first

SkinCancerClass/.__init__.py

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+from model import predict

SkinCancerClass/app.py

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+import gradio as gr
+import os
+import torch
+
+
+from demos.SkinCancerClass.model import predict
+
+
+class_names = [ 'benign_keratosis-like_lesions','basal_cell_carcinoma','actinic_keratoses','dermatofibroma','melanocytic_Nevi']
+example_names = ["actinic_keratoses","basal_cell_carcinoma","melanocytic_Nevi"]
+
+title = "Skin Cancer Classifier"
+description = "An ViT computer vision model to classify images from HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. <br/> List: benign_keratosis-like_lesions, basal_cell_carcinoma, actinic_keratoses, dermatofibroma, melanocytic_Nevi"
+article = "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T"
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example, example.split('_')[0]] for example in os.listdir("examples")]
+# print(example_list)
+# result , timing = predict(example_list[0])
+# 
+
+# Create a single dictionary
+
+
+# Output the combined dictionary
+# print(combined_dict)
+
+# Create the Gradio demo
+# The output of the prediction must be in a dictionary format!
+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=5, label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], 
+                    examples=example_list, 
+                    title=title,
+                    description=description,
+                    article=article,
+                    example_labels=example_names)
+
+# Launch the demo!
+demo.launch(debug=False, # print errors locally?
+            share=True) # generate a publically shareable URL?

SkinCancerClass/model.py

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+#  Here will be the inference to my model on hugging face!!
+# Use a pipeline as a high-level helper
+
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+from transformers import pipeline
+
+pipe = pipeline("image-classification", model="JYL480/vit-base-images")
+
+image_path = "examples/melanocytic_Nevi.jpg"
+
+
+def predict(image):
+    start = timer()
+    result = pipe(image)
+    print(result)
+    pred_time = round(timer() - start, 5)
+    combined_dict = {item['label']: float(item['score']) for item in result}
+    return combined_dict, pred_time
+
+# combined_dict, pred_time = predict(image_path)
+# print(combined_dict)

SkinCancerClass/requirements.txt

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+torch == 2.4.0
+Pillow == 10.4.0
+transformers == 4.43.3
+gradio == 4.40.0