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Dream emotion dist #568

Merged
merged 35 commits into from
Nov 16, 2023
Merged

Dream emotion dist #568

merged 35 commits into from
Nov 16, 2023

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asleepann
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@asleepann asleepann commented Sep 12, 2023

Made dream_emotion dist with the following annotators: bot-emotion-classifier (determines emotion and mood of the bot) and emotional-bot-response (change hypotheses text according to emotion and mood of the bot). Response selector in dream_emotion selects emotional text of the hypothesis.
(old PR: #563)
DO NOT DELETE THIS BRANCH

del api_conf[key]
break

logger.info(f"Available APIs: {', '.join([api['display_name'] for api in api_conf.values()])}")
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you do not use API_CONFIGS and api_conf, so I suggest to remove them (and from yml files too)

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removed

@@ -0,0 +1,25 @@
name: emotion-response_selector
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i suggest to follow unofficial rules and name components with "_" not "-".
With "-" we name containers.
(it was important rule when we were using Kubernetes, so we just follow it for now to be able to use kuber in future without breaking changes)

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fixed
also, thank you for explanations :)

@@ -0,0 +1,23 @@
name: bot-emotion-classifier
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same

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btw why bot emotion cls is annotator not bot uttr annotator?

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name fixed
don't know why I didn't fixed type of the annotator before)
now should be correct: changed state manager method and formatters

SERVICE_PORT: 8050
SERVICE_NAME: emotional-bot-response
WORK_DIR: annotators/emotional_bot_response
FLASK_APP: server
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no need to write it here

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removed

build:
args:
SERVICE_PORT: 8050
SERVICE_NAME: emotional-bot-response
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with _

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fixed

N_UTTERANCES_CONTEXT: 5
FILTER_TOXIC_OR_BADLISTED: 1
EMOTIONAL_RESPONSES: 1
FLASK_APP: server
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you may not put it here- it's below

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removed

state_formatters/dp_formatters.py Show resolved Hide resolved
hypots = [h["text"] for h in hypotheses]

bot_mood_label = (
dialog["bot_utterances"][-1]["annotations"].get("bot_emotion_classifier", {}).get("bot_mood_label", "happy")
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what if this fucntion will be triggered on the 1st step of the dialog? When no bot uttrs

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added handling of this situation

if GENERATIVE_SERVICE_CONFIG:
with open(f"common/generative_configs/{GENERATIVE_SERVICE_CONFIG}", "r") as f:
GENERATIVE_SERVICE_CONFIG = json.load(f)
GENERATIVE_TIMEOUT = int(getenv("GENERATIVE_TIMEOUT", 30))
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float

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fixed

def make_prompt(sentence, emotion="neutral", mood="happy", intensity=7):
prompt = f"""You are a writer who needs to rewrite a sentence to express
specific emotions, moods, and intensity levels. Your task is to generate
a new sentence that conveys the desired emotions, moods, and emotion intensity
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put the prompt into a file. you may replace in the following view. In the prompt put:

- Sentence: {SENTENCE}

and then do: prompt.replace("{SENTENCE}", sentence)

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done

except Exception as e:
sentry_sdk.capture_exception(e)
logger.exception(e)
response = None
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None is a bad choice. If response is str, then set default value to ""

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fixed

except Exception as exc:
logger.exception(exc)
sentry_sdk.capture_exception(exc)
result = {"hypotheses": "fallback standard response"}
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"" empty fallback

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fixed

bot_mood_labels = ["angry"]
bot_emotions = ["anger"]
responses = rewrite_sentences(sentences, bot_mood_labels, bot_emotions)
logger.info("TEST. Sentences: {}".format(sentences))
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more common to use f-string for now which is:

f"TEST. Sentences: {sentences}"

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fixed

bot_emotion = request.json.get("bot_emotion", [])

results = []
for i in range(len(sentences)):
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just FYI
you can do:

for sent, mood_lab, emot in zip(sentences, bot_mood_labels, bot_emotions):

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thanks! done


sentences = request.json.get("sentences", [])
bot_mood_label = request.json.get("bot_mood_label", [])
bot_emotion = request.json.get("bot_emotion", [])
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multiple bot_emotionS (and mood labelS)

