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import gradio as gr | |
import tensorflow as tf | |
def classify_image(Image): | |
model = tf.keras.models.load_model('tumor_model.h5') | |
labels = [ | |
'Tumor - Glioma', | |
'No Tumor', | |
'Tumor - Meningioma', | |
'Tumor - Pituitary' | |
] | |
inp = Image.reshape((-1, 128, 128, 3)) | |
inp = tf.keras.applications.resnet50.preprocess_input(inp) | |
prediction = model.predict(inp).flatten() | |
confidences = {labels[i]: float(prediction[i]) for i in range(4)} | |
return confidences | |
image_input = gr.inputs.Image(shape = (128, 128)) | |
label_output = gr.outputs.Label(num_top_classes = 4) | |
title = "Tumor Classification" | |
description = "Upload an image and get predictions for tumor classification." | |
examples = [ | |
['Example 1.jpg'], | |
['Example 2.jpg'] | |
] | |
interface = gr.Interface( | |
fn = classify_image, | |
inputs = image_input, | |
outputs = label_output, | |
title = title, | |
description = description, | |
examples = examples, | |
button_style ='danger', | |
theme = 'huggingface' | |
) | |
interface.css = """ | |
.main { | |
margin: 0 10%; | |
} | |
""" | |
interface.launch() |