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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_resnet.keras filter=lfs diff=lfs merge=lfs -text

class_indices.json

@@ -0,0 +1,10 @@
+{
+    "AK": 0,
+    "BCC": 1,
+    "BKL": 2,
+    "DF": 3,
+    "MEL": 4,
+    "NV": 5,
+    "SCC": 6,
+    "VASC": 7
+}

label_encoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40f4e059c717e30230dc44bd14493a1576163755a6a9c0fcfdbab9b4117ecbac
+size 518

model_resnet.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2926dfaba446252728e0561bf43389e0edff7c92c8ecbee42996cfd68fc62ea4
+size 223963966

predict.py

@@ -0,0 +1,32 @@
+import tensorflow as tf
+import numpy as np
+import gradio as gr
+from PIL import Image
+
+# load model
+model = tf.keras.models.load_model("model_resnet.keras")
+
+CLASS_NAMES = ['MEL','NV','BCC','AK','BKL','DF','VASC','SCC']
+
+def predict(image):
+    img = image.resize((224, 224))
+    img = np.array(img).astype("float32") / 255.0
+    img = np.expand_dims(img, axis=0)
+
+    pred = model.predict(img)
+    idx = int(np.argmax(pred))
+    conf = float(np.max(pred))
+
+    return {
+        "class": CLASS_NAMES[idx],
+        "confidence": conf
+    }
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="json",
+    title="ISIC 2019 Skin Cancer Classifier API"
+)
+
+iface.launch()

requirements.txt

@@ -0,0 +1,4 @@
+tensorflow-cpu
+gradio
+numpy
+pillow