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# -*- coding: utf-8 -*- | |
# %%capture | |
# #Use capture to not show the output of installing the libraries! | |
#model_multi = tf.keras.models.load_model("densenet") | |
# define the labels for the multi-label classification model | |
#labels_multi = {0: 'healthy', 1: 'mild', 2: 'moderate'} | |
#model = tf.keras.models.load_model('/content/drive/MyDrive/project_image_2023_NO/saved_models/saved_model/densenet') | |
#labels = ['Healthy', 'Patient'] | |
#labels = {0: 'healthy', 1: 'patient'} | |
import gradio as gr | |
import requests | |
import torch | |
import torch.nn as nn | |
from PIL import Image | |
from torchvision.models import resnet50 | |
from torchvision.transforms import functional as F | |
import numpy as np | |
import tensorflow as tf | |
from transformers import pipeline | |
# load the binary classification model | |
model_binary = tf.keras.models.load_model("CNN_binary") | |
# load the multi-label classification model | |
model_multi = tf.keras.models.load_model("CNN_multiclass") | |
# define the labels for the multi-label classification model | |
labels_multi = {0: 'healthy', 1: 'mild', 2: 'moderate'} | |
def classify_binary(inp): | |
inp = inp.reshape((-1, 224, 224, 3)) | |
inp = tf.keras.applications.densenet.preprocess_input(inp) | |
prediction = model_binary.predict(inp) | |
confidence = float(prediction[0]) | |
label = {0: 'healthy', 1: 'patient'} | |
return {label: confidence} | |
def classify_multi(inp): | |
inp = inp.reshape((-1, 224, 224, 3)) | |
inp = tf.keras.applications.densenet.preprocess_input(inp) | |
prediction = model_multi.predict(inp) | |
confidences = {labels_multi[i]: float(prediction[0][i]) for i in range(len(labels_multi))} | |
return confidences | |
binary_interface = gr.Interface(fn=classify_binary, | |
inputs=gr.inputs.Image(shape=(224, 224)), | |
outputs=gr.outputs.Label(num_top_classes=2), | |
title="Binary Image Classification", | |
description="Classify an image as healthy or patient.", | |
examples=[['300104.png']] | |
) | |
multi_interface = gr.Interface(fn=classify_multi, | |
inputs=gr.inputs.Image(shape=(224, 224)), | |
outputs=gr.outputs.Label(num_top_classes=3), | |
title="Multi-class Image Classification", | |
description="Classify an image as healthy, mild or moderate.", | |
examples=[['300104.png']] | |
) | |
demo = gr.TabbedInterface([binary_interface, multi_interface], ["Binary", "Multi-class"]) | |
demo.launch() | |