update app

app.py

@@ -327,18 +327,53 @@ class utilJSON:
      for key in data:
         out.append(key)
      return(out)
+    ###
+    ### funcion "flatten_json" tomada de https://levelup.gitconnected.com/a-deep-dive-into-nested-json-to-data-frame-with-python-69bdabb41938 
+    ### Renu Khandelwal Jul 23, 2023
+    def flatten_json(self,y):
+        try:
+            out = {}
+    
+            def flatten(x, name=''):
+                if type(x) is dict:
+                    for a in x:
+                        flatten(x[a], name + a + '_')
+                elif type(x) is list:
+                    i = 0
+                    for a in x:
+                        flatten(a, name + str(i) + '_')
+                        i += 1
+                else:
+                    out[name[:-1]] = x
+    
+            flatten(y)
+            return out
+        except json.JSONDecodeError:
+            print("Error: The JSON document could not be decoded.")
+        except TypeError:
+            print("Error: Invalid operation or function argument type.")
+        except KeyError:
+            print("Error: One or more keys do not exist.")
+        except ValueError:
+            print("Error: Invalid value detected.")
+        except Exception as e:
+            # Catch any other exceptions
+            print(f"An unexpected error occurred: {str(e)}") 
         
     def obtener_dataframe(self,data):
         claves=self.obtener_keys_json(data)
+        print(claves)
         if len(claves)==1:
-            df = pd.json_normalize(
-            data=data[claves[0]],
-            #meta=['title', 'author'],
-            #record_path='reviews',
-            #errors='raise'
-            )
+           
+            #Flatten nested dictionaries and lists
+            data_flattened = [self.flatten_json(class_info) for class_info in data[claves[0]]]
+            # Create DataFrame from flattened JSON
+            df = pd.DataFrame(data_flattened)
+           
         else:
-            df = pd.json_normalize(data)
+            #df = pd.json_normalize(data)
+            data_flattened = [self.flatten_json(class_info) for class_info in data]
+            df = pd.DataFrame(data_flattened)
             
         return df
 modelo = ModeloDataset()