app.py

# ======================================================
# REMBG + HRNet Fashion Landmark Measurement - Streamlit UI
# ======================================================

import os
import sys
import json
import argparse
import gc
import datetime
import numpy as np
import streamlit as st
from PIL import Image, ImageOps
import sys
import os

# ------------------------------------------------------
# DYNAMIC PATH RESOLUTION & PATH INJECTIONS
# ------------------------------------------------------
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
if os.path.exists(os.path.join(BASE_DIR, "experiments")):
    HRNET_ROOT = BASE_DIR
else:
    parent_dir = os.path.dirname(BASE_DIR)
    if os.path.exists(os.path.join(parent_dir, "experiments")):
        HRNET_ROOT = parent_dir
    else:
        HRNET_ROOT = BASE_DIR

sys.path.insert(0, HRNET_ROOT)
sys.path.insert(0, os.path.join(HRNET_ROOT, "lib"))
sys.path.insert(0, os.path.join(HRNET_ROOT, "streamlit_app"))

try:
    from components.phone_tilt import pant_upload_ui
except ImportError:
    pant_upload_ui = None

import gdown
import io
import time
from dotenv import load_dotenv

# Load environment variables at the very beginning
load_dotenv()

# Diagnostic startup logging
print("[DIAGNOSTIC] Application started", flush=True)

try:
    import psutil
except ImportError:
    psutil = None

def log_memory(label):
    """

    Log application process memory (RSS in GB) for Community Cloud optimization.

    """
    if psutil is None:
        print(f"[MEMORY] {label}: psutil not installed", flush=True)
        return 0.0
    try:
        process = psutil.Process(os.getpid())
        rss_gb = process.memory_info().rss / (1024 ** 3)
        print(f"[MEMORY] {label}: {rss_gb:.3f} GB", flush=True)
        return rss_gb
    except Exception as e:
        print(f"[MEMORY] {label}: (Failed to get process memory: {e})", flush=True)
        return 0.0

log_memory("Clean Streamlit/Python startup")

# Defer heavy imports to optimize startup time
torch = None
cv2 = None
cloudinary = None
rembg = None

# ------------------------------------------------------
# DYNAMIC PATH RESOLUTION & PATH INJECTIONS
# ------------------------------------------------------
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
if os.path.exists(os.path.join(BASE_DIR, "experiments")):
    HRNET_ROOT = BASE_DIR
else:
    parent_dir = os.path.dirname(BASE_DIR)
    if os.path.exists(os.path.join(parent_dir, "experiments")):
        HRNET_ROOT = parent_dir
    else:
        HRNET_ROOT = BASE_DIR

# Insert paths into sys.path to ensure module resolution works from both root and streamlit_app
sys.path.insert(0, HRNET_ROOT)
sys.path.insert(0, os.path.join(HRNET_ROOT, "lib"))
sys.path.insert(0, os.path.join(HRNET_ROOT, "streamlit_app"))

# ------------------------------------------------------
# SERVER-SIDE AUTH GUARD
# ------------------------------------------------------
import firebase_config
import auth
import firestore_service

# Initialize Firebase (Server-Side)
firebase_config.initialize_firebase()

is_embedded = st.query_params.get("embed") == "true"

# Auth is optional - try to verify token if provided, but don't block access
if "uid" not in st.session_state:
    token = st.query_params.get("token")
    if token:
        try:
            with st.spinner("Verifying session..."):
                user_uid = auth.verify_user_from_query()
                st.session_state["uid"] = user_uid
            if not is_embedded:
                st.query_params.clear()
                st.rerun()
        except Exception as e:
            # Token invalid - still allow access, just without Firestore save
            st.session_state["uid"] = None
    else:
        st.session_state["uid"] = None


# ------------------------------------------------------
# STREAMLIT CONFIG
# ------------------------------------------------------
st.set_page_config(
    page_title="DASH AI Fit + BG Remover",
    layout="centered",
    initial_sidebar_state="collapsed"
)

# Fullscreen listener
st.html("""

    <script>

    window.addEventListener('message', function(e) {

        if (!e.data) return;

        if (e.data.type === 'enter-fullscreen') {

            const iframes = window.parent.document.querySelectorAll('iframe[title="components.phone_tilt.phone_tilt"]');

            iframes.forEach(iframe => {

                iframe.style.position = 'fixed';

                iframe.style.top = '0';

                iframe.style.left = '0';

                iframe.style.width = '100vw';

                iframe.style.height = '100vh';

                iframe.style.zIndex = '999999';

                iframe.style.backgroundColor = '#000';

            });

        } else if (e.data.type === 'exit-fullscreen') {

            const iframes = window.parent.document.querySelectorAll('iframe[title="components.phone_tilt.phone_tilt"]');

            iframes.forEach(iframe => {

                iframe.style.position = '';

                iframe.style.top = '';

                iframe.style.left = '';

                iframe.style.width = '';

                iframe.style.height = '';

                iframe.style.zIndex = '';

                iframe.style.backgroundColor = '';

