        return a * (1 - frac) + b * frac + rng.normal(0, 0.05, a.shape)
+
+    def pick(indices: np.ndarray) -> np.ndarray:
+        """Pick one random row from an index array."""
+        return X[rng.choice(indices)]
+
+    # Transition 1: LOW -> HIGH
+    for _ in range(n_per_transition):
+        seq = [
+            pick(low_idx), pick(low_idx),
+            blend(pick(low_idx), pick(high_idx)),
+            pick(high_idx), pick(high_idx),
+        ]
+        X_seq.append(np.array(seq, dtype=np.float32))
+        y_seq.append(2)
+        q_seq.append(1.0)
+
+    # Transition 2: LOW -> MID
+    for _ in range(n_per_transition):
+        seq = [
+            pick(low_idx), pick(low_idx), pick(low_idx),
+            pick(mid_idx), pick(mid_idx),
+        ]
+        X_seq.append(np.array(seq, dtype=np.float32))
+        y_seq.append(1)
+        q_seq.append(1.0)
+
+    # Transition 3: MID -> HIGH
+    for _ in range(n_per_transition):
+        seq = [
+            pick(mid_idx), pick(mid_idx),
+            blend(pick(mid_idx), pick(high_idx)),
+            pick(high_idx), pick(high_idx),
+        ]
+        X_seq.append(np.array(seq, dtype=np.float32))
+        y_seq.append(2)
+        q_seq.append(1.0)
+
+    return X_seq, y_seq, q_seq
+
+
+# ─── Internal helper ─────────────────────────────────────────────────────────
+
+def _build_all_sequences(X: np.ndarray, y: np.ndarray,
+                          quality: np.ndarray,
+                          rng: np.random.Generator,
+                          include_augmentation: bool,
+                          label: str = "") -> tuple:
+    """Builds all sequence types from a given set of scaled rows."""
+    X_all, y_all, q_all = [], [], []
+
+    a_X, a_y, a_q = build_type_a_sequences(X, y, quality)
+    X_all += a_X; y_all += a_y; q_all += a_q
+    print(f"  {label} Type A (within-class):  {len(a_X):5d} sequences")
+
+    if include_augmentation:
+        b_X, b_y, b_q = build_type_b_sequences(X, y, rng)
+        X_all += b_X; y_all += b_y; q_all += b_q
+        print(f"  {label} Type B (escalation):    {len(b_X):5d} sequences")
+
+        min_cls = min(sum(y == 0), sum(y == 1), sum(y == 2))
+        if min_cls >= SEQ_LEN:
+            c_X, c_y, c_q = build_type_c_sequences(X, y, rng)
+            X_all += c_X; y_all += c_y; q_all += c_q
+            print(f"  {label} Type C (cross-risk):    {len(c_X):5d} sequences")
+        else:
+            print(f"  {label} Type C skipped (smallest class has {min_cls} rows)")
+
+    return X_all, y_all, q_all
+
+
+# ─── Main pipeline ────────────────────────────────────────────────────────────
+
+def load_and_preprocess(csv_path: str):
+    """
+    Returns pre-split, pre-scaled sequence arrays + the fitted scaler.
+
+    Returns:
+        X_train, y_train, q_train, X_val, y_val, q_val, scaler
+    """
+
+    # 1. Load
+    df = pd.read_csv(csv_path)
+    print(f"Loaded {len(df)} rows from {csv_path}")
+    df[LABEL_COL] = df[LABEL_COL].str.lower().str.strip()
+
+    # 2. Quality scores BEFORE imputation
+    quality_per_row = df.apply(compute_data_quality, axis=1).values
+
+    # 3. Impute missing values
+    for col in FEATURE_COLS:
+        df[col] = df[col].fillna(df[col].median())
+
+    X_raw = df[FEATURE_COLS].values.astype(np.float32)
+    y_raw = df[LABEL_COL].map(LABEL_MAP).values
+
+    # 4. Split raw rows first (prevents data leakage and sequence overlap)
+    (X_train_raw, X_val_raw,
+     y_train_raw, y_val_raw,
+     q_train_raw, q_val_raw) = train_test_split(
+        X_raw, y_raw, quality_per_row,
+        test_size=0.2, random_state=42, stratify=y_raw
+    )
+    print(f"Raw split -> train: {len(X_train_raw)} rows | val: {len(X_val_raw)} rows")
+    print(f"Train classes: low={sum(y_train_raw==0)} "
+          f"mid={sum(y_train_raw==1)} high={sum(y_train_raw==2)}")
+    print(f"Val   classes: low={sum(y_val_raw==0)} "
+          f"mid={sum(y_val_raw==1)} high={sum(y_val_raw==2)}")
+
+    # 5. Fit scaler on train rows only
+    scaler         = StandardScaler()
+    X_train_scaled = scaler.fit_transform(X_train_raw)
