# Buggy code
def as_story_string(self, e2e=False):
    if self.intent:
        if self.entities:
            ent_string = json.dumps(
                {ent["entity"]: ent["value"] for ent in self.entities}
            )
        else:
            ent_string = ""

        parse_string = "{intent}{entities}".format(
            intent=self.intent.get("name", ""), entities=ent_string
        )
        if e2e:
            message = md_format_message(self.text, self.intent, self.entities)
            return "{}: {}".format(self.intent.get("name"), message)
        else:
            return parse_string
    else:
        return self.text


# Traceback (TypeError)
2019-05-29 12:17:24 INFO     root  - Generating grammar tables from /usr/lib/python3.6/lib2to3/Grammar.txt
2019-05-29 12:17:24 INFO     root  - Generating grammar tables from /usr/lib/python3.6/lib2to3/PatternGrammar.txt

WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.

2019-05-29 12:17:24 WARNING  rasa.core.interpreter  - No NLU model found. Using RegexInterpreter instead.
2019-05-29 12:17:24.816998: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-05-29 12:17:24.821672: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3591990000 Hz
2019-05-29 12:17:24.822324: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x66de000 executing computations on platform Host. Devices:
2019-05-29 12:17:24.822342: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
Bot loaded. Visualisation at http://localhost:5006/visualization.html.
Type a message and press enter (press 'Ctr-c' to exit).
Processed Story Blocks: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1804.78it/s, # trackers=1]
? Your input -> HI
? Your NLU model classified '/HI' with intent 'HI' and there are no entities, is this correct?  Yes
------
Chat History

#    Bot                     You
─────────────────────────────────────────
1    action_listen
─────────────────────────────────────────
2                                   /HI
intent: HI 1.00


Current slots:
date_slot: None, dept: None, doc_slot: None, name: None, requested_slot: None, time: None, time_slot: None

------
2019-05-29 12:17:29 ERROR    rasa.core.training.interactive  - An exception occurred while recording messages.
Traceback (most recent call last):
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/interactive.py", line 1349, in record_messages
endpoint, sender_id, finetune, sender_ids, plot_file
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/interactive.py", line 883, in _predict_till_next_listen
sender_ids, plot_file, endpoint, unconfirmed=[ActionExecuted(action_name)]
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/interactive.py", line 1274, in _plot_trackers
event_sequences[-1], event_sequences, output_file=None, max_history=2
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/visualization.py", line 467, in visualize_neighborhood
else sanitize(message.get("intent", {})).get("name", "  ?  "),
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/visualization.py", line 364, in sanitize
return re.sub(r"""[&amp;\\\<\>"'%();+]""", "", s)
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/re.py", line 191, in sub
return _compile(pattern, flags).sub(repl, string, count)
TypeError: expected string or bytes-like object
2019-05-29 12:17:29 ERROR    asyncio  - Task exception was never retrieved
future: <Task finished coro=<_serve_application.<locals>.run_interactive_io() done, defined at /home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/interactive.py:1391> exception=TypeError('expected string or bytes-like object',)>
Traceback (most recent call last):
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/interactive.py", line 1399, in run_interactive_io
sender_id=uuid.uuid4().hex,
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/interactive.py", line 1349, in record_messages
endpoint, sender_id, finetune, sender_ids, plot_file
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/interactive.py", line 883, in _predict_till_next_listen
sender_ids, plot_file, endpoint, unconfirmed=[ActionExecuted(action_name)]
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/interactive.py", line 1274, in _plot_trackers
event_sequences[-1], event_sequences, output_file=None, max_history=2
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/visualization.py", line 467, in visualize_neighborhood
else sanitize(message.get("intent", {})).get("name", "  ?  "),
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/site-packages/rasa/core/training/visualization.py", line 364, in sanitize
return re.sub(r"""[&amp;\\\<\>"'%();+]""", "", s)
File "/home/sfrplindia1/Work/SantaFe/Raghuram/chatbots/rasa_15/lib/python3.6/re.py", line 191, in sub
return _compile(pattern, flags).sub(repl, string, count)
TypeError: expected string or bytes-like object

# Fixed code
def as_story_string(self, e2e=False):
    if self.intent:
        if self.entities:
            ent_string = json.dumps(
                {ent["entity"]: ent["value"] for ent in self.entities},
                ensure_ascii=False,
            )
        else:
            ent_string = ""

        parse_string = "{intent}{entities}".format(
            intent=self.intent.get("name", ""), entities=ent_string
        )
        if e2e:
            message = md_format_message(self.text, self.intent, self.entities)
            return "{}: {}".format(self.intent.get("name"), message)
        else:
            return parse_string
    else:
        return self.text