Spaces:
Sleeping
Sleeping
File size: 2,391 Bytes
1eebc45 6ea82b1 1eebc45 a8b4012 1eebc45 f62790a 1eebc45 5dba2ed 6ea82b1 5dba2ed 6ea82b1 5dba2ed 6ea82b1 f62790a 6ea82b1 bcaa4d3 6ea82b1 bcaa4d3 6ea82b1 bcaa4d3 6ea82b1 bcaa4d3 6ea82b1 bcaa4d3 6ea82b1 8628f1a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
# -*- 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("densenet")
# load the multi-label classification model
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'}
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()
|