+    X_val_scaled   = scaler.transform(X_val_raw)
+
+    # 6. Build sequences separately
+    rng = np.random.default_rng(42)
+
+    print("\nBuilding TRAINING sequences:")
+    train_seqs = _build_all_sequences(
+        X_train_scaled, y_train_raw, q_train_raw,
+        rng, include_augmentation=True, label="Train"
+    )
+
+    print("\nBuilding VALIDATION sequences:")
+    val_seqs = _build_all_sequences(
+        X_val_scaled, y_val_raw, q_val_raw,
+        rng, include_augmentation=False, label="Val"
+    )
+
+    X_train, y_train, q_train = [np.array(a) for a in train_seqs]
+    X_val,   y_val,   q_val   = [np.array(a) for a in val_seqs]
+
+    print(f"\nFinal -> Train: {len(X_train)} seqs | Val: {len(X_val)} seqs")
+    print(f"Train dist: {dict(zip(*np.unique(y_train, return_counts=True)))}")
+    print(f"Val   dist: {dict(zip(*np.unique(y_val,   return_counts=True)))}")
+
+    return (X_train.astype(np.float32), y_train.astype(np.int64),
+            q_train.astype(np.float32),
+            X_val.astype(np.float32),   y_val.astype(np.int64),
+            q_val.astype(np.float32),
+            scaler)
+
+
+# ─── PyTorch Dataset ──────────────────────────────────────────────────────────
+
+class MaternalDataset(Dataset):
+    def __init__(self, X, y, quality):
+        self.X = torch.tensor(np.array(X), dtype=torch.float32)
+        self.y = torch.tensor(np.array(y), dtype=torch.long)
+        self.q = torch.tensor(np.array(quality), dtype=torch.float32)
+
+    def __len__(self):
+        return len(self.X)
+
+    def __getitem__(self, idx):
+        return self.X[idx], self.y[idx], self.q[idx]
+
+
+def get_dataloaders(csv_path: str, batch_size: int = 32):
+    """Returns train/val DataLoaders and the fitted scaler."""
+    (X_train, y_train, q_train,
+     X_val,   y_val,   q_val,
+     scaler) = load_and_preprocess(csv_path)
+
+    train_ds = MaternalDataset(X_train, y_train, q_train)
+    val_ds   = MaternalDataset(X_val,   y_val,   q_val)
+
+    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)
+    val_loader   = DataLoader(val_ds,   batch_size=batch_size, shuffle=False)
+
+    print(f"\nDataLoaders ready:")
+    print(f"  Train: {len(train_ds)} sequences | "
+          f"{len(train_loader)} batches of {batch_size}")
+    print(f"  Val:   {len(val_ds)} sequences | "
+          f"{len(val_loader)} batches")
+
+    return train_loader, val_loader, scaler

src/evaluate.py

@@ -0,0 +1,373 @@
+# src/evaluate.py
+"""
+MamaGuard — Model Evaluation
+Produces accuracy, per-class metrics, confusion matrix, ROC-AUC, and a text report.
+
+Usage: python -m src.evaluate
+"""
+
+import os
+import pickle
+import numpy as np
+import torch
+import torch.nn.functional as F
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+
+from sklearn.metrics import (
+    accuracy_score,
+    classification_report,
+    confusion_matrix,
+    roc_auc_score,
+    roc_curve,
+)
+
+from src.data_pipeline import load_and_preprocess
+from src.model import MamaGuardMamba3
+
+# ── Config ────────────────────────────────────────────────────────────────────
+CSV_PATH    = "data/maternal_health.csv"
+MODEL_PATH  = "models/mamaguard_mamba3.pt"
+SCALER_PATH = "models/scaler.pkl"
+REPORT_PATH = "models/evaluation_report.txt"
+CM_PATH     = "models/confusion_matrix.png"
+ROC_PATH    = "models/roc_curves.png"
+
+CLASS_NAMES  = ["Low risk", "Medium risk", "High risk"]
+CLASS_COLORS = ["#2e7d32", "#e65100", "#c62828"]
+
+# ── Load model ────────────────────────────────────────────────────────────────
+
+def load_model(device: str):
+    """Load trained model weights into a fresh MamaGuardMamba3 instance."""
+    model = MamaGuardMamba3(
+        input_dim=6, d_model=64, n_layers=4, n_classes=3, d_state=32
+    )
+    model.load_state_dict(
+        torch.load(MODEL_PATH, map_location=device)
+    )
+    model.to(device)
+    model.eval()
+    return model
+
+
+# ── Inference ─────────────────────────────────────────────────────────────────
+
+def get_predictions(model, X: np.ndarray, device: str, batch_size: int = 64):
+    """
+    Run the model on all validation sequences in batches.
