""" NON-INVASIVE ANEMIA SCREENING - Palpebral & Buccal =================================================== Palpebral conjunctiva (required) + Buccal mucosa (optional) """ import gradio as gr import numpy as np import pandas as pd import pickle import cv2 import uuid from datetime import datetime from pathlib import Path # ============================================================ # CONFIG # ============================================================ MODEL_PATH = "model.pkl" YOLO_PALP = "best.pt" YOLO_BUCC = "buccal_best.pt" DATA_LOG = "collected_data.csv" # ============================================================ # LOAD MODELS # ============================================================ def load_models(): global ml_model, feature_names, yolo_palp, yolo_bucc with open(MODEL_PATH, 'rb') as f: data = pickle.load(f) ml_model = data['model'] feature_names = data['features'] print(f"ML model: {data.get('name', 'Unknown')}, {len(feature_names)} features") from ultralytics import YOLO yolo_palp = YOLO(YOLO_PALP) print("Palpebral YOLO loaded") try: yolo_bucc = YOLO(YOLO_BUCC) print("Buccal YOLO loaded") except: yolo_bucc = None print("Buccal YOLO not found") # ============================================================ # IMAGE PROCESSING # ============================================================ def calibrate_image(img): try: if img is None: return None if len(img.shape) == 2: img_rgb = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) elif img.shape[2] == 4: img_rgb = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB) else: img_rgb = img.copy() gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY) _, thresh = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY) contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if not contours: return img_rgb c = max(contours, key=cv2.contourArea) x, y, w, h = cv2.boundingRect(c) if w * h < 1000: return img_rgb patch = img_rgb[y+h//4:y+3*h//4, x+w//4:x+3*w//4] if patch.size == 0: return img_rgb avg = np.mean(patch, axis=(0, 1)) if any(v < 10 for v in avg): return img_rgb scale = np.array([128, 128, 128]) / avg return np.clip(img_rgb * scale, 0, 255).astype(np.uint8) except: return img def extract_features(img, yolo, prefix): if img is None or yolo is None: return None try: img_cal = calibrate_image(img) if img_cal is None: return None results = yolo(img_cal, verbose=False) if len(results) > 0 and results[0].masks is not None: masks = results[0].masks.data.cpu().numpy() confs = results[0].boxes.conf.cpu().numpy() if len(confs) > 0: m = masks[np.argmax(confs)] m = cv2.resize(m, (img_cal.shape[1], img_cal.shape[0])) mask = (m > 0.5).astype(np.uint8) * 255 else: h, w = img_cal.shape[:2] mask = np.zeros((h, w), dtype=np.uint8) mask[h//4:3*h//4, w//4:3*w//4] = 255 else: h, w = img_cal.shape[:2] mask = np.zeros((h, w), dtype=np.uint8) mask[h//4:3*h//4, w//4:3*w//4] = 255 px = img_cal[mask > 0] if len(px) < 100: return None R, G, B = [np.mean(px[:, i]) for i in range(3)] hsv = cv2.cvtColor(img_cal, cv2.COLOR_RGB2HSV) H, S, V = [np.mean(hsv[mask > 0][:, i]) for i in range(3)] lab = cv2.cvtColor(img_cal, cv2.COLOR_RGB2LAB) L, A, Bc = [np.mean(lab[mask > 0][:, i]) for i in range(3)] return { f'{prefix}_R': R, f'{prefix}_G': G, f'{prefix}_B': B, f'{prefix}_H': H, f'{prefix}_S': S, f'{prefix}_V': V, f'{prefix}_L_color': L, f'{prefix}_A_color': A, f'{prefix}_B_color': Bc } except Exception as e: print(f"Error {prefix}: {e}") return None # ============================================================ # FORM TO FEATURES # ============================================================ def form_to_features(age, income_range, family_size, diet, food_grains, food_leafy, food_cereals, food_pulses, food_fish, food_eggs, food_meat, food_jaggery, food_nuts, fruit_freq, veg_freq, tea_coffee, comorb_dm, comorb_htn, comorb_thyroid, prev_anemia, family_anemia, hookworm, bleeding_disorder, deworming, menstrual, pads_per_day, gravida, para, live_births, abortions, stillbirths, family_dm, family_htn): income_map = {"Below ₹5,000": 2500, "₹5,000 - ₹10,000": 7500, "₹10,000 - ₹15,000": 12500, "₹15,000 - ₹25,000": 20000, "₹25,000 - ₹50,000": 37500, "Above ₹50,000": 75000} freq_map = {"Daily": 4, "Twice a week": 3, "Once a week": 2, "Rarely": 1, "Never": 0} pads_map = {"1-2": 1.5, "2-3": 2.5, "3-4": 3.5, "4-5": 4.5, "More than 5": 6, "Not applicable": 2.5} income = income_map.get(income_range, 20000) return { 'age': age, 'income': income, 'family_size': family_size, 'income_per_capita': income / max(family_size, 1), 'diet_veg': 1 if diet == "Vegetarian" else 0, 'diet_nonveg': 1 if diet == "Non-vegetarian" else 0, 'diet_mixed': 1 if diet == "Mixed" else 0, 'food_whole_grains': int(food_grains), 'food_green_leafy_vegetables': int(food_leafy), 'food_cereals': int(food_cereals), 'food_pulses': int(food_pulses), 'food_fish': int(food_fish), 'food_eggs': int(food_eggs), 'food_meat': int(food_meat), 'food_jaggery': int(food_jaggery), 'food_nuts': int(food_nuts), 'food_iron_count': sum([food_grains, food_leafy, food_cereals, food_pulses, food_fish, food_eggs, food_meat, food_jaggery, food_nuts]), 'fruit_freq': freq_map.get(fruit_freq, 2), 'veg_freq': freq_map.get(veg_freq, 2), 'tea_coffee_daily': 1 if tea_coffee == "Yes" else 0, 'tea_coffee_daily_missing': 0, 'comorb_dm': int(comorb_dm), 'comorb_htn': int(comorb_htn), 'comorb_thyroid': int(comorb_thyroid), 'comorb_none': 1 if not any([comorb_dm, comorb_htn, comorb_thyroid]) else 0, 'comorb_count': sum([int(comorb_dm), int(comorb_htn), int(comorb_thyroid)]), 'prev_anemia': 1 if prev_anemia == "Yes" else 0, 'prev_anemia_missing': 0, 'family_anemia': 1 if family_anemia == "Yes" else 0, 'family_anemia_missing': 0, 'hookworm_history': 1 if hookworm == "Yes" else 0, 'hookworm_history_missing': 0, 'bleeding_disorder': 1 if bleeding_disorder == "Yes" else 0, 'bleeding_disorder_missing': 0, 'deworming_done': 1 if deworming == "Yes" else 0, 'deworming_done_missing': 0, 'menstrual_regular': 1 if menstrual == "Regular" else 0, 'menstrual_irregular': 1 if menstrual == "Irregular" else 0, 'pads_per_day': pads_map.get(pads_per_day, 2.5), 'pads_missing': 0, 'gravida': gravida, 'para': para, 'live_births': live_births, 'abortions': abortions, 'stillbirths': stillbirths, 'has_obstetric_history': 1 if gravida > 0 else 0, 'pregnancy_loss': abortions + stillbirths, 'parity_ratio': para / gravida if gravida > 0 else 0, 'family_dm': 1 if family_dm == "Yes" else 0, 'family_dm_missing': 0, 'family_htn': 1 if family_htn == "Yes" else 0, 'family_htn_missing': 0, } # ============================================================ # PREDICTION # ============================================================ def