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Update app.py
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app.py
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@@ -1,10 +1,11 @@
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import numpy as np
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import gradio as gr
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import tensorflow as tf
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models = [ {"name": "my_model_2.h5", "size": 512}, {"name": "my_model.h5", "size": 224},]
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from PIL import Image
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def classify_image(image_path, model_name):
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model_config = next(m for m in models if m["name"] == model_name)
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@@ -26,19 +27,19 @@ def classify_image(image_path, model_name):
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label = "Not glaucoma"
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return label, probability
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inputs = [
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gr.inputs.Image(
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gr.inputs.Dropdown(choices=[m["name"] for m in models], label="Model"),
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]
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outputs = [
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gr.outputs.Textbox(label="Predicted label"),
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gr.outputs.Textbox(label="Probability of glaucoma (0-100)"),
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]
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examples = [
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[
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[
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]
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gr.Interface(classify_image, inputs, outputs, examples=examples).launch()
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from PIL import Image
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import tempfile
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def preprocess_image(image):
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temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
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temp_file.write(image.read())
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temp_file.close()
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return temp_file.name
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def classify_image(image_path, model_name):
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model_config = next(m for m in models if m["name"] == model_name)
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label = "Not glaucoma"
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return label, probability
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inputs = [
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gr.inputs.Image(label="Eye image"),
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gr.inputs.Dropdown(choices=[m["name"] for m in models], label="Model"),
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]
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outputs = [
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gr.outputs.Textbox(label="Predicted label"),
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gr.outputs.Textbox(label="Probability of glaucoma (0-100)"),
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]
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examples = [
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[preprocess_image(open("example_image.jpg", "rb")), "my_model.h5"],
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[preprocess_image(open("example_image_2.jpg", "rb")), "my_model_2.h5"]
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]
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gr.Interface(classify_image, inputs, outputs, examples=examples).launch()
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