+    Returns: y_pred (N,), y_proba (N, 3)
+    """
+    model.eval()
+    all_probs = []
+
+    for i in range(0, len(X), batch_size):
+        batch   = torch.tensor(X[i:i+batch_size], dtype=torch.float32).to(device)
+        with torch.no_grad():
+            logits = model(batch)
+            probs  = F.softmax(logits, dim=-1).cpu().numpy()
+        all_probs.append(probs)
+
+    y_proba = np.vstack(all_probs)
+    y_pred  = y_proba.argmax(axis=1)
+    return y_pred, y_proba
+
+
+# ── Confusion matrix plot ─────────────────────────────────────────────────────
+
+def plot_confusion_matrix(cm: np.ndarray, save_path: str):
+    """Plot raw and normalised confusion matrices side by side."""
+    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+    fig.suptitle("MamaGuard Confusion Matrix", fontsize=14, fontweight="bold")
+
+    for ax_idx, (data, title) in enumerate([
+        (cm, "Raw counts"),
+        (cm.astype(float) / cm.sum(axis=1, keepdims=True), "Normalised (row %)")
+    ]):
+        im = axes[ax_idx].imshow(data, cmap="RdYlGn", vmin=0,
+                                  vmax=(1 if ax_idx == 1 else None))
+        axes[ax_idx].set_xticks(range(3))
+        axes[ax_idx].set_yticks(range(3))
+        axes[ax_idx].set_xticklabels(CLASS_NAMES, rotation=15, ha="right")
+        axes[ax_idx].set_yticklabels(CLASS_NAMES)
+        axes[ax_idx].set_xlabel("Predicted label")
+        axes[ax_idx].set_ylabel("True label")
+        axes[ax_idx].set_title(title)
+
+        for i in range(3):
+            for j in range(3):
+                val  = data[i, j]
+                text = f"{val:.2f}" if ax_idx == 1 else str(int(val))
+                color = "white" if (ax_idx == 1 and val < 0.4) or \
+                                   (ax_idx == 0 and val > cm.max() * 0.6) else "black"
+                axes[ax_idx].text(j, i, text, ha="center", va="center",
+                                   fontsize=11, color=color, fontweight="bold")
+
+        plt.colorbar(im, ax=axes[ax_idx], fraction=0.046, pad=0.04)
+
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches="tight")
+    plt.close()
+    print(f"  Confusion matrix saved -> {save_path}")
+
+
+# ── ROC curves ────────────────────────────────────────────────────────────────
+
+def plot_roc_curves(y_true: np.ndarray, y_proba: np.ndarray, save_path: str):
+    """Plot one-vs-rest ROC curves per class with AUC scores."""
+    from sklearn.preprocessing import label_binarize
+    y_bin = label_binarize(y_true, classes=[0, 1, 2])
+
+    fig, ax = plt.subplots(figsize=(8, 6))
+    ax.plot([0, 1], [0, 1], 'k--', linewidth=1, label="Random (AUC = 0.50)")
+
+    for i, (class_name, color) in enumerate(zip(CLASS_NAMES, CLASS_COLORS)):
+        fpr, tpr, _ = roc_curve(y_bin[:, i], y_proba[:, i])
+        auc         = roc_auc_score(y_bin[:, i], y_proba[:, i])
+        ax.plot(fpr, tpr, color=color, linewidth=2,
+                label=f"{class_name} (AUC = {auc:.3f})")
+
+    ax.set_xlabel("False Positive Rate", fontsize=12)
+    ax.set_ylabel("True Positive Rate (Recall)", fontsize=12)
+    ax.set_title("ROC Curves — MamaGuard Mamba3", fontsize=13, fontweight="bold")
+    ax.legend(loc="lower right", fontsize=10)
+    ax.grid(alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches="tight")
+    plt.close()
+    print(f"  ROC curves saved -> {save_path}")
+
+
+# ── Text report ───────────────────────────────────────────────────────────────
+
+def build_text_report(
+    y_true, y_pred, y_proba,
+    class_report: str,
+    cm: np.ndarray,
+    n_train: int,
+    n_val: int
+) -> str:
+    """Build a complete text report for model card / README."""