            });

        }

    });

    </script>

""")


# ------------------------------------------------------
# PREMIUM CSS INJECTION
# ------------------------------------------------------
CSS_CODE = r"""

<style>

    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap');

    

    html, body, [data-testid="stAppViewContainer"] {

        background-color: #f7f9fc !important;

        color: #09090b !important;

        font-family: 'Inter', sans-serif;

    }

    

    .block-container {

        max-width: 480px !important;

        padding-top: 3rem !important;

        padding-bottom: 5rem !important;

        margin: auto;

    }



    .header-container { text-align: center; margin-bottom: 2rem; }

    .main-subtitle { font-size: 1.1rem; font-weight: 500; color: #52525b; }

    .main-subtitle b { color: #000000; font-weight: 700; }



    .step-header {

        font-size: 0.75rem;

        font-weight: 700;

        color: #18181b !important;

        text-transform: uppercase;

        margin-bottom: 0.8rem;

        margin-top: 2rem;

        letter-spacing: 0.5px;

        display: flex;

        align-items: center;

        gap: 0.5rem;

        border-bottom: 1px solid #f4f4f5;

        padding-bottom: 0.5rem;

    }



    /* ----------------------------------------------------------------

       FILE UPLOADER - PREMIUM BLACK BORDER

       ---------------------------------------------------------------- */

    [data-testid="stFileUploader"] {

        background-color: #ffffff !important;

        border: 2px solid #000000 !important;

        border-radius: 24px !important;

        min-height: 250px !important; 

        position: relative !important;

        box-shadow: 0 10px 30px -10px rgba(0, 0, 0, 0.1) !important;

        padding: 0 !important;

        overflow: hidden !important;

        transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);

    }

    

    [data-testid="stFileUploader"]:hover {

        transform: translateY(-2px);

        box-shadow: 0 20px 40px -15px rgba(0, 0, 0, 0.15) !important;

    }

    

    [data-testid="stFileUploader"] section {

        position: absolute !important;

        top: 0;

        left: 0;

        width: 100% !important;

        height: 100% !important;

        z-index: 20;

        opacity: 0 !important;

        cursor: pointer !important;

    }



    /* Custom Arrow */

    [data-testid="stFileUploader"]::before {

        content: "\2B06"; 

        font-size: 4rem;

        position: absolute;

        top: 35%;

        left: 50%;

        transform: translate(-50%, -50%);

        z-index: 10;

        pointer-events: none;

        color: #000000 !important;

        display: block !important;

        visibility: visible !important;

        transition: transform 0.3s ease;

    }

    

    [data-testid="stFileUploader"]:hover::before {

        transform: translate(-50%, -60%);

    }

    

    /* Custom Text - Polished & Simplified */

    [data-testid="stFileUploader"]::after {

        content: "Upload flat laid pant image";

        text-align: center;

        font-family: 'Inter', sans-serif;

        font-weight: 700;

        font-size: 1.1rem;

        color: #000000 !important;

        position: absolute;

        top: 70%;

        left: 50%;

        transform: translate(-50%, -50%);

        width: 90%;

        z-index: 10;

        pointer-events: none;

        display: block !important;

        visibility: visible !important;

    }



    /* ----------------------------------------------------------------

       WAIST INPUT - MINIMALIST

       ---------------------------------------------------------------- */

    [data-testid="stNumberInput"] button {

        display: none !important;

    }

    input[type=number]::-webkit-inner-spin-button, 

    input[type=number]::-webkit-outer-spin-button { 

        -webkit-appearance: none; 

        margin: 0; 

    }

    input[type=number] {

        -moz-appearance: textfield;

    }



    .stNumberInput input {

        text-align: center !important;

        font-size: 1.75rem !important;

        font-weight: 800 !important;

        height: 64px !important;

        border-radius: 16px !important;

        border: 2px solid #e2e8f0 !important;

        background-color: #ffffff !important;

        color: #000000 !important;

        transition: all 0.2s;

    }

    .stNumberInput input:focus {

        border-color: #000000 !important;

        box-shadow: 0 0 0 4px rgba(0,0,0,0.05) !important;

    }



    /* Analyze Button */

    .stButton > button {

        width: 100%;

        background: #000000 !important;

        color: #ffffff !important;

        border: none !important;

        border-radius: 16px !important;

        padding: 1.25rem !important;

        font-size: 1.1rem !important;

        font-weight: 800 !important;

        text-transform: uppercase;

        letter-spacing: 1px;

        margin-top: 2rem;

        box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.2) !important;

        transition: all 0.3s ease;

    }

    .stButton > button:hover {

        transform: translateY(-2px);

        box-shadow: 0 15px 30px -10px rgba(0, 0, 0, 0.3) !important;

    }



    .social-proof {

        text-align: center;

        font-size: 0.9rem;

        color: #64748b !important;

        margin-top: 1.5rem;

        font-weight: 600; 

