Update app.py

app.py

@@ -32,21 +32,26 @@ def analyse(img,plant_type):
     y_pred = dnn_model.predict(process_img)
     y_pred = y_pred[0]
     print("y pred",y_pred)
-    indx = np.argmax(y_pred)
-    max_prob_indx = plant_label_disease[plant_type.lower()][0]
+    
+    id = np.argmax(y_pred)
+    p_id = plant_label_disease[plant_type.lower()][0]
+    
     for disease in plant_label_disease[plant_type.lower()]:
-            if y_pred[disease] > y_pred[max_prob_indx]:
-                max_prob_indx = disease
-    indx=str(indx)
-    max_prob_indx=str(max_prob_indx)
-    print(label_disease[indx])
-    print(y_pred[0][indx])
-    print(label_disease[max_prob_indx])
-    print(y_pred[0][max_prob_indx])
-    return int(indx),max_prob_indx,label_disease[indx],y_pred[0][indx],label_disease[max_prob_indx],y_pred[0][max_prob_indx]
+            if y_pred[disease] > y_pred[p_id]:
+                p_id = disease
+    
+    overall_predicted_id = id
+    overall_predicted_name = label_disease[str(overall_predicted_id)]
+    overall_predicted_acc = y_pred[overall_predicted_id]
+    
+    plant_predicted_id =pid
+    plant_predicted_name = label_disease[str(plant_predicted_id)]
+    plant_predicted_acc = y_pred[plant_predicted_id]
+    
+    return plant_predicted_id, plant_predicted_name, plant_predicted_acc, overall_predicted_id, overall_predicted_name, overall_predicted_acc
 
 demo = gr.Interface(analyse, 
                     [gr.Image(),gr.Radio(["Apple","Blueberry","Cherry","Corn","Grape","Orange","Peach","Pepper","Potato","Raspberry","Soybean","Squash","Strawberry","Tomato"])],
-                    ["number","number","text","number","text","number"],
+                    ["number","text","number","number","text","number"],
                    )
 demo.launch(share=True,show_error=True)