+    overall_acc = accuracy_score(y_true, y_pred)
+
+    from sklearn.preprocessing import label_binarize
+    y_bin  = label_binarize(y_true, classes=[0, 1, 2])
+    aucs   = [roc_auc_score(y_bin[:, i], y_proba[:, i]) for i in range(3)]
+
+    hr_recall    = cm[2, 2] / max(cm[2, :].sum(), 1)
+    hr_precision = cm[2, 2] / max(cm[:, 2].sum(), 1)
+
+    report = f"""
+╔══════════════════════════════════════════════════════════════════════════╗
+║              MAMAGUARD — MODEL EVALUATION REPORT                        ║
+║              Generated automatically by src/evaluate.py                 ║
+╚══════════════════════════════════════════════════════════════════════════╝
+
+MODEL
+  Architecture : MamaGuard-Mamba3 (Trapezoidal SSM + MIMO + Complex state)
+  Parameters   : ~287,815
+  Input        : Sequence of prenatal visits (up to 5) × 6 vital signs
+  Output       : 3-class risk prediction (Low / Medium / High)
+  Dataset      : UCI Maternal Health Risk (1,014 rows)
+  Training set : {n_train} sequences
+  Validation set: {n_val} sequences
+
+──────────────────────────────────────────────────────────────────────────
+PERFORMANCE METRICS (on held-out validation set)
+──────────────────────────────────────────────────────────────────────────
+
+  Overall accuracy     : {overall_acc:.3f} ({overall_acc*100:.1f}%)
+
+  ROC-AUC per class:
+    Low risk           : {aucs[0]:.3f}
+    Medium risk        : {aucs[1]:.3f}
+    High risk          : {aucs[2]:.3f}
+
+  ** HIGH RISK RECALL (most critical for patient safety):
+    {hr_recall:.3f} — of all truly high-risk patients, {hr_recall*100:.1f}% were correctly flagged
+
+  ** HIGH RISK PRECISION (alarm fatigue indicator):
+    {hr_precision:.3f} — of all patients flagged high-risk, {hr_precision*100:.1f}% truly were
+
+  Detailed per-class report:
+{class_report}
+
+──────────────────────────────────────────────────────────────────────────
+CONFUSION MATRIX (raw counts)
+──────────────────────────────────────────────────────────────────────────
+  Rows = true label, Columns = predicted label
+
+                 Pred: Low   Pred: Mid   Pred: High
+  True: Low        {cm[0,0]:5d}       {cm[0,1]:5d}        {cm[0,2]:5d}
+  True: Mid        {cm[1,0]:5d}       {cm[1,1]:5d}        {cm[1,2]:5d}
+  True: High       {cm[2,0]:5d}       {cm[2,1]:5d}        {cm[2,2]:5d}
+
+  Most dangerous mistakes (False Negatives for High Risk):
+    High-risk patients predicted as Low risk  : {cm[2,0]}
+    High-risk patients predicted as Mid risk  : {cm[2,1]}
+
+──────────────────────────────────────────────────────────────────────────
+HYBRID SYSTEM NOTE
+──────────────────────────────────────────────────────────────────────────
+  MamaGuard uses a HYBRID architecture:
+    1. Mamba3 model: handles subtle temporal patterns (learned from data)
+    2. WHO clinical rules: hard overrides for obvious danger signs
+       - Rule 1: SystolicBP >= 160 -> RED
+       - Rule 2: SystolicBP >= 140 -> AMBER minimum
+       - Rule 3: Blood sugar > 11.1 -> AMBER minimum
+       - Rule 4: BP rise >= 20 mmHg -> AMBER minimum
+       - Rule 5: 3+ vitals escalating simultaneously -> RED
+
+  The metrics above reflect the NEURAL MODEL ONLY (without clinical rules).
+  In deployment, the clinical rules provide an additional safety floor,
+  meaning real-world recall for high-risk cases is higher than shown above.
+
+──────────────────────────────────────────────────────────────────────────
+LIMITATIONS
+──────────────────────────────────────────────────────────────────────────
+  1. SMALL DATASET: Trained on 1,014 rows from a single UCI dataset.
+     Real-world clinical models typically require 10,000–100,000+ samples.
+
+  2. SYNTHETIC SEQUENCES: The UCI dataset has no patient IDs or timestamps.
+     We created artificial 5-visit sequences by sorting rows by age.
+     These do not represent real patient trajectories.
+
+  3. NOT CLINICALLY VALIDATED: This model has NOT been validated against
+     real patient outcomes in a clinical setting. It must NOT be used
+     for actual medical decisions without proper clinical validation trials.
+
+  4. POPULATION BIAS: The UCI dataset was collected from a specific
+     population. Performance may differ on patients from different
+     regions, ethnicities, or healthcare contexts.
+
+  5. RESEARCH PROTOTYPE: This is a proof-of-concept demonstrating the
+     application of Mamba3 SSMs to maternal health risk prediction.
+     The system design (alarm fatigue mitigation, resource-aware routing,
+     OCR auto-fill) represents the primary contribution of this work.
+
+──────────────────────────────────────────────────────────────────────────
+CITATION
+──────────────────────────────────────────────────────────────────────────
+  If you use this work, please cite:
+    MamaGuard: Maternal Mortality Early Warning using Mamba3 Sequential
+    State-Space Models with Clinical Safety Rules.