    }



    #MainMenu, footer, header {visibility: hidden;}

    .stDeployButton {display:none;}

    [data-testid="stHeader"] {display: none;}

</style>

"""
st.markdown(CSS_CODE, unsafe_allow_html=True)

# ------------------------------------------------------
# LAZY IMPORTS & RESOURCE CACHING
# ------------------------------------------------------
def get_cloudinary():
    global cloudinary
    if cloudinary is None:
        import cloudinary
        import cloudinary.uploader
    return cloudinary

def init_cloudinary():
    """Configure Cloudinary using environment variables."""
    cld = get_cloudinary()
    try:
        cld.config(
            cloud_name=os.getenv("CLOUDINARY_CLOUD_NAME"),
            api_key=os.getenv("CLOUDINARY_API_KEY"),
            api_secret=os.getenv("CLOUDINARY_API_SECRET"),
            secure=True
        )
        return True
    except Exception as e:
        st.error(f"Failed to initialize Cloudinary: {e}")
        return False

# ------------------------------------------------------
# REMBG MODEL PATHS & SETUP (STRICT SINGLE INSTANCE)
# ------------------------------------------------------
@st.cache_resource(show_spinner="Loading background removal model...")
def load_rembg_session(model_name="isnet-general-use"):
    """

    Load a single cached rembg session.

    """
    global rembg
    if rembg is None:
        import rembg

    print(f"[{model_name.upper()}] Starting initialization", flush=True)
    rss_before = log_memory(f"Before {model_name} initialization")

    try:
        import onnxruntime as ort
        so = ort.SessionOptions()
        so.intra_op_num_threads = 1
        so.inter_op_num_threads = 1
        so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
        
        session = rembg.new_session(model_name, sess_opts=so)
        print(f"[{model_name.upper()}] initialization completed", flush=True)
        rss_after = log_memory(f"After {model_name} initialization")
        delta = rss_after - rss_before
        print(f"[MEMORY] {model_name} initialization delta = {delta:.3f} GB", flush=True)
        return session, model_name
    except Exception as e:
        print(f"[{model_name.upper()}] FAILED: {e}", flush=True)
        import traceback
        traceback.print_exc()
        raise e

# ------------------------------------------------------
# SEGMENTATION MASK & LANDMARK REFINEMENT HELPERS
# ------------------------------------------------------
def load_mask_portrait(mask_np):
    """Rotate the mask to match portrait image orientation if landscape."""
    global cv2
    if cv2 is None:
        import cv2
    h, w = mask_np.shape[:2]
    if w > h:
        mask_np = cv2.rotate(mask_np, cv2.ROTATE_90_COUNTERCLOCKWISE)
    return mask_np

def refine_landmarks(pts, binary_mask, max_snap_dist_ratio=0.08):
    """

    Refines HRNet landmarks by snapping them to the closest outer boundary of the pants.

    Only boundary landmarks are snapped. WAIST_C (inside waistband) is ignored.

    """
    global cv2
    if cv2 is None:
        import cv2
        
    refined_pts = pts.copy()
    contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    if not contours:
        return refined_pts, 0.0
        
    largest_contour = max(contours, key=cv2.contourArea)
    contour_pts = largest_contour.reshape(-1, 2)
    
    H, W = binary_mask.shape[:2]
    max_snap_dist = max_snap_dist_ratio * max(H, W)
    
    boundary_landmark_ids = [
        WAIST_L, WAIST_R, 
        OUTER_ANKLE_L, OUTER_ANKLE_R, 
        INNER_ANKLE_L, INNER_ANKLE_R, 
        INSEAM
    ]
    
    snap_distances = []
    for lid in boundary_landmark_ids:
        if lid not in pts:
            continue
        pt = np.array(pts[lid], dtype=np.float32)
        dists = np.linalg.norm(contour_pts - pt, axis=1)
        min_idx = np.argmin(dists)
        min_dist = dists[min_idx]
        
        if min_dist <= max_snap_dist:
            refined_pts[lid] = (int(contour_pts[min_idx][0]), int(contour_pts[min_idx][1]))
            snap_distances.append(min_dist)
            
    avg_snap_dist = float(np.mean(snap_distances)) if snap_distances else 0.0
    return refined_pts, avg_snap_dist

def draw_landmarks_debug(img, orig_pts, refined_pts):
    """Draws original and refined landmarks on the image for visual verification."""
    global cv2
    if cv2 is None:
        import cv2
        
    draw = img.copy()
    boundary_landmark_ids = [
        WAIST_L, WAIST_R, 
        OUTER_ANKLE_L, OUTER_ANKLE_R, 
        INNER_ANKLE_L, INNER_ANKLE_R, 
        INSEAM
    ]
    
    # Draw original landmarks in red
    for lid, pt in orig_pts.items():
        color = (255, 0, 0) if lid in boundary_landmark_ids else (255, 165, 0)
        cv2.circle(draw, pt, 4, color, -1)
        
