Added Gradio

app.py

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+### 1. Imports and class names setup ### 
+import gradio as gr
+import os
+import torch
+
+from model import create_model
+from timeit import default_timer as timer
+import torchvision
+import torchvision.transforms as transforms
+
+transformer = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(256),
+    transforms.ToTensor(),
+    transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
+])
+
+### 2. Model and transforms preparation ###
+
+# Create model
+model  = create_model(
+    num_classes=3, # len(class_names) would also work
+)
+
+
+# Load saved weights
+model.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+### 3. Predict function ###
+
+from typing import Tuple, Dict
+
+def predict(img):
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Transform the target image and add a batch dimension
+    img = transformer(img).unsqueeze(0)
+    
+    
+    # Put model into evaluation mode and turn on inference mode
+    model.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_prob = torch.sigmoid(model(img))
+        
+    pred_probs = {'Covid' : float(pred_prob), 'Non Covid' : (1-float(pred_prob))}
+
+    # Return the prediction dictionary and prediction time 
+    return pred_probs
+
+### 4. Gradio app ###
+
+
+# Create title, description and article strings
+title = "Corona Prediction"
+description = "A Convolutional Neural Network To classify whether a person have Corona or not using CT  Scans."
+article = "Created by Thenujan Nagaratnam for DNN module at UoM"
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# 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=2, label="Predictions")], # our fn has two outputs, therefore we have two outputs
+                    examples=example_list, 
+                    title=title,
+                    description=description,
+                    article=article)
+
+
+# Launch the demo!
+demo.launch()

model.py

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+import torch
+import torchvision
+import torchvision.models as models
+
+from torch import nn
+
+
+def create_effnetb2_model(num_classes:int=3, 
+                          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
+
+    model = models.resnet50(pretrained=True)
+    # 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

requirements.txt

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