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| import mlflow | |
| import pandas as pd | |
| import joblib | |
| import os | |
| from fastai.data.block import MultiCategoryBlock, RandomSplitter, DataBlock, CategoryBlock | |
| from fastai.vision.data import * | |
| from fastai.vision.learner import * | |
| from fastai.vision.all import * | |
| import pathlib | |
| plt = platform.system() | |
| if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath | |
| lesion_model = None | |
| pipe_cancer = None | |
| def f_load_cnn_model(): | |
| global lesion_model | |
| logged_model = os.path.join("mole_models", "ed52a28a7b504ff7ba851c850221d1dd") | |
| lesion_model = mlflow.fastai.load_model(logged_model) | |
| lesion_model.cbs.remove(lesion_model.cbs[6]) | |
| lesion_model.cbs.remove(lesion_model.cbs[4]) | |
| lesion_model.cbs.remove(lesion_model.cbs[3]) | |
| def get_image_files(df): | |
| # df = df.assign(path=lambda x: working_dataset_path + x["path"]) | |
| return df | |
| def get_x(df): | |
| return df["path"] | |
| def get_y(df): | |
| return df["dx"] | |
| def f_create_df_with_files_input(file_path): | |
| list_files = [file_path] | |
| list_labels = [""] | |
| df = pd.DataFrame(data={"path": list_files, "dx": list_labels}) | |
| return df | |
| def f_predict_cnn_with_tta(file_path): | |
| df = f_create_df_with_files_input(file_path) | |
| dl = lesion_model.dls.test_dl(df) | |
| dl.after_item = Pipeline([ToTensor, Resize(700, method=ResizeMethod.Crop), RandomResizedCrop(350)]) | |
| pred, _targ = lesion_model.tta(dl=dl, n=4, use_max=False) | |
| return pred.tolist()[0] | |
| def f_predict_cnn_simple(file_path): | |
| preds = lesion_model.predict(file_path) | |
| return preds[2].tolist() | |
| def f_load_cancer_classifier(): | |
| global pipe_cancer | |
| pipe_cancer = joblib.load(os.path.join("mole_models", "pipeline.rf_classifier_mole")) | |
| def f_predict_cancer(preds, age, sex, localization): | |
| df = pd.DataFrame(data=[preds], columns=lesion_model.dls.vocab) | |
| df["age"] = age | |
| df["sex"] = sex | |
| df["localization"] = localization | |
| df["label"] = "" | |
| label = pipe_cancer.predict(df)[0] | |
| label = "Possibly suspicious" if label=="cancer" else "benign" | |
| return label | |