Spaces:
Sleeping
Sleeping
File size: 1,855 Bytes
72f9149 |
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 |
pip install gradio==3.14.0
import gradio as gr
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
import pickle
from tensorflow.keras.models import load_model
# Load the RGB to hyperspectral conversion model
converion_model = load_model('/kaggle/input/convmo/Conversion_model.h5')
# Load the cancer classification model
#cancer_model = pickle.load(open("/kaggle/input/classi/ClassRF (1).pkl", "rb"))
cancer_model = pickle.load(open("/kaggle/input/logistic/LRclass.pkl", "rb"))
def classify(rgb_image):
img = Image.fromarray(rgb_image.astype('uint8'), 'RGB')
img = img.resize((272, 512))
arr = np.array(img).astype('float32') / 255.0
new_size = (272, 512)
resized_rgb_img = tf.image.resize(arr, new_size)
resized_rgb_img = tf.reshape(resized_rgb_img, (272, 512, 3))
resized_rgb_img = np.expand_dims(resized_rgb_img , axis=0)
# Convert the RGB image to hyperspectral using your model
hyperspectral_image = converion_model(resized_rgb_img)
hyperspectral_image = tf.image.resize(hyperspectral_image, new_size)
hyperspectral_image = tf.reshape(hyperspectral_image, (272, 512, 16))
imgplot = hyperspectral_image.numpy().astype(np.float32)
imgplot= imgplot.reshape(-1, 272*512*16)
prediction = cancer_model.predict(imgplot)
if np.argmax(prediction) == 0:
x= "cancer"
else:
x="not a cancer"
return x
# Define the Gradio interface
#image_input = gr.inputs.Image()
output_label = gr.components.Label()
#output_label=["text"]
image_input = gr.components.Image()
gr.Interface(
classify,
image_input,
output_label,
title="RGB to Hyperspectral Conversion and Cancer Classification",
description="Upload an RGB image and get a prediction of whether you have skin cancer or not."
).launch(share=True)
|