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| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| from sklearn.preprocessing import LabelEncoder | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision import transforms | |
| import torchvision | |
| import torchvision.models as models | |
| from torchvision.datasets import ImageFolder | |
| from torch.utils.data.dataset import Dataset | |
| from torch.utils.data import Dataset, random_split, DataLoader | |
| from torch.utils.data import DataLoader | |
| from sklearn.model_selection import train_test_split | |
| from tqdm.notebook import tqdm | |
| class net50(torch.nn.Module): | |
| def __init__(self, base_model, base_out_features, num_classes): | |
| super(net50,self).__init__() | |
| self.base_model=base_model | |
| self.linear1 = torch.nn.Linear(base_out_features, 512) | |
| self.output = torch.nn.Linear(512,num_classes) | |
| def forward(self,x): | |
| x = F.relu(self.base_model(x)) | |
| x = F.relu(self.linear1(x)) | |
| x = self.output(x) | |
| return x | |
| def get_default_device(): | |
| if torch.cuda.is_available(): | |
| return torch.device('cuda') | |
| else: | |
| return torch.device('cpu') | |
| device = get_default_device() | |
| PATH = "./model/model.zip" | |
| map_location=torch.device('cpu') | |
| def predict_single(img): | |
| xb = transform_image(img) # Transforming image to Tensor | |
| xb = xb.to(device) | |
| preds = model(xb) # change model object here | |
| max_val, kls = torch.max(preds, 1) | |
| print('Predicted :', breeds[kls]) | |
| return breeds[kls] | |
| def image_mod(image): | |
| return predict_single(image) | |
| def transform_image(image_bytes): | |
| my_transforms = transforms.Compose([transforms.Resize((500)), | |
| transforms.ToTensor(), | |
| transforms.Normalize( | |
| [0.485, 0.456, 0.406], | |
| [0.229, 0.224, 0.225])]) | |
| return my_transforms(image_bytes).unsqueeze(0) | |
| res = torchvision.models.resnet50(pretrained=True) | |
| for param in res.parameters(): ## Freezing layers | |
| param.requires_grad=False | |
| model = net50(base_model=res, base_out_features=res.fc.out_features, num_classes=120) | |
| model.load_state_dict(torch.load(PATH,map_location)) | |
| model.eval() | |
| breeds=['Chihuahua', | |
| 'Japanese spaniel', | |
| 'Maltese dog', | |
| 'Pekinese', | |
| 'Shih Tzu', | |
| 'Blenheim spaniel', | |
| 'papillon', | |
| 'toy terrier', | |
| 'Rhodesian ridgeback', | |
| 'Afghan hound', | |
| 'basset', | |
| 'beagle', | |
| 'bloodhound', | |
| 'bluetick', | |
| 'black and tan coonhound', | |
| 'Walker hound', | |
| 'English foxhound', | |
| 'redbone', | |
| 'borzoi', | |
| 'Irish wolfhound', | |
| 'Italian greyhound', | |
| 'whippet', | |
| 'Ibizan hound', | |
| 'Norwegian elkhound', | |
| 'otterhound', | |
| 'Saluki', | |
| 'Scottish deerhound', | |
| 'Weimaraner', | |
| 'Staffordshire bullterrier', | |
| 'American Staffordshire terrier', | |
| 'Bedlington terrier', | |
| 'Border terrier', | |
| 'Kerry blue terrier', | |
| 'Irish terrier', | |
| 'Norfolk terrier', | |
| 'Norwich terrier', | |
| 'Yorkshire terrier', | |
| 'wire haired fox terrier', | |
| 'Lakeland terrier', | |
| 'Sealyham terrier', | |
| 'Airedale', | |
| 'cairn', | |
| 'Australian terrier', | |
| 'Dandie Dinmont', | |
| 'Boston bull', | |
| 'miniature schnauzer', | |
| 'giant schnauzer', | |
| 'standard schnauzer', | |
| 'Scotch terrier', | |
| 'Tibetan terrier', | |
| 'silky terrier', | |
| 'soft coated wheaten terrier', | |
| 'West Highland white terrier', | |
| 'Lhasa', | |
| 'flat coated retriever', | |
| 'curly coated retriever', | |
| 'golden retriever', | |
| 'Labrador retriever', | |
| 'Chesapeake Bay retriever', | |
| 'German short haired pointer', | |
| 'vizsla', | |
| 'English setter', | |
| 'Irish setter', | |
| 'Gordon setter', | |
| 'Brittany spaniel', | |
| 'clumber', | |
| 'English springer', | |
| 'Welsh springer spaniel', | |
| 'cocker spaniel', | |
| 'Sussex spaniel', | |
| 'Irish water spaniel', | |
| 'kuvasz', | |
| 'schipperke', | |
| 'groenendael', | |
| 'malinois', | |
| 'briard', | |
| 'kelpie', | |
| 'komondor', | |
| 'Old English sheepdog', | |
| 'Shetland sheepdog', | |
| 'collie', | |
| 'Border collie', | |
| 'Bouvier des Flandres', | |
| 'Rottweiler', | |
| 'German shepherd', | |
| 'Doberman', | |
| 'miniature pinscher', | |
| 'Greater Swiss Mountain dog', | |
| 'Bernese mountain dog', | |
| 'Appenzeller', | |
| 'EntleBucher', | |
| 'boxer', | |
| 'bull mastiff', | |
| 'Tibetan mastiff', | |
| 'French bulldog', | |
| 'Great Dane', | |
| 'Saint Bernard', | |
| 'Eskimo dog', | |
| 'malamute', | |
| 'Siberian husky', | |
| 'affenpinscher', | |
| 'basenji', | |
| 'pug', | |
| 'Leonberg', | |
| 'Newfoundland', | |
| 'Great Pyrenees', | |
| 'Samoyed', | |
| 'Pomeranian', | |
| 'chow', | |
| 'keeshond', | |
| 'Brabancon griffon', | |
| 'Pembroke', | |
| 'Cardigan', | |
| 'toy poodle', | |
| 'miniature poodle', | |
| 'standard poodle', | |
| 'Mexican hairless', | |
| 'dingo', | |
| 'dhole', | |
| 'African hunting dog'] | |
| iface = gr.Interface(image_mod, gr.Image(type="pil"), "text", examples=["doggo1.png","doggo2.jpg","doggo3.png","doggo4.png"]) | |
| iface.launch() |