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from keras.models import load_model
import cv2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import gradio as gr
import numpy as np
my_model=load_model('Liver_model.h5',compile=True)
heart_model=load_model('Chicken_Heart_model.h5',compile=True)
lu_model=load_model('Lungs_model.h5',compile=True)
auth_model=load_model('post_auth_model.h5',compile=True)
heart_class_name={0:'Dilation(eccentric)',1:'Hepatoma',2:'Hypertrophy(concentric)',3:'Hypertrophy(physiological)',4:'Infraction Damage',5:'Normal'}
heart_result={0:'Critical',1:'Critical',2:'Critical',3:'Critical',4:'Critical',5:'Normal'}
heart_recommend={0:'panadol',1:'peracetamol',2:'ponston',4:'brofon',5:'No Need'}
def Heart_Disease_prediction(img):
img = img.reshape((1, img.shape[0], img.shape[1], img.shape[2]))
# Create the data generator with desired properties
datagen = ImageDataGenerator(
rotation_range=30,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode="nearest",
)
# Generate a batch of augmented images (contains only the single image)
augmented_images = datagen.flow(img, batch_size=1)
# Get the first (and only) augmented image from the batch
augmented_img = next(augmented_images)[0]
img=cv2.resize(augmented_img.astype(np.uint8),(128,128))
class_no=heart_model.predict(img.reshape(1,128,128,3)).argmax()
name="Heart Disease Class Name: "+""+heart_class_name.get(class_no)
result="Heart Disease result: "+""+heart_result.get(class_no)
recommend="Heart Medicine Recommend: "+""+heart_recommend.get(class_no)
return name,result,recommend
liver_class_num={0:'Healthy',1:'Un-Healthy'}
liver_result={0:'Normal',1:'Critical'}
liver_recommend={0:'No need Medicine',1:'Panadol'}
def Liver_Predict(Image):
Image=cv2.resize(Image,(224,224))
class_no=my_model.predict(Image.reshape(1,224,224,3)).argmax()
class_name="Liver Class Name: "+""+liver_class_num.get(class_no)
liver_class_result="Liver Class Result: "+""+liver_result.get(class_no)
liver_class_recommend="Liver Class Recommend: "+""+liver_recommend.get(class_no)
return class_name,liver_class_result,liver_class_recommend
lung_classes={0:'Lungs of infected chickens showing congestion, hemorrhage and consolidation with traces of fibrin at 24 hpi (hours post-infection)',
1:'gradual paleness and reduction in size of lungs at 2 dpi (days post-infection)',
2:'gradual paleness and reduction in size of lung at 3 dpi (days post-infection)',
3:'severe congestion, hemorrhage, and gradual shrinking of lungs at 4 dpi (days post-infection)',
4:'severe congestion, hemorrhage, and gradual shrinking of lungs at 5 dpi (days post-infection)'}
lung_result={0:'critical',1:'critical',2:'critical',3:'critical',4:'critical'}
lung_recommend={0:'panadol',1:'peracetamol',2:'ponston',4:'brofon'}
def Lungs_predict(image):
image=cv2.resize(image,(224,224))
lung_no=lu_model.predict(image.reshape(1,224,224,3)).argmax()
lung_disease_name="Lung Disease Name: "+""+lung_classes.get(lung_no)
lung_r="Lung result: "+""+lung_result.get(lung_no)
lung_re="Lung recommendation: "+""+lung_recommend.get(lung_no)
return lung_disease_name,lung_r,lung_re
def main(Image):
img=cv2.resize(Image,(224,224))
indx=auth_model.predict(img.reshape(1,224,224,3)).argmax()
if indx==0:
Name='Unkown'
result='N/A'
recommend='N/A'
return Name,result,recommend,Name,result,recommend,Name,result,recommend
else:
heart_n,heart_r,heart_re=Heart_Disease_prediction(Image)
liver_name,liver_r,liver_re=Liver_Predict(Image)
lung_d,lung_r,lung_re=Lungs_predict(Image)
return heart_n,heart_r,heart_re,liver_name,liver_r,liver_re,lung_d,lung_r,lung_re
interface=gr.Interface(fn=main,inputs='image',outputs=[gr.components.Textbox(label="Heart Disease Name"),gr.components.Textbox(label="Heart result Name"),gr.components.Textbox(label="Heart recommend"),
gr.components.Textbox(label="Liver Disease Name"),gr.components.Textbox(label="liver result Name"),gr.components.Textbox(label="Liver recommend"),
gr.components.Textbox(label="Lung Disease Name"),gr.components.Textbox(label="Lung result Name"),gr.components.Textbox(label="Lung recommend")],
title="Postmortem")
interface.launch(debug=True)
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