app.py

# Final, API-only app.py for Hugging Face Space

import os
import cv2
import tempfile
import numpy as np
import uvicorn
from PIL import Image
from inference_sdk import InferenceHTTPClient
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
import tensorflow as tf
from huggingface_hub import hf_hub_download

# --- 1. Configuration and Model Loading ---
ROBOFLOW_API_KEY = os.environ.get("ROBOFLOW_API_KEY")
CLIENT_FACE = InferenceHTTPClient(api_url="https://detect.roboflow.com", api_key=ROBOFLOW_API_KEY)
CLIENT_EYES = InferenceHTTPClient(api_url="https://detect.roboflow.com", api_key=ROBOFLOW_API_KEY)
CLIENT_IRIS = InferenceHTTPClient(api_url="https://detect.roboflow.com", api_key=ROBOFLOW_API_KEY)

leuko_model = None
try:
    model_path = hf_hub_download("skibi11/leukolook-eye-detector", "MobileNetV1_best.keras")
    leuko_model = tf.keras.models.load_model(model_path)
    print("--- LEUKOCORIA MODEL LOADED SUCCESSFULLY! ---")
except Exception as e:
    print(f"--- FATAL ERROR: COULD NOT LOAD LEUKOCORIA MODEL: {e} ---")
    raise RuntimeError(f"Could not load leukocoria model: {e}")

# --- 2. All Helper Functions ---
def enhance_image_unsharp_mask(image, strength=0.5, radius=5):
    blur = cv2.GaussianBlur(image, (radius, radius), 0)
    return cv2.addWeighted(image, 1.0 + strength, blur, -strength, 0)

def detect_faces_roboflow(image_path):
    return CLIENT_FACE.infer(image_path, model_id="face-detector-v4liw/2").get("predictions", [])

def detect_eyes_roboflow(image_path, raw_image):
    resp = CLIENT_EYES.infer(image_path, model_id="eye-detection-kso3d/3")
    crops = []
    for p in resp.get("predictions", []):
        x1 = int(p['x'] - p['width'] / 2)
        y1 = int(p['y'] - p['height'] / 2)
        x2 = int(p['x'] + p['width'] / 2)
        y2 = int(p['y'] + p['height'] / 2)
        crop = raw_image[y1:y2, x1:x2]
        if crop.size > 0:
            crops.append(crop)
    return crops

def get_largest_iris_prediction(eye_crop):
    is_success, buffer = cv2.imencode(".jpg", eye_crop)
    if not is_success: return None
    resp = CLIENT_IRIS.infer(buffer, model_id="iris_120_set/7")
    preds = resp.get("predictions", [])
    return max(preds, key=lambda p: p["width"] * p["height"]) if preds else None

def run_leukocoria_prediction(iris_crop):
    if leuko_model is None: return {"error": "Leukocoria model not loaded"}, 0.0
    
    img_pil = Image.fromarray(cv2.cvtColor(iris_crop, cv2.COLOR_BGR2RGB))
    enh = enhance_image_unsharp_mask(np.array(img_pil))
    enh_rs = cv2.resize(enh, (224, 224))
    img_array = np.array(enh_rs) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    
    prediction = leuko_model.predict(img_array)
    confidence = float(prediction[0][0])
    has_leuko = confidence > 0.5
    
    return has_leuko, confidence

# --- 3. FastAPI Application ---
app = FastAPI()

@app.post("/detect/")
async def full_detection_pipeline(image: UploadFile = File(...)):
    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
        contents = await image.read()
        tmp.write(contents)
        temp_image_path = tmp.name
    
    try:
        if not detect_faces_roboflow(temp_image_path):
            return JSONResponse(status_code=400, content={"error": "No face detected."})

        raw_image = cv2.imread(temp_image_path)
        eye_crops = detect_eyes_roboflow(temp_image_path, raw_image)

        if len(eye_crops) != 2:
            return JSONResponse(status_code=400, content={"error": "Exactly two eyes not detected."})
        
        eye_crops.sort(key=lambda c: cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY))[0])

        flags = {}
        for i, eye_crop in enumerate(eye_crops):
            side = "left" if i == 0 else "right"
            pred = get_largest_iris_prediction(eye_crop)
            if pred:
                x1, y1 = int(pred['x'] - pred['width'] / 2), int(pred['y'] - pred['height'] / 2)
                x2, y2 = int(pred['x'] + pred['width'] / 2), int(pred['y'] + pred['height'] / 2)
                iris_crop = eye_crop[y1:y2, x1:x2]
                has_leuko, confidence = run_leukocoria_prediction(iris_crop)
                flags[side] = has_leuko
            else:
                flags[side] = None
        
        return JSONResponse(content={"leukocoria": flags, "warnings": []})

    finally:
        os.remove(temp_image_path)

# --- 4. Run the Server ---
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)