| 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) | |