Create app.py

app.py

@@ -0,0 +1,28 @@
+from keras.models import load_model
+import cv2
+import json
+import gradio as gr
+
+model_data=load_model("SkinCancerModel.h5",compile=True)
+f=open("data.json")
+
+data=json.load(f)
+
+
+cancer_class_list=list(data)
+
+
+def Canccer_Prediction(image):
+  image=cv2.resize(img,(180,180))/255.0
+  result=model_data.predict(image.reshape(1,180,180,3)).argmax()
+
+  return cancer_class_list[result],data[cancer_class_list[result]]['description'],data[cancer_class_list[result]]['symptoms'],data[cancer_class_list[result]]['causes'],data[cancer_class_list[result]]['treatement-1'],data[cancer_class_list[result]]['treatement-2']
+
+
+interface=gr.Interface(fn=Canccer_Prediction,
+                       inputs="image",
+                       outputs=[gr.components.Textbox(label="Cancer Name"),gr.components.Textbox(label="Description"),gr.components.Textbox(label="Symptoms"),gr.components.Textbox(label="Causes"),gr.components.Textbox(label="Treatment 1"),gr.components.Textbox(label="Treatment 2")],
+                       enablue_queu=True)
+interface.launch(debug=True)  
+
+