Create app.py

app.py

@@ -0,0 +1,27 @@
+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)
+