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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 of 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 = 'Unknown 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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