| 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) | |