Spaces:
Running
Running
| import gradio as gr | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision import transforms | |
| # load model | |
| model = torch.jit.load("food_classifier.ptl") | |
| # Transformations that will be applied | |
| the_transform = transforms.Compose([ | |
| transforms.Resize((224,224)), | |
| transforms.CenterCrop((224,224)), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225]) | |
| ]) | |
| # Classes | |
| class_names = ['Apple Pie','Bibimbap','Cannoli','Edamame','Falafel','French Toast','Ice Cream','Ramen','Sushi','Tiramisu'] | |
| # Returns transformed image | |
| def transform_img(img): | |
| return the_transform(img) | |
| # Returns string with class and probability | |
| def classify_img(img): | |
| # Applying transformation to the image | |
| model_img = transform_img(img) | |
| model_img = model_img.view(1,3,224,224) | |
| # Running image through the model | |
| model.eval() | |
| with torch.no_grad(): | |
| result = model(model_img) | |
| # Converting values to softmax values | |
| result = F.softmax(result,dim=1) | |
| probability = round(result[0][result.argmax()].item() * 100, 2) | |
| # Returning class name and probability | |
| return f'{class_names[result.argmax()]} : {probability}% confident' | |
| demo = gr.Interface(classify_img, | |
| inputs = gr.inputs.Image(type="pil"), | |
| outputs = "text", | |
| description="Insert food image you would like to classify! <br> Categories: Apple Pie, Bibimbap, Cannoli, Edamame, Falafel, French Toast, Ice Cream, Ramen, Sushi, Tiramisu") | |
| demo.launch(inline=False) |