Add application file

app.py

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+import gradio as gr
+from tensorflow import keras
+from skimage.transform import resize
+
+# def greet(name):
+#     return "Hello " + name + "!!"
+
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()
+
+oc_resnet50_model = keras.models.load_model('oc_model.h5')
+labels = ['Malignant Lesion', 'Benign Lesion']
+
+def classify_image(inp):
+
+  inp =resize(inp, (300, 300, 3))
+  inp = inp.reshape((-1, 300, 300, 3))
+  # inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
+  prediction = oc_resnet50_model.predict(inp).flatten()
+  confidences = {labels[i]: float(prediction[i]) for i in range(2)}
+  return confidences
+
+gr.Interface(fn=classify_image, 
+             inputs=gr.Image(shape=(300, 300)),
+             outputs=gr.Label(num_top_classes=2),
+             ).launch()

oc_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:17cadbca5ce3eaa1f3b4646f9ce07b8f6cc2b6f065bb251d525514120294849f
+size 2619381592

requirements.txt

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+tensorflow
+scikit-image
+gradio
+matplotlib
+numpy