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fixed

build:
args:
SERVICE_PORT: 8048
SERVICE_NAME: emotion-response_selector
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name of component with only _
name of container with only -

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fixed

"metadata": {
"display_name": "Dream",
"author": "DeepPavlov",
"description": "Main version of DeepPavlov Dream Socialbot",
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change it

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fixed

{
"sentences": hypots,
"bot_mood_label": bot_mood_labels,
"bot_emotion": bot_emotions,
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bot_emotions
bot_mood_labels

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fixed

if GENERATIVE_SERVICE_CONFIG:
with open(f"common/generative_configs/{GENERATIVE_SERVICE_CONFIG}", "r") as f:
GENERATIVE_SERVICE_CONFIG = json.load(f)
GENERATIVE_TIMEOUT = int(getenv("GENERATIVE_TIMEOUT", 30.0))
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Suggested change
GENERATIVE_TIMEOUT = int(getenv("GENERATIVE_TIMEOUT", 30.0))
GENERATIVE_TIMEOUT = float(getenv("GENERATIVE_TIMEOUT", 30.0))

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fixed

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@dilyararimovna dilyararimovna left a comment

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Good Job! thank you!

print("SENTENCE:", sentence)
print("USER EMOTION: ", user_emotion)
print("SENTIMENT: ", sentiment)
print("OLD BOT MOOD: ", bot_mood)
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all prints should be deleted (or changed to logger) -- they will not be shown because of logger

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deleted them (I already had the same info in logger)

annotated_utterances = request.json.get("annotated_utterances", [])
bot_moods = request.json.get("bot_mood", [])

for i in range(len(sentences)):
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just writing to show you how it is more beautiful to write these 4 lines of code in one:

for sentence, annotated_utterance, bot_mood in zip(sentences, annotated_utterances, bot_moods):

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I don't know why but sometimes zip confuses me in code of other people, and I find for i easier to understand, but I will try to use zip instead because it's truly more beautiful :)

)
try:
hypotheses = send_request_to_prompted_generative_service(
"",
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dialog context is a list of utterance not str

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fixed

sentence-ranker:8128, property-extraction:8136, prompt-selector:8135, openai-api-chatgpt:8145,
dff-dream-persona-chatgpt-prompted-skill:8137, dff-dream-faq-prompted-skill:8170,
openai-api-chatgpt-16k:8167, bot-emotion-classifier:8051, emotional-bot-response:8050,
emotion-ranking-based-response-selector:8048"
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i suggest to get rid of extra components in this distribution. For example, factoid compoentns, etc.
So, you me leave components from "in general" prompted distribution (aka https://github.com/deeppavlov/dream/tree/main/assistant_dists/dream_persona_openai_prompted):

sentseg:8011, combined-classification:8087,
sentence-ranker:8128, prompt-selector:8135, openai-api-chatgpt:8145,
dff-dream-persona-chatgpt-prompted-skill:8137, dff-dream-faq-prompted-skill:8170,
openai-api-chatgpt-16k:8167, bot-emotion-classifier:8051, emotional-bot-response:8050,
emotion-ranking-based-response-selector:8048

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done. Hope I removed unnecessary components from all config files...

components/fuipwahef749384h2e.yml Show resolved Hide resolved
- candidate_annotators
required_previous_services: null
state_manager_method: add_bot_utterance
tags: null
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selectors. And in pipeline conf please also fix it. As in skill selector (this is a bug in whole dream -- I am goijng to fix it in a separate pull request for all response selectors. I actually thought I did it previously, but seems like not yet).
https://github.com/deeppavlov/dream/blob/dev/assistant_dists/dream_persona_openai_prompted/pipeline_conf.json#L159-L161 (also find a card for this component and make analogy)

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added tags to response selector in component card and in pipeline_conf

group: candidate_annotators
connector:
protocol: http
timeout: 15.0
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why this timeout is 15 seconds if your generative timeout is 120? fix it. Including in pipeline and othe config files. Maybe you can decrease the generative timeout (it won't take 120 secods because the prompt is yours hard-coded, so it would be a fast response by openai)

