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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= ['alpine sea holly',
+ 'anthurium',
+ 'artichoke',
+ 'azalea',
+ 'ball moss',
+ 'balloon flower',
+ 'barbeton daisy',
+ 'bearded iris',
+ 'bee balm',
+ 'bird of paradise',
+ 'bishop of llandaff',
+ 'black-eyed susan',
+ 'blackberry lily',
+ 'blanket flower',
+ 'bolero deep blue',
+ 'bougainvillea',
+ 'bromelia',
+ 'buttercup',
+ 'californian poppy',
+ 'camellia',
+ 'canna lily',
+ 'canterbury bells',
+ 'cape flower',
+ 'carnation',
+ 'cautleya spicata',
+ 'clematis',
+ "colt's foot",
+ 'columbine',
+ 'common dandelion',
+ 'corn poppy',
+ 'cyclamen',
+ 'daffodil',
+ 'desert-rose',
+ 'english marigold',
+ 'fire lily',
+ 'foxglove',
+ 'frangipani',
+ 'fritillary',
+ 'garden phlox',
+ 'gaura',
+ 'gazania',
+ 'geranium',
+ 'giant white arum lily',
+ 'globe thistle',
+ 'globe-flower',
+ 'grape hyacinth',
+ 'great masterwort',
+ 'hard-leaved pocket orchid',
+ 'hibiscus',
+ 'hippeastrum',
+ 'japanese anemone',
+ 'king protea',
+ 'lenten rose',
+ 'lotus lotus',
+ 'love in the mist',
+ 'magnolia',
+ 'mallow',
+ 'marigold',
+ 'mexican aster',
+ 'mexican petunia',
+ 'monkshood',
+ 'moon orchid',
+ 'morning glory',
+ 'orange dahlia',
+ 'osteospermum',
+ 'oxeye daisy',
+ 'passion flower',
+ 'pelargonium',
+ 'peruvian lily',
+ 'petunia',
+ 'pincushion flower',
+ 'pink primrose',
+ 'pink-yellow dahlia',
+ 'poinsettia',
+ 'primula',
+ 'prince of wales feathers',
+ 'purple coneflower',
+ 'red ginger',
+ 'rose',
+ 'ruby-lipped cattleya',
+ 'siam tulip',
+ 'silverbush',
+ 'snapdragon',
+ 'spear thistle',
+ 'spring crocus',
+ 'stemless gentian',
+ 'sunflower',
+ 'sweet pea',
+ 'sweet william',
+ 'sword lily',
+ 'thorn apple',
+ 'tiger lily',
+ 'toad lily',
+ 'tree mallow',
+ 'tree poppy',
+ 'trumpet creeper',
+ 'wallflower',
+ 'water lily',
+ 'watercress',
+ 'wild pansy',
+ 'windflower',
+ 'yellow iris'
+ ]
+
+### 2. Model and transforms preparation ###
+
+# Create EffNetB2 model
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=102, # len(class_names) would also work
+)
+
+# Load saved weights
+effnetb2.load_state_dict(
+    torch.load(
+        f="pretrained_effnetb2_feature_extractor_fl102.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+### 3. Predict function ###
+
+# Create predict function
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+    start_time = timer()
+    
+    # Transform the target image and add a batch dimension
+    img = effnetb2_transforms(img).unsqueeze(0)
+    
+    # Put model into evaluation mode and turn on inference mode
+    effnetb2.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(effnetb2(img), dim=1)
+    
+    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+    
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+    
+    # Return the prediction dictionary and prediction time 
+    return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+
+title = "Flofi Demo"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of 102 flower species."
+article = "Created by Haydar Uçar."
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type="pil"), # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=102, label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch(debug=False, # print errors locally?
+            share=True) # generate a publically shareable URL?

model.py

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+import torch
+import torchvision
+
+from torch import nn
+
+
+def create_effnetb2_model(num_classes:102, 
+                          seed:int=42):
+    """Creates an EfficientNetB2 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): number of classes in the classifier head. 
+            Defaults to 3.
+        seed (int, optional): random seed value. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): EffNetB2 feature extractor model. 
+        transforms (torchvision.transforms): EffNetB2 image transforms.
+    """
+    # Create EffNetB2 pretrained weights, transforms and model
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights)
+
+    # Freeze all layers in base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change classifier head with random seed for reproducibility
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1408, out_features=num_classes),
+    )
+    
+    return model, transforms

pretrained_effnetb2_feature_extractor_fl102.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:945699875e822cdcf5e7fdcc887aa7c1704eeca0009e21db3b32e3081684dbe5
+size 31853121