predict_hb(img_palp, img_bucc, age, income_range, family_size, diet, food_grains, food_leafy, food_cereals, food_pulses, food_fish, food_eggs, food_meat, food_jaggery, food_nuts, fruit_freq, veg_freq, tea_coffee, comorb_dm, comorb_htn, comorb_thyroid, prev_anemia, family_anemia, hookworm, bleeding_disorder, deworming, menstrual, pads_per_day, gravida, para, live_births, abortions, stillbirths, family_dm, family_htn, actual_hb): session_id = str(uuid.uuid4())[:8] sites = [] if img_palp is None: return "❌ Palpebral image required.", "", "" all_features = {} # Palpebral (required) palp_feat = extract_features(img_palp, yolo_palp, 'palpebral') if palp_feat is None: return "❌ Could not process palpebral image.", "", "" all_features.update(palp_feat) sites.append('palpebral') # Buccal (optional) if img_bucc is not None and yolo_bucc: bucc_feat = extract_features(img_bucc, yolo_bucc, 'buccal') if bucc_feat: all_features.update(bucc_feat) sites.append('buccal') # Tabular tab_feat = form_to_features(age, income_range, family_size, diet, food_grains, food_leafy, food_cereals, food_pulses, food_fish, food_eggs, food_meat, food_jaggery, food_nuts, fruit_freq, veg_freq, tea_coffee, comorb_dm, comorb_htn, comorb_thyroid, prev_anemia, family_anemia, hookworm, bleeding_disorder, deworming, menstrual, pads_per_day, gravida, para, live_births, abortions, stillbirths, family_dm, family_htn) all_features.update(tab_feat) # Default values for missing image features defaults = { 'palpebral_R': 150, 'palpebral_G': 100, 'palpebral_B': 100, 'palpebral_H': 10, 'palpebral_S': 100, 'palpebral_V': 150, 'palpebral_L_color': 130, 'palpebral_A_color': 150, 'palpebral_B_color': 140, 'buccal_R': 160, 'buccal_G': 110, 'buccal_B': 110, 'buccal_H': 8, 'buccal_S': 90, 'buccal_V': 160, 'buccal_L_color': 140, 'buccal_A_color': 145, 'buccal_B_color': 135, } for f in feature_names: if f not in all_features: all_features[f] = defaults.get(f, 0) X = pd.DataFrame([all_features])[feature_names] hb = round(float(ml_model.predict(X)[0]), 1) if hb >= 12: sev, col, rec = "Normal", "🟢", "No anemia detected." elif hb >= 11: sev, col, rec = "Mild Anemia", "🟡", "Consider iron-rich foods." elif hb >= 8: sev, col, rec = "Moderate Anemia", "🟠", "Consult healthcare provider." else: sev, col, rec = "Severe Anemia", "🔴", "Seek immediate medical attention." # Log try: record = {'session_id': session_id, 'timestamp': datetime.now().isoformat(), 'predicted_hb': hb, 'actual_hb': actual_hb if actual_hb > 0 else None, 'sites': ','.join(sites), **all_features} df = pd.DataFrame([record]) if Path(DATA_LOG).exists(): df.to_csv(DATA_LOG, mode='a', header=False, index=False) else: df.to_csv(DATA_LOG, index=False) except: pass result = f"## {col} Estimated Hb: {hb} g/dL\n### {sev}\n{rec}\n\n---\n**Sites:** {', '.join(sites)} | **Session:** {session_id}" return result, session_id, f"{hb} g/dL" # ============================================================ # INTERFACE # ============================================================ def create_app(): with gr.Blocks(title="Anemia Screening", theme=gr.themes.Soft()) as app: gr.Markdown("# 🩺 Non-Invasive Anemia Screening\n**Palpebral conjunctiva required. Buccal mucosa optional.