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import keras
from keras.models import load_model
import gradio as gr
import cv2
my_model=load_model('Final_Chicken_disease_model.h5',compile=True)
auth_model=load_model('auth_model.h5',compile=True)
name_disease={0:'Coccidiosis',1:'Healthy',2:'New Castle Disease',3:'Salmonella'}
result={0:'Critical',1:'No issue',2:'Critical',3:'Critical'}
recommend={0:'Panadol',1:'You have no need Medicine',2:'Percetamol',3:'Ponston'}
def predict(image):
image_check=cv2.resize(image,(224,224))
indx=auth_model.predict(image_check.reshape(1,224,224,3)).argmax()
if indx==0:
image=cv2.resize(image,(224,224))
indx=my_model.predict(image.reshape(1,224,224,3)).argmax()
name=name_disease.get(indx)
status=result.get(indx)
recom=recommend.get(indx)
return name,status,recom
else:
name='Unkown Image'
status='N/A'
recom='N/A'
return name,status,recom
interface=gr.Interface(fn=predict,inputs=[gr.Image(label='upload Image')],outputs=[gr.components.Textbox(label="Disease Name"),gr.components.Textbox(label="result"),gr.components.Textbox(label='Medicine Recommend')],
examples=[['disease.jpg'],['ncd.jpg']])
interface.launch(debug=True)
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