Spaces:
Sleeping
Sleeping
| ### Imports for Modules ### | |
| import gradio as gr | |
| import os | |
| import torch | |
| from typing import Tuple, Dict | |
| from timeit import default_timer as timer | |
| ### Functional Imports | |
| from model import getEffNetModel | |
| classNames = ["Actinic Keratosis", "Basal Cell Carcinoma", "Dermatofibroma", "Melanoma", "Nevus", "Pigmented Benign Keratosis", "Seborrheic Keratosis", "Squamous Cell Carcinoma", "Vascular Lesion"] | |
| effNetModel, effNetTransforms = getEffNetModel(42,len(classNames)) | |
| effNetModel.load_state_dict(torch.load(f="EffNetModel.pt",map_location=torch.device("cpu"))) | |
| def predictionMaker(img): | |
| startTime = timer() | |
| img = effNetTransforms(img).unsqueeze(0) | |
| effNetModel.eval() | |
| with torch.inference_mode(): | |
| predProbs = torch.softmax(effNetModel(img),dim=1) | |
| predDict = {classNames[i]: float(predProbs[0][i]) for i in range(len(classNames))} | |
| endTime = timer() | |
| predTime = round(endTime-startTime,4) | |
| return predDict,predTime | |