Update app.py

app.py

@@ -21,7 +21,7 @@ import numpy as np
 
 
 from keras.models import model_from_json
-#from keras.preprocessing import image
+from keras.preprocessing import image
 #from keras.applications.vgg16 import VGG16, preprocess_input
 #import heapq
 
@@ -30,7 +30,7 @@ model_json2 = file.read()
 #file.close()
 loaded_model = model_from_json(model_json2)
 #loaded_model = model_from_json("focusondriving.json")
-loaded_model.load_weights("focusondriving.h5")
+#loaded_model.load_weights("focusondriving.h5")
 
 class_dict = {
     'c0': 'hands on the wheel',
@@ -46,26 +46,26 @@ class_dict = {
 }
 
 def predict_image(pic):
-   # img = image.load_img(pic, target_size=(224, 224))
-   # x = image.img_to_array(img) 
-   # x = np.expand_dims(x, axis=0)
-   # x = preprocess_input(x)
-  #  preds = loaded_model.predict(x)
-   # preds = list(preds[0])
+    img = image.load_img(pic, target_size=(224, 224))
+    x = image.img_to_array(img) 
+    x = np.expand_dims(x, axis=0)
+    #x = preprocess_input(x)
+    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
     
@@ -82,7 +82,7 @@ iface = gr.Interface(
     
     
     interpretation="default",
-    title = 'FER - Facial Expression Recognition',
+    title = 'FER - Facial Expression Recognitionllll',
     description = 'Probablemente nos daremos cuenta de que muchas veces se miente cuando se tratan las emociones, ¿pero nuestra cara también miente? https://saturdays.ai/2022/03/16/detectando-emociones-mediante-imagenes-con-inteligencia-artificial/ ',
     
     theme = 'grass'