Update app.py

app.py

@@ -6,7 +6,7 @@ import numpy as np
 import cv2
 import tensorflow as tf
 import h5py
-def sepia(img):
+def sepia(img,plant_type):
     label_disease = {
         0 : 'Apple___Apple_scab',
         1 : 'Apple___Black_rot',
@@ -80,8 +80,13 @@ def sepia(img):
     y_pred = dnn_model.predict(process_img)
     print("y pred",y_pred)
     indx = np.argmax(y_pred)
-    print(label_disease[indx])
-    return label_disease[indx],y_pred[indx]
+    max_prob_indx = plant_label_disease[plant_type.lower()][0]
+    for disease in plant_label_disease[plant_type.lower()]:
+            print(disease,y_pred[0][disease],max_prob_indx,y_pred[0][max_prob_indx])
+            if y_pred[0][disease]>y_pred[0][max_prob_indx]:
+                max_prob_indx = disease
+    print(label_disease[indx],y_pred[indx],label_disease[max_prob_indx],y_pred[max_prob_indx])
+    return label_disease[indx],y_pred[indx],label_disease[max_prob_indx],y_pred[max_prob_indx]
 
-demo = gr.Interface(sepia, [gr.Image(),gr.Radio(["Apple","Blueberry","Cherry","Corn","Grape","Orange","Peach","Pepper","Potato","Raspberry","Soybean","Squash","Strawberry","Tomato"])],["text","number"])
+demo = gr.Interface(sepia, [gr.Image(),gr.Radio(["Apple","Blueberry","Cherry","Corn","Grape","Orange","Peach","Pepper","Potato","Raspberry","Soybean","Squash","Strawberry","Tomato"])],["text","number","text","number"])
 demo.launch(share=True)