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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 | |
| from tensorflow.keras.preprocessing import image as image_utils | |
| from tensorflow.keras.applications import densenet, efficientnet | |
| # load the binary classification model | |
| model_cnn = tf.keras.models.load_model("CNN_binary") | |
| model_efficientnet = tf.keras.models.load_model("efficientNet_binary") | |
| # define the labels for the multi-label classification model | |
| labels_cnn = {0: 'healthy', 1: 'patient'} | |
| labels_efficientnet = {0: 'healthy', 1: 'patient'} | |
| def classify_cnn(inp): | |
| img = np.array(inp) | |
| img = img.reshape((1, 224, 224, 3)) | |
| img = densenet.preprocess_input(img) | |
| prediction = model_cnn.predict(img) | |
| confidence = float(prediction[0]) | |
| return {labels_cnn[prediction.argmax()]: confidence} | |
| def classify_efficientnet(inp): | |
| img = np.array(inp) | |
| img = img.reshape((1, 224, 224, 3)) | |
| img = efficientnet.preprocess_input(img) | |
| prediction = model_efficientnet.predict(img) | |
| confidence = float(prediction[0]) | |
| return {labels_efficientnet[prediction.argmax()]: confidence} | |
| cnn_interface = gr.Interface(fn=classify_cnn, | |
| inputs=gr.inputs.Image(shape=(224, 224)), | |
| outputs=gr.outputs.Label(num_top_classes=2), | |
| title="CNN Binary Image Classification", | |
| description="Classify an image as healthy or patient using a CNN model.", | |
| examples=[['300104.png']] | |
| ) | |
| efficientnet_interface = gr.Interface(fn=classify_efficientnet, | |
| inputs=gr.inputs.Image(shape=(224, 224)), | |
| outputs=gr.outputs.Label(num_top_classes=2), | |
| title="EfficientNet Binary Image Classification", | |
| description="Classify an image as healthy or patient using an EfficientNet model.", | |
| examples=[['300104.png']] | |
| ) | |
| # create a combined interface with tabs for each binary classification model | |
| demo = gr.Interface([cnn_interface, efficientnet_interface], | |
| "tab", | |
| title="Binary Image Classification", | |
| description="Classify an image as healthy or patient using different binary classification models." | |
| ) | |
| demo.launch() | |