things are added

app.py

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+
+# import the essentials
+from demos.foodvision_mini.model import create_vit_b_16_model
+import torch
+import torchvision
+import time
+import gradio as gr
+import numpy as np
+from pathlib import Path
+
+class_names = ['pizza','steak','sushi']
+device = 'cuda' if torch.cuda.is_available else 'cpu'
+
+# creating the vit_b_16_model and loading it with state_dict of our trained model
+vit_b_16_model,vit_b_16_transform = create_vit_b_16_model(num_classes=3)
+vit_b_16_model.load_state_dict(torch.load(f='vit_b_16_20_percent_data.pth'))
+
+# create the predict function
+def predict(img):
+
+  """
+  args:
+  img: is an image
+
+  returns: prediction class, prediction probability, and time taken to make the prediction
+
+  """
+
+  # transforming the image
+  tr_img = vit_b_16_transform(img).unsqueeze(dim=0).to(device)
+
+  # make prediction with vit_b_16
+  model = vit_b_16_model.to(device)
+
+  # starting the time
+  start_time = time.perf_counter()
+
+  model.eval()
+  with torch.inference_mode():
+    pred_logit = model(tr_img)
+    pred_label = torch.argmax(pred_logit,dim=1).cpu()
+    pred_prob = torch.max(torch.softmax(pred_logit,dim=1)).cpu().item()
+
+  # ending the time
+  end_time = time.perf_counter()
+  # pred_dict = {str(class_names[i]):float(pred_prob[0][i].item()) for i in range(len(class_names))}
+  pred_prob  = float(np.round(pred_prob,3))
+  pred_class = class_names[pred_label]
+  time_taken = float(np.round(end_time-start_time,3))
+
+
+
+  return pred_class,pred_prob,time_taken
+
+
+# create example list
+example_dir = Path('demos/foodvision_mini/examples')
+example_list = [['examples/' + str(filepath)] for filepath in os.listdir(example_dir)]
+
+# create Gradio interface
+description = 'A machine learning model to classify images into pizza,steak and sushi appropriately'
+title = 'Image Classifier'
+
+
+demo = gr.Interface(fn=predict, # this function maps the inputs to the output
+                    inputs=gr.Image(type='pil'), # pillow image
+                    outputs=[gr.Label(num_top_classes=1,label='Prediction'),
+                             gr.Number(label='prediction probability'),
+                             gr.Number(label='prediction time(s)')],
+                    examples=example_list,
+                    description=description,
+                    title=title
+                    )
+
+demo.launch(debug=False, # print errors locally?
+            share=True) # share to the public?
+
+
+
+
+

model.py

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+
+import torchvision
+import torch
+def create_vit_b_16_model(num_classes=3):
+
+  weights = torchvision.models.ViT_B_16_Weights.DEFAULT
+  transform = torchvision.models.ViT_B_16_Weights.DEFAULT.transforms()
+  model = torchvision.models.vit_b_16(weights=weights)
+
+  # freeze the layers
+  for param in model.parameters():
+    param.requires_grad = False
+
+  # modify the heads layer
+  model.heads = torch.nn.Sequential(
+      torch.nn.Linear(in_features=768,out_features=num_classes)
+  )
+
+  return model,transform

requirements.txt

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+torch>=1.12.0
+torchvision>=0.13.0
+gradio>=3.1.4

vit_b_16_20_percent_data.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72dbf7ad12f2bf4ce1aedab9105ba670c9a7ba6fae54aba670b77700e954a8ae
+size 343266910