Update app.py

app.py

@@ -53,21 +53,21 @@ 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
+    return txt2
     
     
 iface = gr.Interface(