Primera versión a probar en el hosting de Hugging Face.

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b540de92a7222f8b7a8794310ecc8daf96955200b5281d12c7ade8ebc4f69dcc
+size 2453432

nevus_ai.py

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+# Import dependencies
+from keras.models import load_model
+from PIL import Image, ImageOps
+import numpy as np
+import gradio as gr
+
+
+# Definition of the main function for predictions
+def predict_nevus(image):
+    # Load the model
+    model = load_model('keras_model.h5')
+
+    # Create the array of the right shape to feed into the keras model
+    # The 'length' or number of images you can put into the array is
+    # determined by the first position in the shape tuple, in this case 1.
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+
+    #turn the image into a numpy array
+    image_array = np.asarray(image)
+    
+    # Normalize the image
+    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+    
+    # Load the image into the array
+    data[0] = normalized_image_array
+
+    # run the inference
+    prediction = model.predict(data)
+    return { 
+        'Melanoma': float(prediction[0][0]), 
+        'Lunar': float(prediction[0][1])
+    }
+
+# Deploy with Gradio
+examples=[
+  ['2.jpg'],
+  ['37.jpg'],
+  ['186.jpg']
+]
+
+iface = gr.Interface(
+    fn=predict_nevus, 
+    inputs=gr.inputs.Image(shape=(224, 224)), 
+    outputs="label",
+    title="Detector de melanomas",
+    description="Herramienta online que utiliza inteligencia artificial para detectar posibles melanomas en fotografías de lunares.",
+    examples=examples,
+    allow_flagging='never',
+    theme="peach"
+)
+
+iface.launch(debug=True)