+    [Your Name], 2025. GitHub: [your-repo-url]
+    Based on: Gu & Dao (2023) Mamba; UCI Maternal Health Risk dataset.
+
+══════════════════════════════════════════════════════════════════════════
+"""
+    return report
+
+
+# ── Main ──────────────────────────────────────────────────────────────────────
+
+def evaluate():
+    print("\n" + "="*60)
+    print("  MamaGuard -- Model Evaluation")
+    print("="*60 + "\n")
+
+    for path, name in [(MODEL_PATH, "model"), (SCALER_PATH, "scaler"), (CSV_PATH, "dataset")]:
+        if not os.path.exists(path):
+            print(f"ERROR: {name} not found at {path}")
+            print("Run python -m src.train first.")
+            return
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Device: {device}")
+
+    # Load data
+    print("\nLoading and preprocessing data...")
+    (X_train, y_train, q_train,
+     X_val, y_val, q_val,
+     scaler) = load_and_preprocess(CSV_PATH)
+
+    n_train = len(X_train)
+    n_val   = len(X_val)
+
+    print(f"Validation set: {n_val} sequences")
+    print(f"Class distribution in validation: "
+          f"Low={sum(y_val==0)} Mid={sum(y_val==1)} High={sum(y_val==2)}")
+
+    # Load model and predict
+    print("\nLoading model...")
+    model = load_model(device)
+
+    print("Running inference on validation set...")
+    y_pred, y_proba = get_predictions(model, X_val, device)
+
+    # Compute metrics
+    print("\nComputing metrics...")
+    overall_acc  = accuracy_score(y_val, y_pred)
+    cm           = confusion_matrix(y_val, y_pred)
+    class_report = classification_report(
+        y_val, y_pred,
+        target_names=CLASS_NAMES,
+        digits=3
+    )
+
+    print(f"\n{'-'*50}")
+    print(f"  Overall Accuracy: {overall_acc:.3f} ({overall_acc*100:.1f}%)")
+    print(f"{'-'*50}")
+    print("\nPer-class metrics:")
+    print(class_report)
+
+    print("Confusion matrix (rows=true, cols=predicted):")
+    header = f"{'':15s} {'Low':>8s} {'Mid':>8s} {'High':>8s}"
+    print(header)
+    for i, name in enumerate(CLASS_NAMES):
+        row = f"  {name:13s} " + " ".join(f"{cm[i,j]:8d}" for j in range(3))
+        print(row)
+
+    hr_recall = cm[2, 2] / max(cm[2, :].sum(), 1)
+    print(f"\n** HIGH RISK RECALL: {hr_recall:.3f}")
+    if hr_recall < 0.60:
+        print("   [!] WARNING: Less than 60% of high-risk patients detected by model alone.")
+        print("   The WHO clinical rules provide the safety floor in deployment.")
+    elif hr_recall < 0.75:
+        print("   [!] Moderate. Consider retraining with more data (Path B).")
+    else:
+        print("   [OK] Good recall -- model is learning the high-risk pattern.")
+
+    # Save plots
+    print(f"\nSaving evaluation plots...")
+    os.makedirs("models", exist_ok=True)
+    plot_confusion_matrix(cm, CM_PATH)
+    plot_roc_curves(y_val, y_proba, ROC_PATH)
+
+    # Save text report
+    report_text = build_text_report(
+        y_val, y_pred, y_proba,
+        class_report, cm, n_train, n_val
+    )
+    with open(REPORT_PATH, "w", encoding="utf-8") as f:
+        f.write(report_text)
+    print(f"  Full report saved -> {REPORT_PATH}")
+
+    # Final summary
+    print(f"\n{'='*60}")
+    print("  EVALUATION COMPLETE")
+    print(f"{'='*60}")
+    print(f"\n  Files saved:")
+    print(f"    {REPORT_PATH}   <- paste into Hugging Face model card")
+    print(f"    {CM_PATH}       <- include in LinkedIn post")
+    print(f"    {ROC_PATH}      <- include in GitHub README")
+
+    print(f"\n  Overall accuracy : {overall_acc*100:.1f}%")
+    print(f"  High-risk recall : {hr_recall*100:.1f}%")
+
+    if hr_recall < 0.60:
+        print("\n  RECOMMENDATION: Retrain with augmented data before publishing.")
+        print("  Current model relies heavily on WHO clinical rules for safety.")
+        print("  This is still publishable as a research prototype -- be transparent.")
+    else:
+        print("\n  RECOMMENDATION: Model is ready to publish as research prototype.")
+        print("  Include the evaluation_report.txt in your model card.")
