Update app.py

app.py

@@ -46,27 +46,28 @@ 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)
+   # 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])
+  #  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}%"
-    return txt2
+    #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
     
     
 iface = gr.Interface(