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48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
import gradio as gr
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import tensorflow as tf
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# from vis.utils import utils
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# from vis.visualization import visualize_cam
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import numpy as np
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from PIL import Image
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import requests
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from urllib.request import urlretrieve
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# # Download human-readable labels for ImageNet.
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# response = requests.get("https://git.io/JJkYN")
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# labels = response.text.split("\n")
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labels = range(1000) # comment this later
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mobile_net = tf.keras.applications.MobileNetV2()
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def image_classifier(im):
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arr = np.expand_dims(im, axis=0)
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arr = tf.keras.applications.mobilenet.preprocess_input(arr)
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prediction = mobile_net.predict(arr).flatten()
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return {labels[i]: float(prediction[i]) for i in range(1000)}
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def image_explain(im):
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model.layers[-1].activation = tf.keras.activations.linear
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model = utils.apply_modifications(model)
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penultimate_layer_idx = 2
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class_idx = class_idxs_sorted[0]
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seed_input = img
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grad_top1 = visualize_cam(model, layer_idx, class_idx, seed_input,
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penultimate_layer_idx = penultimate_layer_idx,#None,
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backprop_modifier = None,
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grad_modifier = None)
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print(grad_top_1)
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return grad_top1
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image = gr.inputs.Image(shape=(224, 224))
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label = gr.outputs.Label(num_top_classes=3)
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gr.Interface(image_classifier, image, label,
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capture_session=True,
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interpretation="default",
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examples=[
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["images/cheetah1.jpg"],
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["images/lion.jpg"]
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]).launch(); |