    # Draw refined landmarks in green
    for lid, pt in refined_pts.items():
        if lid in boundary_landmark_ids:
            cv2.circle(draw, pt, 4, (0, 255, 0), -1)
            # Draw line showing direction of shift
            if lid in orig_pts:
                cv2.line(draw, orig_pts[lid], pt, (0, 0, 255), 1)
                
    return draw


# ------------------------------------------------------
# HRNET PATHS & SETUP (STRICT SINGLE INSTANCE)
# ------------------------------------------------------
CFG_FILE = os.path.join(HRNET_ROOT, "experiments/deepfashion2/hrnet/w48_384x288_adam_lr1e-3.yaml")
MODEL_FILE = os.path.join(HRNET_ROOT, "lib/models/pose_hrnet-w48_384x288-deepfashion2_mAP_0.7017.pth")
MODEL_URL = "https://drive.google.com/file/d/1Srj8YJLu00aZm368sCtEIWB8Vkpr7kXA/view?usp=sharing"

def ensure_model_exists():
    if not os.path.exists(MODEL_FILE):
        os.makedirs(os.path.dirname(MODEL_FILE), exist_ok=True)
        with st.spinner("Initializing AI engine..."):
            gdown.download(MODEL_URL, MODEL_FILE, quiet=True)

@st.cache_resource(show_spinner="Loading HRNet Landmark Detection model...")
def load_hrnet():
    """

    Load and cache HRNet model. Uses @st.cache_resource so it stays in RAM

    between requests (avoids ~5–10 s reload on each analysis).

    """
    global torch, cv2
    if torch is None:
        import torch
    if cv2 is None:
        import cv2

    # Limit CPU threads before model creation to reduce RSS overhead on cloud VMs
    torch.set_num_threads(2)

    print("[HRNET] Starting HRNet initialization", flush=True)
    rss_before = log_memory("Before HRNet initialization")

    ensure_model_exists()
    from config import cfg, update_config
    import models
    
    args = argparse.Namespace(cfg=CFG_FILE, opts=[], modelDir="", logDir="", dataDir="", prevModelDir="")
    update_config(cfg, args)
    cfg.freeze()
    model = models.pose_hrnet.get_pose_net(cfg, is_train=False)
    model.load_state_dict(torch.load(MODEL_FILE, map_location="cpu"), strict=False)
    model.eval()

    rss_after = log_memory("After HRNet initialization")
    delta = rss_after - rss_before
    print(f"[MEMORY] HRNet initialization delta = {delta:.3f} GB", flush=True)
    return model

# ------------------------------------------------------
# AI HELPERS
# ------------------------------------------------------
def load_image_portrait(pil_img):
    global cv2
    if cv2 is None: import cv2
    
    img = np.array(pil_img.convert("RGB"))
    h, w = img.shape[:2]
    if w > h:
        img = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)
    return img

def round_half(x):
    return round(x * 2) / 2

# Constants
WAIST_L, WAIST_C, WAIST_R = 6, 0, 30
OUTER_ANKLE_L, OUTER_ANKLE_R = 14, 16
INNER_ANKLE_L, INNER_ANKLE_R = 9, 21
INSEAM = 27
FALLBACK_INNER_ANKLE_L = 108

def run_hrnet(img):
    """

    Run HRNet inference using the cached model instance.

    Intermediate tensors are freed immediately after inference.

    """
    global torch, cv2
    if torch is None: import torch
    if cv2 is None: import cv2
    
    model = load_hrnet()  # Cached — no reload cost after first request
    
    H, W = img.shape[:2]
    inp = cv2.resize(img, (288, 384)).astype(np.float32) / 255.0
    inp = inp.transpose(2, 0, 1)
    inp_tensor = torch.from_numpy(inp).unsqueeze(0)
    
    log_memory("Before HRNet inference")
    with torch.inference_mode():
        heatmaps_tensor = model(inp_tensor).squeeze(0)
        heatmaps = heatmaps_tensor.cpu().numpy()
    log_memory("After HRNet inference")

    pts = {}
    num_joints, hm_h, hm_w = heatmaps.shape
    for jid in range(num_joints):
        y, x = np.unravel_index(np.argmax(heatmaps[jid]), heatmaps[jid].shape)
        pts[jid] = (int(x * W / hm_w), int(y * H / hm_h))
    if FALLBACK_INNER_ANKLE_L in pts and pts[FALLBACK_INNER_ANKLE_L][1] > pts[INNER_ANKLE_L][1]:
        pts[INNER_ANKLE_L] = pts[FALLBACK_INNER_ANKLE_L]
    
