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app.py

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+### 1. Imports and class names setup ###
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple,Dict
+
+# Setup class names
+class_names=["pizza","steak","sushi"]
+### 2. Model and transforms perparation ###
+effnetb2,effnetb2_transforms=create_effnetb2_model(num_classes=len(class_names))
+
+# Load save weights
+effnetb2.load_state_dict(
+    torch.load(
+        f="effnetb2_feature_extractor_food101_mini.pth",
+        map_location=torch.device("cpu") # load the model to the CPU
+    )
+)
+
+### 3. Predict function ###
+def predict(img)->Tuple[Dict,float]:
+  # start a timer
+  start_time = timer()
+
+  # Transform the input image for use with EffNetB2
+  img=effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index
+
+  # Put model into eval mode, make prediction
+  effnetb2.eval()
+  with torch.inference_mode():
+    # Pass transformed image through the model and turn the prediction logits into probaiblities
+    pred_probs=torch.softmax(effnetb2(img),dim=1)
+
+  # Create a prediction label and prediction probability dictionary
+  pred_labels_and_probs = {}
+
+  # pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+  # comment loop below to un-comment line above
+  for i,class_name in enumerate(class_names):
+    pred_labels_and_probs[class_name]=pred_probs[0][i]
+
+  # Calculate pred time
+  end_time=timer()
+  pred_time=round(end_time-start_time,4)
+
+  # Return pred dict and pred time
+  return pred_labels_and_probs,pred_time
+
+### 4. Gradio app ###
+
+# Create title, description and article
+
+title="Food101 Big Classification"
+description = "An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images as pizza, steak or sushi."
+article = "Created at [Food101 Mini Classification](https://github.com/MRameezU/Food-101-Mini-Classification.git)."
+
+# Create example list
+# Create example list
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # maps inputs to outputs
+                    inputs=gr.Image(type="pil"),
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"),
+                             gr.Number(label="Prediction time (s)")],
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch()

effnetb2_feature_extractor_food101_mini.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:90f73ef943cd559fb486e7fd7a5512587a1589bd98f37da098604cd7aebcacb6
+size 31300410

model.py

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+import torch
+import torchvision
+
+from torch import nn
+
+def create_effnetb2_model(num_classes:int=3,
+                          seed:int=42):
+  # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
+  weights=torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transforms=weights.transforms()
+  model=torchvision.models.efficientnet_b2(weights=weights)
+
+  for param in model.parameters():
+    param.requires_grad=False
+    model.classifier=nn.Sequential(
+        nn.Dropout(p=0.3,inplace=True),
+        nn.Linear(in_features=1408,out_features=num_classes)
+    )
+  return model,transforms
+

requirements.txt

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+
+torch==1.12.0
+torchvision==0.13.0
+gradio==4.38.1
+numpy<2