+
+    print()
+
+
+if __name__ == "__main__":
+    evaluate()

src/explainability.py

@@ -0,0 +1,95 @@
+"""
+MamaGuard — Explainability
+Gradient-based attribution for per-feature and per-visit importance.
+"""
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+from src.model import MamaGuardMamba3
+
+FEATURE_NAMES = ['Age', 'SystolicBP', 'DiastolicBP', 'BloodSugar', 'BodyTemp', 'HeartRate']
+RISK_LABELS   = ['Low risk', 'Medium risk', 'High risk']
+RISK_EMOJI    = ['🟢', '🟡', '🔴']
+
+
+def explain_prediction(
+    model: MamaGuardMamba3,
+    x_sequence: np.ndarray,
+    scaler,
+    device: str = "cpu"
+) -> dict:
+    """
+    Runs the model on one patient and returns an explanation dict with:
+    risk_level, probabilities, confidence, top_reasons,
+    feature_importance, and visit_importance.
+    """
+    model.eval()
+
+    x_tensor = torch.tensor(x_sequence, dtype=torch.float32).unsqueeze(0).to(device)
+    x_tensor.requires_grad_(True)
+
+    # Forward pass
+    logits = model(x_tensor)
+    probs  = F.softmax(logits, dim=-1)
+
+    pred_class = probs.argmax(dim=-1).item()
+    confidence = probs[0, pred_class].item()
+
+    # Gradient attribution
+    score = logits[0, pred_class]
+    score.backward()
+
+    grads = x_tensor.grad[0].cpu().numpy()
+    attribution = np.abs(grads)
+
+    # Per-feature importance (average over visits)
+    feature_importance = attribution.mean(axis=0)
+    feature_importance = feature_importance / (feature_importance.sum() + 1e-9)
+
+    # Per-visit importance (average over features)
+    visit_importance = attribution.mean(axis=1)
+    visit_importance = visit_importance / (visit_importance.sum() + 1e-9)
+
+    # Build human-readable top reasons
+    sorted_features = sorted(
+        zip(FEATURE_NAMES, feature_importance),
+        key=lambda x: x[1], reverse=True
+    )
+    top_reasons = []
+
+    x_orig = scaler.inverse_transform(x_sequence)
+
+    for feat, importance in sorted_features[:2]:
+        feat_idx = FEATURE_NAMES.index(feat)
+        vals = x_orig[:, feat_idx]
+        trend = vals[-1] - vals[0]
+
+        if abs(trend) > 0.5:
+            direction = "rising" if trend > 0 else "falling"
+            top_reasons.append(
+                f"{feat} is {direction} (from {vals[0]:.1f} to {vals[-1]:.1f})"
+            )
+        else:
+            top_reasons.append(
+                f"{feat} is consistently elevated (avg {vals.mean():.1f})"
+            )
+
+    # Assemble result
+    probs_np = probs[0].detach().cpu().numpy()
+
+    return {
+        "risk_level":         RISK_LABELS[pred_class],
+        "risk_emoji":         RISK_EMOJI[pred_class],
+        "probabilities": {
+            label: round(float(p), 4)
+            for label, p in zip(RISK_LABELS, probs_np)
+        },
+        "confidence":        round(confidence, 4),
+        "top_reasons":       top_reasons,
+        "feature_importance": {
+            feat: round(float(imp), 4)
+            for feat, imp in zip(FEATURE_NAMES, feature_importance)
+        },
+        "visit_importance":  [round(float(v), 4) for v in visit_importance],
+    }

src/model.py

@@ -0,0 +1,171 @@
+"""
+MamaGuard — Mamba3 Model
+Trapezoidal SSM with MIMO expansion and complex-valued state.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Mamba3SSMLayer(nn.Module):
+    """Core recurrent SSM engine of one Mamba3 block."""
+
+    def __init__(self, d_model: int, d_state: int = 32, expand: int = 2):
+        super().__init__()
+        self.d_model = d_model
+        self.d_state = d_state
+        self.d_inner = d_model * expand
+
+        # Input/output projections (MIMO)
+        self.in_proj  = nn.Linear(d_model, self.d_inner * 2, bias=False)
+        self.out_proj = nn.Linear(self.d_inner, d_model, bias=False)
+
+        # Local depthwise convolution
+        self.conv1d = nn.Conv1d(
+            in_channels=self.d_inner,
+            out_channels=self.d_inner,
+            kernel_size=3,
+            padding=1,
+            groups=self.d_inner,
+            bias=True
+        )
+
+        # SSM parameters
+        self.A_log = nn.Parameter(torch.randn(self.d_inner, d_state))
+        self.D = nn.Parameter(torch.ones(self.d_inner))
+
+        # Input-dependent (selective) parameters: B, C, and Δ
+        self.x_proj = nn.Linear(self.d_inner, d_state * 2 + 1, bias=False)
+        self.dt_proj = nn.Linear(1, self.d_inner, bias=True)
+
+        # Trapezoidal blending parameter (α)
+        self.alpha = nn.Parameter(torch.tensor(0.5))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """x: (batch_size, seq_len, d_model) -> same shape output."""
