app.py

import gradio as gr
import cv2
import numpy as np
import torch
from transformers import AutoModel

device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModel.from_pretrained("ianpan/chest-x-ray-basic", trust_remote_code=True)
model = model.eval().to(device)


def calculate_ctr(mask):
    lungs = np.zeros_like(mask, dtype=np.uint8)
    lungs[(mask == 1) | (mask == 2)] = 1
    heart = (mask == 3).astype("uint8")

    lung_y, lung_x = np.where(lungs == 1)
    heart_y, heart_x = np.where(heart == 1)

    if lung_x.size == 0 or heart_x.size == 0:
        return None, None, None, None, None

    thorax_left = int(lung_x.min())
    thorax_right = int(lung_x.max())
    heart_left = int(heart_x.min())
    heart_right = int(heart_x.max())

    lung_range = thorax_right - thorax_left
    heart_range = heart_right - heart_left
    if lung_range == 0:
        ctr = None
    else:
        ctr = float(heart_range / lung_range)

    return ctr, thorax_left, thorax_right, heart_left, heart_right


def _run_model(image):
    """Shared logic: from PIL image -> (img_gray, mask, view_idx, age, female_prob, coords...)"""
    img = np.array(image.convert("L"))
    h, w = img.shape[:2]

    x = model.preprocess(img)
    x = torch.from_numpy(x).unsqueeze(0).unsqueeze(0).float()

    with torch.inference_mode():
        out = model(x.to(device))

    mask_small = out["mask"].argmax(1)[0].cpu().numpy()
    mask = cv2.resize(mask_small.astype("uint8"), (w, h), interpolation=cv2.INTER_NEAREST)

    view_idx = out["view"].argmax(1).item()
    age_pred = float(out["age"].item())
    female_prob = float(out["female"].item())

    ctr, thorax_left, thorax_right, heart_left, heart_right = calculate_ctr(mask)

    return (
        img,
        mask,
        h,
        w,
        ctr,
        thorax_left,
        thorax_right,
        heart_left,
        heart_right,
        view_idx,
        age_pred,
        female_prob,
    )


# ---------- 1) Visual demo (what you already have) ----------

def analyze(image):
    if image is None:
        return None, "No image uploaded."

    (
        img,
        mask,
        h,
        w,
        ctr,
        thorax_left,
        thorax_right,
        heart_left,
        heart_right,
        view_idx,
        age_pred,
        female_prob,
    ) = _run_model(image)

    color = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
    overlay = color.copy()
    overlay[mask == 1] = [0, 255, 0]
    overlay[mask == 2] = [0, 128, 255]
    overlay[mask == 3] = [255, 0, 0]
    blended = cv2.addWeighted(color, 0.7, overlay, 0.3, 0)

    view_map = {0: "AP", 1: "PA", 2: "lateral"}
    view = view_map.get(view_idx, "unknown")

    lines = []
    if ctr is not None:
        lines.append(f"CTR: {ctr:.2f}")
    else:
        lines.append("CTR: could not be reliably calculated (segmentation issue).")

    lines.extend([
        f"View (model): {view}",
        f"Predicted age: {age_pred:.0f} years",
        f"Predicted sex: {'Female' if female_prob >= 0.5 else 'Male'} (prob={female_prob:.2f})",
        "",
        "⚠️ Research/educational use only, NOT for clinical decision-making.",
    ])

    if view != "PA":
        lines.append("⚠️ CTR is normally interpreted on PA view. Interpret with caution.")

    return blended, "\n".join(lines)


visual_demo = gr.Interface(
    fn=analyze,
    inputs=gr.Image(type="pil", label="Chest X-ray (PNG/JPG) – frontal view"),
    outputs=[
        gr.Image(label="Segmentation overlay"),
        gr.Textbox(label="AI output"),
    ],
    title="AI CTR helper (research only)",
    description=(
        "Segments heart and lungs and estimates CTR using 'ianpan/chest-x-ray-basic'. "
        "Research use only."
    ),
)


# ---------- 2) JSON points API (for your Lovable app) ----------

def get_points(image):
    if image is None:
        return {"error": "No image uploaded"}

    (
        img,
        mask,
        h,
        w,
        ctr,
        thorax_left,
        thorax_right,
        heart_left,
        heart_right,
        view_idx,
        age_pred,
        female_prob,
    ) = _run_model(image)

    result = {
        "image_width": w,
        "image_height": h,
        "ctr": ctr,
        "thorax_left_px": thorax_left,
        "thorax_right_px": thorax_right,
        "heart_left_px": heart_left,
        "heart_right_px": heart_right,
        "view_idx": int(view_idx),
    }
    return result


points_api = gr.Interface(
    fn=get_points,
    inputs=gr.Image(type="pil", label="Chest X-ray (PNG/JPG) – frontal view"),
    outputs=gr.JSON(label="CTR points JSON"),
    title="CTR points API",
    description="Returns thorax/heart x-coordinates and CTR as JSON.",
    api_name="ctr_points",  # important for programmatic calls
)

demo = gr.TabbedInterface(
    [visual_demo, points_api],
    ["Viewer", "JSON API"],
)

if __name__ == "__main__":
    demo.launch()