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app.py

@@ -1,3 +1,15 @@
 import gradio as gr
+from transformers import pipeline
 
-gr.Interface.load("models/Fu-chiang/bit-50-Otisis-binary").launch()
+pipeline = pipeline(task="image-classification", model="Fu-chiang/bit-50-skin-lesions")
+
+def predict(image):
+    predictions = pipeline(image)
+    return {p["label"]: p["score"] for p in predictions}
+
+gr.Interface(
+    predict,
+    inputs=gr.inputs.Image(label="Upload skin lesion picture", type="filepath"),
+    outputs=gr.outputs.Label(num_top_classes=2),
+    title="Melanoma or Nevus?",
+).launch()

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+torch