    # Free intermediate tensors (NOT the model — it stays cached)
    del inp, inp_tensor, heatmaps_tensor, heatmaps
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    log_memory("After HRNet cleanup")
    
    return pts

def dist(p, a, b):
    x1, y1 = p[a]
    x2, y2 = p[b]
    return float(np.hypot(x1 - x2, y1 - y2))

def draw_measurements(img, pts):
    global cv2
    if cv2 is None: import cv2
    
    draw = img.copy()
    connections = [
        (WAIST_L, WAIST_C), (WAIST_C, WAIST_R),
        (WAIST_L, OUTER_ANKLE_L), (WAIST_R, OUTER_ANKLE_R),
        (INNER_ANKLE_L, OUTER_ANKLE_L),
        (INNER_ANKLE_R, OUTER_ANKLE_R),
        (INNER_ANKLE_L, INSEAM),
        (INNER_ANKLE_R, INSEAM),
    ]
    for a, b in connections:
        cv2.line(draw, pts[a], pts[b], (60,120,180), 2)
    return draw

def resize_and_compress(image_arr, max_dim=1000, quality=75):
    """Optimizes the image: resizes to max_dim and compresses to JPEG."""
    try:
        pil_img = Image.fromarray(image_arr)
        width, height = pil_img.size
        if max(width, height) > max_dim:
            if width > height:
                new_width = max_dim
                new_height = int(height * (max_dim / width))
            else:
                new_height = max_dim
                new_width = int(width * (max_dim / height))
            pil_img = pil_img.resize((new_width, new_height), Image.BICUBIC)
        buf = io.BytesIO()
        pil_img.save(buf, format="JPEG", quality=quality, optimize=True)
        buf.seek(0)
        return buf
    except Exception as e:
        print(f"Optimization failed: {e}")
        return None

def upload_image_to_cloud(image_obj, folder):
    cld = get_cloudinary()
    try:
        if isinstance(image_obj, (np.ndarray, np.generic)):
            pil_img = Image.fromarray(image_obj)
            buf = io.BytesIO()
            pil_img.save(buf, format="JPEG", quality=80)
            buf.seek(0)
            obj_to_upload = buf
        else:
            obj_to_upload = image_obj

        response = cld.uploader.upload(
            obj_to_upload,
            folder=folder,
            resource_type="image",
            timeout=120
        )
        return response.get("secure_url")
    except Exception as e:
        print(f"Image upload failed: {e}")
        return None

# ------------------------------------------------------
# UI HEADER
# ------------------------------------------------------
st.markdown("""

<div class="header-container">

    <div class="main-subtitle">Find your perfect size in <b>10 seconds.</b></div>

</div>

""", unsafe_allow_html=True)

# ------------------------------------------------------
# STEP 1: NAME
# ------------------------------------------------------
st.markdown('<div class="step-header">STEP 1 - ENTER YOUR NAME</div>', unsafe_allow_html=True)
user_name = st.text_input("Enter your name", placeholder="Enter your name", label_visibility="collapsed")

# ------------------------------------------------------
# STEP 2: IMAGE UPLOAD
# ------------------------------------------------------
st.markdown('<div class="step-header">STEP 2 - upload a flat laid pant image</div>', unsafe_allow_html=True)

# Use the custom all-in-one upload/tilt/camera component
if pant_upload_ui:
    _component_result = pant_upload_ui(key="pant_uploader")
    # Store the image in session_state so it survives the rerun triggered
    # by the "ANALYZE MY FIT" button click (component value resets on rerun).
    if _component_result is not None:
        st.session_state["uploaded_img_pil"] = _component_result
    
    uploaded_img_pil = st.session_state.get("uploaded_img_pil", None)
    
    # Give user visual feedback on desktop/mobile that their upload succeeded
    if uploaded_img_pil is not None:
        st.success("Image uploaded successfully!")
        st.image(uploaded_img_pil, caption="Your Uploaded Image", width=150)
else:
    uploaded_img_pil = None

uploaded = None  # fallback for file-based pipeline path

# ------------------------------------------------------
# STEP 3: WAIST SIZE (Editable Only)
# ------------------------------------------------------
st.markdown('<div class="step-header">STEP 3 - ENTER WAIST SIZE</div>', unsafe_allow_html=True)

# Initialize session state for waist if not exists
if 'waist_val' not in st.session_state:
    st.session_state.waist_val = 32

# Callback to sync input with session state
def update_waist_from_input():
    st.session_state.waist_val = st.session_state.waist_input

# Single editable number input
st.number_input(
    "Waist Size", 
    min_value=24, 
    max_value=60, 
    value=st.session_state.waist_val, 
    step=1, 
    key="waist_input",
    on_change=update_waist_from_input,
    label_visibility="collapsed"
)

# ------------------------------------------------------
# DEBUG MODE & OPTIONS
# ------------------------------------------------------
show_debug = False

# ------------------------------------------------------
# SOCIAL PROOF & ACTION
# ------------------------------------------------------
st.markdown('<div class="social-proof">Used by 500+ Users</div>', unsafe_allow_html=True)

run_ai = st.button("ANALYZE MY FIT")

# ------------------------------------------------------
# VALIDATION & LOGIC
# ------------------------------------------------------
if run_ai:
    if not user_name:
        st.warning("Please enter your name.")
        st.stop()
    if uploaded_img_pil is None and uploaded is None:
        st.warning("Please upload or capture a pant image.")
        st.stop()
    
