Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision import transforms | |
| # load model | |
| model = torch.jit.load("food_classifier_resnet18.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) | |
| # Grabbing top 3 indices and probabilities for each index | |
| top3_prob, top3_catid = torch.topk(result,3) | |
| # Dictionary I will display | |
| model_output = {} | |
| for i in range(top3_prob.size(1)): | |
| model_output[class_names[top3_catid[0][i].item()]] = top3_prob[0][i].item() | |
| print(model_output) | |
| return model_output | |
| demo = gr.Interface(classify_img, | |
| inputs = gr.inputs.Image(type="pil"), | |
| outputs = gr.outputs.Label(type="confidences",num_top_classes=3), | |
| title = "Food Classifier!", | |
| description = "Insert food image you would like to classify! Returns confidence % for the top three categories <br> Categories: Apple Pie, Bibimbap, Cannoli, Edamame, Falafel, French Toast, Ice Cream, Ramen, Sushi, Tiramisu", | |
| ) | |
| demo.launch(inline=False) |