**") with gr.Row(): with gr.Column(scale=1): img_palp = gr.Image(label="Palpebral Conjunctiva (Required)", type="numpy") img_bucc = gr.Image(label="Buccal Mucosa (Optional)", type="numpy") with gr.Column(scale=2): with gr.Accordion("Demographics", open=True): with gr.Row(): age = gr.Slider(10, 80, 25, step=1, label="Age") family_size = gr.Slider(1, 15, 4, step=1, label="Family Size") income_range = gr.Dropdown(["Below ₹5,000", "₹5,000 - ₹10,000", "₹10,000 - ₹15,000", "₹15,000 - ₹25,000", "₹25,000 - ₹50,000", "Above ₹50,000"], value="₹10,000 - ₹15,000", label="Income") with gr.Accordion("Diet", open=True): diet = gr.Radio(["Vegetarian", "Non-vegetarian", "Mixed"], value="Mixed", label="Diet") with gr.Row(): food_grains = gr.Checkbox(label="Whole Grains", value=True) food_leafy = gr.Checkbox(label="Leafy Veg", value=True) food_cereals = gr.Checkbox(label="Cereals", value=True) with gr.Row(): food_pulses = gr.Checkbox(label="Pulses", value=True) food_fish = gr.Checkbox(label="Fish") food_eggs = gr.Checkbox(label="Eggs") with gr.Row(): food_meat = gr.Checkbox(label="Meat") food_jaggery = gr.Checkbox(label="Jaggery") food_nuts = gr.Checkbox(label="Nuts") with gr.Row(): fruit_freq = gr.Dropdown(["Daily", "Twice a week", "Once a week", "Rarely", "Never"], value="Twice a week", label="Fruits") veg_freq = gr.Dropdown(["Daily", "Twice a week", "Once a week", "Rarely", "Never"], value="Daily", label="Vegetables") tea_coffee = gr.Radio(["Yes", "No"], value="Yes", label="Daily Tea/Coffee") with gr.Accordion("Medical History", open=False): with gr.Row(): comorb_dm = gr.Checkbox(label="Diabetes") comorb_htn = gr.Checkbox(label="Hypertension") comorb_thyroid = gr.Checkbox(label="Thyroid") with gr.Row(): prev_anemia = gr.Radio(["Yes", "No"], value="No", label="Anemia (past 12mo)") family_anemia = gr.Radio(["Yes", "No"], value="No", label="Family Anemia") with gr.Row(): hookworm = gr.Radio(["Yes", "No"], value="No", label="Hookworm") bleeding_disorder = gr.Radio(["Yes", "No"], value="No", label="Bleeding Disorder") deworming = gr.Radio(["Yes", "No"], value="Yes", label="Deworming Done") with gr.Row(): family_dm = gr.Radio(["Yes", "No"], value="No", label="Family Diabetes") family_htn = gr.Radio(["Yes", "No"], value="No", label="Family Hypertension") with gr.Accordion("Menstrual/Obstetric", open=False): with gr.Row(): menstrual = gr.Radio(["Regular", "Irregular", "Not applicable"], value="Regular", label="Menstrual") pads_per_day = gr.Dropdown(["1-2", "2-3", "3-4", "4-5", "More than 5", "Not applicable"], value="2-3", label="Pads/day") with gr.Row(): gravida = gr.Slider(0, 10, 0, step=1, label="Gravida") para = gr.Slider(0, 10, 0, step=1, label="Para") live_births = gr.Slider(0, 10, 0, step=1, label="Live Births") with gr.Row(): abortions = gr.Slider(0, 5, 0, step=1, label="Abortions") stillbirths = gr.Slider(0, 5, 0, step=1, label="Stillbirths") with gr.Row(): actual_hb = gr.Number(label="Actual Hb (optional)", value=0, minimum=0, maximum=25) submit = gr.Button("🔬 Estimate Hb", variant="primary", size="lg") result = gr.Markdown() with gr.Row(): session = gr.Textbox(label="Session ID", interactive=False) hb_out = gr.Textbox(label="Estimated Hb", interactive=False) gr.Markdown("---\n🔒 Anonymous data collection. No PII stored.") submit.click(predict_hb, [img_palp, img_bucc, age, income_range, family_size, diet, food_grains, food_leafy, food_cereals, food_pulses, food_fish, food_eggs, food_meat, food_jaggery, food_nuts, fruit_freq, veg_freq, tea_coffee, comorb_dm, comorb_htn, comorb_thyroid, prev_anemia, family_anemia, hookworm, bleeding_disorder, deworming, menstrual, pads_per_day, gravida, para, live_births, abortions, stillbirths, family_dm, family_htn, actual_hb], [result, session, hb_out]) return app if __name__ == "__main__": load_models() create_app().launch()