    # Init Cloudinary now if needed
    if not init_cloudinary():
        st.warning("Cloudinary config missing.")

    try:
        # Use image from the component (PIL) or fall back to uploaded file
        if uploaded_img_pil is not None:
            img_pil = ImageOps.exif_transpose(uploaded_img_pil)
        else:
            img_pil = Image.open(uploaded)
            img_pil = ImageOps.exif_transpose(img_pil)

        # --- Conditional Proportional Resize ---
        # Only resize if EITHER dimension exceeds MAX_PROCESSING_DIM.
        # Small images are passed through untouched.
        MAX_PROCESSING_DIM = 1024
        orig_w, orig_h = img_pil.size

        scale_x = 1.0
        scale_y = 1.0
        resized = False

        if orig_w > MAX_PROCESSING_DIM or orig_h > MAX_PROCESSING_DIM:
            # Compute scale factor so the longer side lands exactly on MAX_PROCESSING_DIM
            scale = MAX_PROCESSING_DIM / max(orig_w, orig_h)
            new_w = int(orig_w * scale)
            new_h = int(orig_h * scale)

            scale_x = new_w / orig_w
            scale_y = new_h / orig_h
            resized = True

            import cv2 as _cv2
            _img_cv2 = np.array(img_pil.convert("RGB"))
            _img_cv2 = _cv2.cvtColor(_img_cv2, _cv2.COLOR_RGB2BGR)
            _proc = _cv2.resize(_img_cv2, (new_w, new_h), interpolation=_cv2.INTER_AREA)
            img_pil = Image.fromarray(_cv2.cvtColor(_proc, _cv2.COLOR_BGR2RGB))
            del _proc, _img_cv2
            gc.collect()
            print(f"[RESIZE] Original: {orig_w}x{orig_h} -> Processing: {new_w}x{new_h} (scale={scale:.4f})", flush=True)
        else:
            print(f"[RESIZE] Image {orig_w}x{orig_h} is within limit — no resize.", flush=True)

        waist_in = st.session_state.waist_val

        if show_debug:
            st.markdown("### 🔍 Background Removal & Landmark Refinement Debug Mode")
            
            # Use cached single ISNet session to avoid loading multiple model weights into memory
            rembg_session, active_model = load_rembg_session("isnet-general-use")
            
            t_start = time.time()
            import rembg as _rembg
            log_memory("Before rembg inference (Debug)")
            removed = _rembg.remove(img_pil, session=rembg_session)
            log_memory("After rembg inference (Debug)")
            infer_time = time.time() - t_start
            
            if removed.mode == "RGBA":
                white_bg = Image.new("RGBA", removed.size, "WHITE")
                white_bg.paste(removed, mask=removed)
                img_rgb = white_bg.convert("RGB")
                alpha = np.array(removed.split()[-1])
                mask_np = (alpha >= 128).astype(np.uint8) * 255
                del alpha, white_bg
            else:
                img_rgb = removed.convert("RGB")
                gray = cv2.cvtColor(np.array(img_rgb), cv2.COLOR_RGB2GRAY)
                _, mask_np = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)
                del gray
                
            img_np = load_image_portrait(img_rgb)
            binary_mask = load_mask_portrait(mask_np)
            del mask_np
            gc.collect()
            
            raw_pts = run_hrnet(img_np)
            pts, avg_snap = refine_landmarks(raw_pts, binary_mask)

            # Draw on the processing-copy size (correct visual alignment)
            debug_img = draw_landmarks_debug(img_np, pts, pts)
            result_img = draw_measurements(img_np, pts)

            # Map landmark coords back to original space only when a resize occurred
            if resized:
                for lid in pts:
                    px, py = pts[lid]
                    pts[lid] = (int(px / scale_x), int(py / scale_y))

            del raw_pts, binary_mask
            gc.collect()
            del img_np
            gc.collect()

            # Early-convert to bytes to free large arrays before display
            _removed_buf = io.BytesIO()
            removed.save(_removed_buf, format="PNG")
            _removed_buf.seek(0)
            del removed

            _debug_buf = io.BytesIO()
            Image.fromarray(debug_img).save(_debug_buf, format="JPEG", quality=88, optimize=True)
            _debug_buf.seek(0)
            del debug_img

            # result_img is needed for the shared measurement block below
            _result_buf = io.BytesIO()
            Image.fromarray(result_img).save(_result_buf, format="JPEG", quality=88, optimize=True)
            _result_buf.seek(0)
            del result_img

            # Convert original img_pil to bytes
            _orig_buf = io.BytesIO()
            img_pil.save(_orig_buf, format="JPEG", quality=88, optimize=True)
            _orig_buf.seek(0)
            del img_pil
            gc.collect()

            col1, col2 = st.columns(2)
            with col1:
                st.image(_removed_buf, caption="ISNet Cutout", width="stretch")
            with col2:
                st.image(_debug_buf, caption="Landmarks Refined", width="stretch")
                
            st.markdown(f"""

