Spaces:
Sleeping
Sleeping
| def load_model(name): | |
| return "mock_model" | |
| def run_inference(model, spectrum): | |
| return { | |
| "prediction": "Stubbed Output", | |
| "class_index": 0, | |
| "logits": [0.0, 1.0], | |
| "class_labels": ["Stub", "Output"] | |
| } | |
| # ---------- ACTUAL MODEL LOADING/INFERENCE CODE ---------------------| | |
| # import torch | |
| # import numpy as np | |
| # from pathlib import Path | |
| # from scripts.preprocess_dataset import resample_spectrum | |
| # from models.figure2_cnn import Figure2CNN | |
| # from models.resnet_cnn import ResNet1D | |
| # # -- Label Map -- | |
| # LABELS = ["Stable (Unweathered)", "Weathered (Degraded)"] | |
| # # -- Model Paths -- | |
| # MODEL_CONFIG = { | |
| # "figure2": { | |
| # "class": Figure2CNN, | |
| # "path": "outputs/figure2_model.pth" | |
| # }, | |
| # "resnet": { | |
| # "class": ResNet1D, | |
| # "path": "outputs/resnet_model.pth" | |
| # } | |
| # } | |
| # def load_model(model_name: str): | |
| # if model_name not in MODEL_CONFIG: | |
| # raise ValueError(f"Unknown model '{model_name}'. Valid options: {list(MODEL_CONFIG.keys())}") | |
| # config = MODEL_CONFIG[model_name] | |
| # model = config["class"]() | |
| # state_dict = torch.load(config["path"], map_location=torch.device("cpu"), weights_only=True) | |
| # model.load_state_dict(state_dict) | |
| # model.eval() | |
| # return model | |
| # def run_inference(model, spectrum: list): | |
| # # -- Validate Input -- | |
| # if not isinstance(spectrum, list) or len(spectrum) < 10: | |
| # raise ValueError("Spectrum must be a list of floats with reasonable length") | |
| # # -- Convert to Numpy -- | |
| # spectrum = np.array(spectrum, dtype=np.float32) | |
| # # -- Resample -- | |
| # x_vals = np.arange(len(spectrum)) | |
| # spectrum = resample_spectrum(x_vals, spectrum, target_len=500) | |
| # # -- Normalize -- | |
| # mean = np.mean(spectrum) | |
| # std = np.std(spectrum) | |
| # if std == 0: | |
| # raise ValueError("Standard deviation of spectrum is zero; normalization will fail.") | |
| # spectrum = (spectrum - mean) / std | |
| # # -- To Tensor -- | |
| # x = torch.tensor(spectrum, dtype=torch.float32).unsqueeze(0).unsqueeze(0) # Shape (1, 1, 500) | |
| # with torch.no_grad(): | |
| # logits = model(x) | |
| # pred_index = torch.argmax(logits, dim=1).item() | |
| # return { | |
| # "prediction": LABELS[pred_index], | |
| # "class_index": pred_index, | |
| # "logits": logits.squeeze().tolist(), | |
| # "class_labels": LABELS | |
| # } | |
| # ---------- ACTUAL MODEL LOADING/INFERENCE CODE ---------------------| |