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# -*- coding: utf-8 -*- | |
"""gradioApp.ipynb | |
Automatically generated by Colaboratory. | |
Original file is located at | |
https://colab.research.google.com/drive/19rOnZUE7tNaMyAjlhnO4vLKb8mojrf2V | |
""" | |
# Commented out IPython magic to ensure Python compatibility. | |
# %%capture | |
# #Use capture to not show the output of installing the libraries! | |
# !pip install gradio | |
import gradio as gr | |
import numpy as np | |
import tensorflow as tf | |
model = tf.keras.models.load_model('/content/drive/MyDrive/project_image_2023_NO/saved_models/saved_model/densenet') | |
labels = ['Healthy', 'Patient'] | |
def classify_image(inp): | |
inp = inp.reshape((-1, 224, 224, 3)) | |
inp = tf.keras.applications.densenet.preprocess_input(inp) | |
prediction = model.predict(inp) | |
confidences = {labels[i]: float(prediction[0][i]) for i in range(2)} | |
return confidences | |
gr.Interface(fn=classify_image, | |
inputs=gr.Image(shape=(224, 224)), | |
outputs=gr.Label(num_top_classes = 2), | |
title="Demo", | |
description="Here's a sample image classification. Enjoy!", | |
).launch(share = True) |