| from keras.models import load_model | |
| import cv2 | |
| import json | |
| import gradio as gr | |
| model_result=load_model("fyp.h5",compile=True) | |
| f=open("fyp file.json") | |
| data=json.load(f) | |
| Tumor_Classes=list(data) | |
| def Tumor_Prediction(image): | |
| image=cv2.resize(image,(32,32))/255.0 | |
| result=model_result.predict(image.reshape(1,32,32,3)).argmax() | |
| return Tumor_Classes[result],data[Tumor_Classes[result]]['Description'],data[Tumor_Classes[result]]['Causes'],data[Tumor_Classes[result]]['Symptoms'],data[Tumor_Classes[result]]['Treatment'] | |
| interface=gr.Interface(fn=Tumor_Prediction, | |
| inputs="image", | |
| outputs=[gr.components.Textbox(label="Tumor Name"),gr.components.Textbox(label="Description"),gr.components.Textbox(label="Causes"),gr.components.Textbox(label="Symptoms"),gr.components.Textbox(label="Treatment")], | |
| enable_queu=True) | |
| interface.launch(debug=True) | |