+        B, L, _ = x.shape
+
+        # Project to inner dimension + gating signal
+        xz = self.in_proj(x)
+        x_in, z = xz.chunk(2, dim=-1)
+
+        # Local convolution + SiLU activation
+        x_conv = self.conv1d(x_in.transpose(1, 2)).transpose(1, 2)
+        x_conv = F.silu(x_conv)
+
+        # Compute input-dependent SSM parameters
+        dt_raw, B_ssm, C_ssm = self.x_proj(x_conv).split(
+            [1, self.d_state, self.d_state], dim=-1
+        )
+
+        dt = F.softplus(self.dt_proj(dt_raw))
+        A_real = -torch.exp(self.A_log)
+        alpha = torch.sigmoid(self.alpha)
+
+        # SSM recurrence
+        h = torch.zeros(B, self.d_inner, self.d_state, device=x.device)
+        outputs = []
+
+        for t in range(L):
+            dt_t = dt[:, t, :].unsqueeze(-1)
+            B_t  = B_ssm[:, t, :].unsqueeze(1)
+            C_t  = C_ssm[:, t, :].unsqueeze(1)
+            u_t  = x_conv[:, t, :]
+
+            # Trapezoidal discretization: blend ZOH + Implicit Euler
+            A_d_zoh   = torch.exp(A_real * dt_t)
+            A_d_euler = 1.0 / (1.0 - A_real * dt_t * 0.5 + 1e-6)
+            A_d = alpha * A_d_zoh + (1.0 - alpha) * A_d_euler
+
+            # State update + output
+            h = A_d * h + dt_t * B_t * u_t.unsqueeze(-1)
+            y_t = (C_t * h).sum(dim=-1) + self.D * u_t
+            outputs.append(y_t)
+
+        y = torch.stack(outputs, dim=1)
+
+        # Apply gating and project back
+        y = y * F.silu(z)
+        return self.out_proj(y)
+
+
+class Mamba3Block(nn.Module):
+    """One complete Mamba3 processing block: LayerNorm -> SSM -> LayerNorm -> FFN."""
+
+    def __init__(self, d_model: int, d_state: int = 32):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(d_model)
+        self.ssm   = Mamba3SSMLayer(d_model, d_state)
+        self.norm2 = nn.LayerNorm(d_model)
+
+        self.ffn = nn.Sequential(
+            nn.Linear(d_model, d_model * 4),
+            nn.GELU(),
+            nn.Linear(d_model * 4, d_model),
+            nn.Dropout(p=0.1)
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.ssm(self.norm1(x))
+        x = x + self.ffn(self.norm2(x))
+        return x
+
+
+class MamaGuardMamba3(nn.Module):
+    """
+    Complete MamaGuard model.
+    Flow: raw vitals (6) -> embed -> 4 Mamba3 blocks -> pool -> classify (3 classes)
+    """
+
+    def __init__(
+        self,
+        input_dim:  int = 6,
+        d_model:    int = 64,
+        n_layers:   int = 4,
+        n_classes:  int = 3,
+        d_state:    int = 32,
+    ):
+        super().__init__()
+
+        self.input_proj = nn.Sequential(
+            nn.Linear(input_dim, d_model),
+            nn.LayerNorm(d_model),
+        )
+
+        self.blocks = nn.ModuleList([
+            Mamba3Block(d_model, d_state) for _ in range(n_layers)
+        ])
+
+        self.norm_out = nn.LayerNorm(d_model)
+
+        self.classifier = nn.Sequential(
+            nn.Linear(d_model, d_model // 2),
+            nn.GELU(),
+            nn.Dropout(p=0.2),
+            nn.Linear(d_model // 2, n_classes)
+        )
+
+    def forward(self, x: torch.Tensor, return_features: bool = False):
+        """
+        x: (batch_size, seq_len, input_dim)
+        Returns: logits (batch_size, n_classes)
+        """
+        x = self.input_proj(x)
+
+        for block in self.blocks:
+            x = block(x)
+
+        x = self.norm_out(x)
+        features = x.mean(dim=1)       # global average pool over time
+        logits = self.classifier(features)
+
+        if return_features:
+            return logits, features
+        return logits
+
+    def predict_proba(self, x: torch.Tensor):
+        """Returns probabilities (after softmax) instead of logits."""
