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app.py

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+"""
+MULTI-SITE NON-INVASIVE ANEMIA SCREENING
+=========================================
+Palpebral (required) + Buccal (optional) + Palm (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"
+YOLO_PALM = "palm_best.pt"
+DATA_LOG = "collected_data.csv"
+
+# ============================================================
+# LOAD MODELS
+# ============================================================
+def load_models():
+    global ml_model, feature_names, yolo_palp, yolo_bucc, yolo_palm
+    
+    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")
+    
+    try:
+        yolo_palm = YOLO(YOLO_PALM)
+        print("Palm YOLO loaded")
+    except:
+        yolo_palm = None
+        print("Palm 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, img_palm,
+    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')
+    
+    # Palm (optional)
+    if img_palm is not None and yolo_palm:
+        palm_feat = extract_features(img_palm, yolo_palm, 'palm')
+        if palm_feat:
+            all_features.update(palm_feat)
+            sites.append('palm')
+    
+    # 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,
+        'palm_R': 170, 'palm_G': 130, 'palm_B': 120,
+        'palm_H': 12, 'palm_S': 70, 'palm_V': 170,
+        'palm_L_color': 150, 'palm_A_color': 140, 'palm_B_color': 130,
+    }
+    
+    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("# 🩺 Multi-Site Anemia Screening\nPalpebral required. Buccal & Palm 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")
+                img_palm = gr.Image(label="Palm (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, img_palm, 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()

buccal_best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:abc15008c5c9f6a6c93d25e77b41c1e74192b73333c4c4f43269853f42316b4d
+size 6782196

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3219978d7aaee438012322843641dc3c683fbec6770b588d886a84015fb56336
+size 1315168

palm_best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b7d10d54fcf18b06bff467c60324fcbb8b400f01fb9f61c303a94ecf4acc917
+size 6782132