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changed generative timeout to 30 sec everywhere


SENTENCE_RANKER_ANNOTATION_NAME = getenv("SENTENCE_RANKER_ANNOTATION_NAME")
SENTENCE_RANKER_SERVICE_URL = getenv("SENTENCE_RANKER_SERVICE_URL")
SENTENCE_RANKER_TIMEOUT = int(getenv("SENTENCE_RANKER_TIMEOUT"))
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Suggested change
SENTENCE_RANKER_TIMEOUT = int(getenv("SENTENCE_RANKER_TIMEOUT"))
SENTENCE_RANKER_TIMEOUT = float(getenv("SENTENCE_RANKER_TIMEOUT"))

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also compare file with the latest version of response selector original (which it is copied from) because I think there were some bug fixes, i want you to check whether you do not have same bugs here

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fixed. Also, added new test files as in ranking_based_response_selector: test.py and conftest.py


if EMOTIONAL_RESPONSES:
emotional_hypotheses = hypotheses[best_id].get("annotations").get("emotional_bot_response")
selected_responses.append(emotional_hypotheses.pop("hypotheses"))
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из emotional_hypotheses не обязательно извлекать текст как pop.
а вот из hypotheses[best_id] надо выдернуть text в любом случае - чтобы убрать лишнее из дикта и оставить там только атрибуты, без ключа text.
плюс нужна проверка, что предсказание аннотатора, то есть этот эмо респонс, не пустой.

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done

@@ -0,0 +1,9 @@
SERVICE_PORT: 8048
SERVICE_NAME: emotion_response_selector
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ты его в пайплайн конфе используешь как response_selector, а не emotion_response_selector. Так что поменяй тут название обратно (и в docker compose yml тоже)

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done

build:
args:
SERVICE_PORT: 8048
SERVICE_NAME: emotion_response_selector
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ты его в пайплайн конфе используешь как response_selector, а не emotion_response_selector. Так что поменяй тут название обратно (и в docker compose yml тоже)

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done

dialog_formatter: state_formatters.dp_formatters:bot_mood_emotion_formatter
response_formatter: state_formatters.dp_formatters:simple_formatter_service
previous_services: skills
required_previous_services: skills
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не надо делать это required. пусть тут будет null

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done

build:
args:
SERVICE_PORT: 8048
SERVICE_NAME: emotion_response_selector
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ты его в пайплайн конфе используешь как response_selector, а не emotion_response_selector. Так что поменяй тут название обратно (и в docker compose yml тоже)

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done

{
"bot_mood": new_bot_mood,
"bot_mood_label": new_bot_mood_label,
"bot_emotion": bot_emotion,
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oops... I have noticed previously.
So, you need to create an empty list result, and append those dicts to it on every iteration of for cycle above. and then jsonify result

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so, you input and output batches would be of the same length. because now they are input of, for example, 5 samples, and output of length 1 anyway

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done

model_type: NN-based
is_customizable: true
author: publisher@deeppavlov.ai
description: Propose alternative hypotheses with specified emotional color.
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if it proposes a hypotheses, why it is a candidate annotator NOT a skill?
I suggest to make a few changes and move it to skills

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This is not a skill, because it uses hypotheses and changes them according to specified emotions. Yes, it sounds confusing, we initially discussed this too, but we need to finish it for the AC report. I can change the description in the component

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Changed description

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so, it doesn't produce a new one hypotheses, but processes existing hypothesEs?

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yes

"is_enabled": true,
"source": {
"component": "components/rFC0YJOoDFvS.yml",
"service": "services/agent_services/service_configs/dream"
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component above card should be created a new one (in the card in one place the title of the dist is used, so it is unique). And dream -> dream_emotion

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done

"is_enabled": true,
"source": {
"component": "components/sbDcAqiNqxFz.yml",
"service": "services/agent_services/service_configs/dream"
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component above card should be created a new one (in the card in one place the title of the dist is used, so it is unique). And dream -> dream_emotion

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done

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image

@dilyararimovna dilyararimovna merged commit e12986e into dev Nov 16, 2023
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