+        with torch.no_grad():
+            logits = self.forward(x)
+            return F.softmax(logits, dim=-1)

src/train.py

@@ -0,0 +1,137 @@
+"""
+MamaGuard — Training Loop
+Trains the Mamba3 model with class-weighted loss and LR scheduling.
+"""
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import pickle
+import os
+from src.model import MamaGuardMamba3
+from src.data_pipeline import get_dataloaders
+
+MODEL_SAVE_PATH  = "models/mamaguard_mamba3.pt"
+SCALER_SAVE_PATH = "models/scaler.pkl"
+os.makedirs("models", exist_ok=True)
+
+
+def compute_class_weights(train_loader):
+    """Compute inverse-frequency class weights with 3× boost for high-risk."""
+    all_labels = []
+    for _, y, _ in train_loader:
+        all_labels.extend(y.numpy())
+
+    counts = np.bincount(all_labels, minlength=3)
+    total  = counts.sum()
+
+    weights = total / (3 * counts + 1e-6)
+    weights[2] *= 3.0   # high-risk class boost
+    print(f"Class weights: LOW={weights[0]:.2f}, MID={weights[1]:.2f}, HIGH={weights[2]:.2f}")
+    return torch.tensor(weights, dtype=torch.float32)
+
+
+def train(
+    csv_path:   str   = "data/maternal_health.csv",
+    epochs:     int   = 50,
+    batch_size: int   = 32,
+    lr:         float = 1e-3,
+    device:     str   = None
+):
+    """Full training loop with validation and best-model checkpointing."""
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Training on: {device}")
+
+    # Load data
+    train_loader, val_loader, scaler = get_dataloaders(csv_path, batch_size)
+
+    with open(SCALER_SAVE_PATH, "wb") as f:
+        pickle.dump(scaler, f)
+    print(f"Scaler saved to {SCALER_SAVE_PATH}")
+
+    # Build model
+    model = MamaGuardMamba3(
+        input_dim=6, d_model=64, n_layers=4, n_classes=3, d_state=32
+    ).to(device)
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Model parameters: {total_params:,}")
+
+    # Loss function with class weights
+    class_weights = compute_class_weights(train_loader).to(device)
+    criterion = nn.CrossEntropyLoss(weight=class_weights)
+
+    # Optimizer + scheduler
+    optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
+    scheduler = optim.lr_scheduler.ReduceLROnPlateau(
+        optimizer, mode="min", factor=0.5, patience=5,
+    )
+
+    # Training loop
+    best_val_loss = float("inf")
+    best_val_acc  = 0.0
+
+    for epoch in range(1, epochs + 1):
+        # Training phase
+        model.train()
+        train_loss, train_correct, train_total = 0.0, 0, 0
+
+        for X_batch, y_batch, _ in train_loader:
+            X_batch = X_batch.to(device)
+            y_batch = y_batch.to(device)
+
+            optimizer.zero_grad()
+            logits = model(X_batch)
+            loss = criterion(logits, y_batch)
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
+            optimizer.step()
+
+            train_loss += loss.item()
+            preds = logits.argmax(dim=-1)
+            train_correct += (preds == y_batch).sum().item()
+            train_total   += len(y_batch)
+
+        # Validation phase
+        model.eval()
+        val_loss, val_correct, val_total = 0.0, 0, 0
+
+        with torch.no_grad():
+            for X_batch, y_batch, _ in val_loader:
+                X_batch = X_batch.to(device)
+                y_batch = y_batch.to(device)
+                logits  = model(X_batch)
+                loss    = criterion(logits, y_batch)
+                val_loss   += loss.item()
+                preds = logits.argmax(dim=-1)
+                val_correct += (preds == y_batch).sum().item()
+                val_total   += len(y_batch)
+
+        avg_train_loss = train_loss / len(train_loader)
+        avg_val_loss   = val_loss   / len(val_loader)
+        train_acc      = train_correct / train_total
+        val_acc        = val_correct   / val_total
+
+        print(
+            f"Epoch {epoch:3d}/{epochs} | "
+            f"Train Loss: {avg_train_loss:.4f} Acc: {train_acc:.3f} | "
+            f"Val Loss: {avg_val_loss:.4f} Acc: {val_acc:.3f}"
+        )
+
+        scheduler.step(avg_val_loss)
+
+        if val_acc > best_val_acc:
+            best_val_acc  = val_acc
+            best_val_loss = avg_val_loss
+            torch.save(model.state_dict(), MODEL_SAVE_PATH)
+            print(f"  * Best model saved (val_acc={val_acc:.3f})")
+
+    print(f"\nTraining complete. Best val accuracy: {best_val_acc:.3f}")
+    print(f"Model saved to: {MODEL_SAVE_PATH}")
+    return model
+
+
+if __name__ == "__main__":
+    train()