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app.py

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+import gradio as gr
+import os
+import torch
+from PIL import Image
+from timeit import default_timer as timer
+
+from model import create_model
+from typing import Tuple, Dict
+
+class_names = ['Benign', 'Malignant']
+
+model, transform = create_model()
+
+# Load saved weights
+model.load_state_dict(
+    torch.load(
+        f="melanoma_model1.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()
+
+    # Apply transformations to the image
+    img_tensor = transform(img).unsqueeze(0).to(next(model.parameters()).device)
+
+    # Put model into evaluation mode
+    model.eval()
+
+    # Pass the image through the model
+    with torch.no_grad():
+        y_logits = model(img_tensor).squeeze()
+        y_pred_probs = torch.sigmoid(y_logits)
+
+    # Round the prediction probabilities to get binary predictions
+    y_pred_binary = torch.round(y_pred_probs).item()
+
+    # Create a dictionary with the class label and the corresponding prediction probability
+    pred_label = class_names[int(y_pred_binary)]
+
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+
+    # Return the prediction dictionary and prediction time
+    return {pred_label: float(y_pred_probs)}, pred_time
+
+# Create title, description and article strings
+title = "Melanoma Cancer Detection"
+description = "An Vision Tranformer feature extractor computer vision model to classify images of MELANOMA CANCER.."
+article = " model is built by Shukurullo Meliboev using Kaggle's Melanoma disease datasets."
+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=1, label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch(False) # generate a publically shareable URL?

melanoma_model1.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cefce437c7b45b48513536454fcf2be41049995597180bb818a4a6b2ed0ae8d4
+size 343259298

model.py

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+import torch
+import torchvision
+from torch import nn
+def create_model():
+    torch.manual_seed(42)
+    torch.cuda.manual_seed(42)
+
+    weights = torchvision.models.ViT_B_16_Weights.DEFAULT
+    transform = weights.transforms()
+    model = torchvision.models.vit_b_16(weights=weights)
+
+        # 4. Freeze all layers in base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    model.heads = nn.Sequential(
+         nn.Linear(768,1)
+    )
+    return model, transform

requirements.txt

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+  torch==2.2.1
+  torchvision==0.17.1
+  gradio==4.22.0