| """ |
| inference.py — ONNX Runtime wrapper for the Random Forest model. |
| |
| Expected model: final_model_Random_Forest.onnx |
| Input : float_input [None, 120] float32 (raw features — no scaling needed) |
| Outputs: label [None] int64 |
| probabilities [None, 3] float32 |
| """ |
|
|
| from pathlib import Path |
| from threading import Lock |
|
|
| import numpy as np |
| import onnxruntime as rt |
|
|
| MODEL_PATH = Path(__file__).parent / "model" / "final_model_Random_Forest.onnx" |
|
|
| CLASS_NAMES = { |
| 0: "Common / Benign Nevi", |
| 1: "Atypical / Other Benign", |
| 2: "Melanoma (Suspected)", |
| } |
|
|
| CLASS_RISK = { |
| 0: "healthy", |
| 1: "watch", |
| 2: "danger", |
| } |
|
|
| CLASS_WHAT = { |
| 0: "A common benign mole. Melanocytic nevi are very common — most adults have 10–40. Almost always completely harmless.", |
| 1: "This category includes atypical or other benign lesions such as seborrhoeic keratosis, actinic keratosis, dermatofibroma, or vascular lesions. While many are harmless, some may need treatment.", |
| 2: "Melanoma is the most serious type of skin cancer. It develops from pigment-producing cells. Early detection is critical — when caught early, treatment is highly effective.", |
| } |
|
|
| CLASS_ACTION = { |
| 0: "No action needed. Monitor for changes in shape, colour, size, or bleeding.", |
| 1: "Recommended: book a consultation with a dermatologist for professional evaluation.", |
| 2: "Please see a dermatologist or doctor as soon as possible. Do not delay.", |
| } |
|
|
| _session = None |
| _session_lock = Lock() |
|
|
| def load_model() -> rt.InferenceSession: |
| """Load ONNX model (cached after first call).""" |
| global _session |
| if _session is None: |
| with _session_lock: |
| if _session is None: |
| if not MODEL_PATH.exists(): |
| raise FileNotFoundError( |
| f"ONNX model not found at {MODEL_PATH}. " |
| "Please copy final_model_Random_Forest.onnx into backend/models/" |
| ) |
| opts = rt.SessionOptions() |
| opts.intra_op_num_threads = 4 |
| _session = rt.InferenceSession(str(MODEL_PATH), sess_options=opts) |
| return _session |
|
|
|
|
| def predict(features: np.ndarray) -> dict: |
| """ |
| Run ONNX inference on a (120,) or (1, 120) feature vector. |
| |
| Returns: |
| { |
| "label": int, # 0, 1, or 2 |
| "class_name": str, |
| "risk": str, # healthy / watch / danger |
| "probabilities": [p0, p1, p2], # float list, sums to 1 |
| "confidence": float, # max probability |
| "what": str, # plain-language explanation |
| "action": str, # recommended next step |
| } |
| """ |
| sess = load_model() |
| if features.ndim == 1: |
| features = features.reshape(1, -1) |
| features = features.astype(np.float32) |
|
|
| label_arr, prob_arr = sess.run( |
| ["label", "probabilities"], |
| {"float_input": features} |
| ) |
| label = int(label_arr[0]) |
| probs = prob_arr[0].tolist() |
| if label not in CLASS_NAMES: |
| raise ValueError(f"Model returned unexpected label {label}; probabilities={probs}") |
|
|
| return { |
| "label": label, |
| "class_name": CLASS_NAMES[label], |
| "risk": CLASS_RISK[label], |
| "probabilities": probs, |
| "confidence": float(max(probs)), |
| "what": CLASS_WHAT[label], |
| "action": CLASS_ACTION[label], |
| } |
|
|