            **Model Metrics:**

            * **Active Model Used:** `{active_model}`

            * **Inference Time:** `{infer_time:.2f} seconds`

            * **Average Snapping Distance:** `{avg_snap:.2f} pixels`

            """)
            del _debug_buf
            gc.collect()


        else:
            # Step 1: Remove background
            with st.spinner("⚡ Removing background..."):
                img_w, img_h = img_pil.size
                img_mode = img_pil.mode
                channels = len(img_pil.getbands())
                approx_mb = (img_w * img_h * channels) / (1024 ** 2)
                print(f"[IMAGE] width={img_w} height={img_h} mode={img_mode} approx={approx_mb:.2f}MB", flush=True)

                rembg_session, active_model = load_rembg_session()

                # Pre-shrink to 800px max before rembg — rembg internally resizes
                # to ~1024 anyway, but passing a smaller image reduces the RGBA
                # output array size and the inference memory spike significantly.
                REMBG_MAX_DIM = 800
                rembg_input = img_pil
                _rw, _rh = img_pil.size
                if max(_rw, _rh) > REMBG_MAX_DIM:
                    _rs = REMBG_MAX_DIM / max(_rw, _rh)
                    rembg_input = img_pil.resize(
                        (int(_rw * _rs), int(_rh * _rs)), Image.BICUBIC
                    )
                    print(f"[REMBG] Resized input to {rembg_input.size} for BG removal", flush=True)

                import rembg as _rembg
                log_memory("Before rembg inference")
                removed_bg_img = _rembg.remove(rembg_input, session=rembg_session)
                log_memory("After rembg inference")
                del rembg_input
                st.info(f"Active segmentation model: **{active_model}**")

                
            # Step 2: Convert to RGB with white background
            with st.spinner("Preparing image for analysis..."):
                if removed_bg_img.mode == "RGBA":
                    white_bg = Image.new("RGBA", removed_bg_img.size, "WHITE")
                    white_bg.paste(removed_bg_img, mask=removed_bg_img)
                    img_rgb = white_bg.convert("RGB")
                    alpha = np.array(removed_bg_img.split()[-1])
                    mask_np = (alpha >= 128).astype(np.uint8) * 255
                    del alpha, white_bg
                else:
                    img_rgb = removed_bg_img.convert("RGB")
                    gray = cv2.cvtColor(np.array(img_rgb), cv2.COLOR_RGB2GRAY)
                    _, mask_np = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)
                    del gray
                
                img_np = load_image_portrait(img_rgb)
                binary_mask = load_mask_portrait(mask_np)
                del img_rgb, mask_np

                # Early-save removed_bg_img to bytes then free the large RGBA PIL object
                # so it is not alive in RAM while HRNet (~0.40 GB) is loading
                _removed_buf = io.BytesIO()
                removed_bg_img.save(_removed_buf, format="PNG")
                _removed_buf.seek(0)
                del removed_bg_img

                gc.collect()
                log_memory("After ISNet cleanup + early RGBA free")

            # Step 3: Run HRNet landmark estimation & refine positions
            with st.spinner("Analyzing measurements..."):
                raw_pts = run_hrnet(img_np)
                pts, _ = refine_landmarks(raw_pts, binary_mask)
                result_img = draw_measurements(img_np, pts)

                # Map landmark coords back to original space only when a resize occurred
                if resized:
                    for lid in pts:
                        px, py = pts[lid]
                        pts[lid] = (int(px / scale_x), int(py / scale_y))

                del raw_pts, binary_mask, img_np
                gc.collect()

        # --- Early conversion: free large numpy/PIL objects, keep only compact bytes ---
        # Convert result_img (numpy HWC array) -> JPEG bytes, then free the array
        _result_buf = io.BytesIO()
        Image.fromarray(result_img).save(_result_buf, format="JPEG", quality=88, optimize=True)
        _result_buf.seek(0)
        del result_img

        # NOTE: _removed_buf was already created and removed_bg_img freed earlier
        # (inside the non-debug branch above) to free RAM before HRNet loads.
        # Guard: only create _removed_buf here if show_debug path ran instead.
        if "_removed_buf" not in locals():
            _removed_buf = io.BytesIO()

        # Convert original img_pil -> JPEG bytes, then free PIL object
        _orig_buf = io.BytesIO()
        img_pil.save(_orig_buf, format="JPEG", quality=88, optimize=True)
        _orig_buf.seek(0)
        del img_pil
        gc.collect()
        log_memory("After early buffer conversion")

        # Calculate Measurements using refined points
        waist_px = dist(pts, WAIST_L, WAIST_C) + dist(pts, WAIST_C, WAIST_R)
        outseam_px = (dist(pts, WAIST_L, OUTER_ANKLE_L) + dist(pts, WAIST_R, OUTER_ANKLE_R)) / 2
        ankle_px = (dist(pts, INNER_ANKLE_L, OUTER_ANKLE_L) + dist(pts, INNER_ANKLE_R, OUTER_ANKLE_R)) / 2
        inseam_px = (dist(pts, INSEAM, INNER_ANKLE_L) + dist(pts, INSEAM, INNER_ANKLE_R)) / 2

        half_waist = (waist_in / 2) - 1
        outseam_val = round_half(outseam_px / waist_px * half_waist)
        ankle_val = round_half(ankle_px / waist_px * half_waist)
        inseam_val = round_half(inseam_px / waist_px * half_waist)
        log_memory("After measurement calculation")

