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| import timm | |
| import torch | |
| import torch.nn.functional as nnf | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import json | |
| from torchvision.transforms import ToTensor, Resize | |
| from PIL import Image | |
| #model = torch.load("/home/user/app/model_scripted.pkl",map_location=torch.device('cpu')) | |
| model = torch.jit.load("/home/user/app/model_scripted.pkl") | |
| model.eval() | |
| with open("/home/user/app/class_mapping.json", "r") as read_file: | |
| classes = json.load(read_file) | |
| def classify_image(inp): | |
| inp = Image.fromarray(inp) | |
| inp= Resize((300, 300))(inp) | |
| inp= ToTensor()(inp) | |
| inp = torch.unsqueeze(inp, 0) | |
| ##print(inp.shape) | |
| #inp = inp.astype(np.uint8).reshape((-1, 3, 300, 300)) | |
| ##print(inp.shape) | |
| #inp = torch.from_numpy(inp).float() | |
| ##confidences = model(inp) | |
| preds = model(inp).data[0] | |
| #means = preds.mean(dim=0, keepdim=True) | |
| #stds = preds.std(dim=0, keepdim=True) | |
| #preds = 4 * (preds - means) / stds | |
| ##preds = nnf.normalize(model(inp).data[0], dim=0) | |
| preds = nnf.softmax(preds, dim=0) | |
| preds = [pred.cpu() for pred in preds] | |
| preds = [float(pred.detach()) for pred in preds] | |
| print(pd.Series(preds).describe()) | |
| #confidences_dict = {classes[i]: float(confidences.data[0][i]) for i in range(len(confidences.data[0]))} | |
| confidences_dict = {classes[str(i)]: float(preds[i]) for i in range(len(preds))} | |
| return confidences_dict | |
| gr.Interface(fn=classify_image, | |
| inputs=gr.Image(shape=(300, 300)), | |
| outputs=gr.Label(num_top_classes=3)).launch(debug = True) |