Update app.py

app.py

@@ -23,7 +23,7 @@ import numpy as np
 from keras.models import model_from_json
 from keras.preprocessing import image
 #from keras.applications.vgg16 import VGG16, preprocess_input
-import heapq
+#import heapq
 
 file = open("focusondriving.json", 'r')
 model_json2 = file.read()
@@ -53,19 +53,19 @@ def predict_image(pic):
     preds = loaded_model.predict(x)
     preds = list(preds[0])
 
-    list_desc_order = heapq.nlargest(2, range(len(preds)), key=preds.__getitem__)
-    result1 = f'c{list_desc_order[0]}'
-    result2 = '-'
-    result2_ = 0
-    if preds[list_desc_order[1]] > 0.3:
-        result2 = f'c{list_desc_order[1]}'
-        result2_  = round(preds[list_desc_order[1]], 2)
-    txt = f"category {directory} result 1 {result1} {round(preds[list_desc_order[0]],2)} | result2 {result2} {result2_}"
-    txt = f"categoria {directory}"
+    #list_desc_order = heapq.nlargest(2, range(len(preds)), key=preds.__getitem__)
+    #result1 = f'c{list_desc_order[0]}'
+    #result2 = '-'
+    #result2_ = 0
+    #if preds[list_desc_order[1]] > 0.3:
+    #    result2 = f'c{list_desc_order[1]}'
+    #    result2_  = round(preds[list_desc_order[1]], 2)
+    #txt = f"category {directory} result 1 {result1} {round(preds[list_desc_order[0]],2)} | result2 {result2} {result2_}"
+    #txt = f"categoria {directory}"
     
-    score = round(preds[list_desc_order[0]], 2)*100
-    score = int(score)
-    txt2 = f"resultado: {class_dict.get(result1)}     probabilidad {score}%"
+    #score = round(preds[list_desc_order[0]], 2)*100
+    #score = int(score)
+    #txt2 = f"resultado: {class_dict.get(result1)}     probabilidad {score}%"
     txt3="pepe"
     return txt3