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app.py

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+import gradio as gr
+import os
+import torch
+
+from model import create_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+
+# Loading saved weights
+model, transforms = create_model(num_classes=13)
+model_load_dict = torch.load('iran_cars_model_dict.pth', map_location=torch.device('cpu'))
+model.load_state_dict(model_load_dict['state_dict'])
+class_names = model_load_dict['class_names']
+
+
+def predict(img):
+  """Transforms and performs a prediction on img 
+      and returns prediction and time taken.
+  """
+  # starting the timer
+  start_time = timer()
+
+  # transforming the target image and adding a batch dimention
+  img = transforms(img).unsqueeze(0)
+
+  # putting model into evaluation mode
+  model.eval()
+
+  # turning on inference_mode in context manager
+  with torch.inference_mode():
+    pred_probs = torch.softmax(model(img), dim=1)
+
+  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+  
+  pred_time = round(timer() - start_time, 5)
+
+  return pred_labels_and_probs, pred_time
+
+
+
+title = 'Iran Cars CoputerVision_V0 🚗'
+description = 'an EfficientNetb0 CV model created by MiladAbdollahi'
+article = 'github.com/Milad-Abdollahi'
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+demo = gr.Interface(fn=predict,
+                  inputs=gr.Image(type='pil'),
+                  outputs=[gr.Label(num_top_classes=13, label='Predictions'),
+                          gr.Number(Label="Prediction time (s)")],
+                  examples=example_list,
+                  title=title,
+                  description=description,
+                  article=article
+                  )
+
+demo.launch(debug=False, share=True)

model.py

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+
+import torch
+import torchvision
+from torch import nn
+
+device='cpu'
+def create_model(num_classes: int=13):
+  weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT
+  transforms = weights.transforms()
+  model = torchvision.models.efficientnet_b0(weights=weights).to(device)
+
+  for param in model.features.parameters():
+   param.requires_grad = False
+
+  model.classifier = torch.nn.Sequential(
+      torch.nn.Dropout(p=0.2, inplace=True),
+      torch.nn.Linear(in_features=1280, out_features=13, bias=True)
+  ).to(device)
+
+  return model, transforms
+  

requirements.txt

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