        # ------------------------------------------------------
        # RESULTS DISPLAY (Styled Card)
        # Use pre-converted byte buffers — large arrays already freed
        # ------------------------------------------------------
        st.image(_result_buf, caption="AI Detection Result", width="stretch")

        
        results_html = f"""

        <style>

            .results-card {{

                background: #ffffff;

                border: 1px solid #f4f4f5;

                border-radius: 28px;

                padding: 2.5rem;

                color: #09090b;

                box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.05);

                margin-top: 2rem;

            }}

            .results-header {{ font-size: 1.2rem; font-weight: 700; margin-bottom: 1.5rem; }}

            .size-badge {{ font-size: 4rem; font-weight: 800; line-height: 1; margin-bottom: 1.5rem; }}

            .detail-grid {{ display: grid; grid-template-columns: 1fr 1fr; gap: 1.5rem; border-top: 1px solid #f4f4f5; padding-top: 2rem; }}

            .detail-label {{ font-size: 0.7rem; color: #a1a1aa; text-transform: uppercase; font-weight: 800; letter-spacing: 1px; }}

            .detail-value {{ font-size: 1.25rem; font-weight: 700; }}

        </style>

        <div class="results-card">

            <div class="results-header">Hello {user_name},</div>

            <div class="detail-label">Recommended Size</div>

            <div class="size-badge">{int(waist_in)}</div>

            <div class="detail-grid">

                <div class="detail-item">

                    <div class="detail-label">Waist</div>

                    <div class="detail-value">{waist_in}in</div>

                </div>

                <div class="detail-item">

                    <div class="detail-label">Outseam</div>

                    <div class="detail-value">{outseam_val}in</div>

                </div>

                <div class="detail-item">

                    <div class="detail-label">Inseam</div>

                    <div class="detail-value">{inseam_val}in</div>

                </div>

                <div class="detail-item">

                    <div class="detail-label">Ankle</div>

                    <div class="detail-value">{ankle_val}in</div>

                </div>

            </div>

        </div>

        """
        st.markdown(results_html, unsafe_allow_html=True)

        # ------------------------------------------------------
        # POST MESSAGE TO PARENT (auto-save to localStorage)
        # ------------------------------------------------------
        fit_payload = json.dumps({
            "type": "dashFitResult",
            "name": user_name,
            "waist": waist_in,
            "outseam": float(outseam_val),
            "inseam": float(inseam_val),
            "ankle": float(ankle_val),
            "recommendedSize": str(int(waist_in)),
            "saved_at": datetime.datetime.now(datetime.timezone.utc).isoformat()
        })
        post_msg_js = f"""

        <script>

            (function() {{

                var payload = {fit_payload};

                try {{ window.top.postMessage(payload, '*'); }} catch(e) {{}}

                try {{ window.parent.postMessage(payload, '*'); }} catch(e) {{}}

                try {{ window.parent.parent.postMessage(payload, '*'); }} catch(e) {{}}

                console.log('✅ Fit data emitted from Streamlit:', payload);

            }})();

        </script>

        """
        st.html(post_msg_js)
        
        st.success("✅ Measurements saved successfully! You can now access them via 'Custom Fit' on the DASH website.")

        # ------------------------------------------------------
        # AUTOMATIC SAVE (Server-Side, only if logged in)
        # Upload from _result_buf (already a compressed JPEG BytesIO)
        # ------------------------------------------------------
        if st.session_state.get("uid"):
            with st.spinner("Syncing to cloud profile..."):
                _result_buf.seek(0)
                image_url = upload_image_to_cloud(_result_buf, "dashclothing/scans")
                if image_url:
                    scan_data = {
                        "name": user_name,
                        "imageUrl": image_url,
                        "waist": waist_in,
                        "outseam": outseam_val,
                        "inseam": inseam_val,
                        "ankle": ankle_val,
                        "status": "completed"
                    }
                    try:
                        doc_id = firestore_service.save_prediction(st.session_state["uid"], scan_data)
                        st.success("Result Saved!")
                    except Exception as e:
                        st.error(f"Failed to save to database: {str(e)}")
                else:
                    st.warning("Image upload skipped.")

        # Final cleanup of all request-specific variables
        del _result_buf, _removed_buf, _orig_buf, pts
        gc.collect()
        log_memory("After Request Cleanup")
        # Clear the stored image so the next click requires a fresh upload
        st.session_state.pop("uploaded_img_pil", None)

    except Exception as e:
        st.error(f"An error occurred: {e}")
        # Clear stale image on error too
        st.session_state.pop("uploaded_img_pil", None)