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import base64
from ctm.utils.exponential_backoff import exponential_backoff
class BaseProcessor(object):
_processor_registry = {} # type: ignore[var-annotated] # FIX ME
@classmethod
def register_processor(cls, processor_name): # type: ignore[no-untyped-def] # FIX ME
def decorator(subclass): # type: ignore[no-untyped-def] # FIX ME
cls._processor_registry[processor_name] = subclass
return subclass
return decorator
def __new__(cls, processor_name, *args, **kwargs): # type: ignore[no-untyped-def] # FIX ME
if processor_name not in cls._processor_registry:
raise ValueError(
f"No processor registered with name '{processor_name}'"
)
return super(BaseProcessor, cls).__new__(
cls._processor_registry[processor_name]
)
def set_model(self): # type: ignore[no-untyped-def] # FIX ME
raise NotImplementedError(
"The 'set_model' method must be implemented in derived classes."
)
@staticmethod
def process_image(image_path): # type: ignore[no-untyped-def] # FIX ME
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
@staticmethod
def process_audio(audio_path): # type: ignore[no-untyped-def] # FIX ME
return None
@staticmethod
def process_video(video_path): # type: ignore[no-untyped-def] # FIX ME
return None
def ask(self, query, context, image_path, audio_path, video_path): # type: ignore[no-untyped-def] # FIX ME
gist = self.ask_info( # type: ignore[no-untyped-call] # FIX ME
query, context, image_path, audio_path, video_path
)
score = self.ask_score(query, gist, verbose=True) # type: ignore[no-untyped-call] # FIX ME
return gist, score
@exponential_backoff(retries=5, base_wait_time=1) # type: ignore[misc, no-untyped-call] # FIX ME
def ask_relevance(self, query: str, gist: str) -> float:
response = self.model.chat.completions.create( # type: ignore[attr-defined] # FIX ME
model="gpt-4-0125-preview",
messages=[
{
"role": "user",
"content": "How related is the information ({}) with the query ({})? Answer with a number from 0 to 5 and do not add any other thing.".format(
gist, query
),
},
],
max_tokens=50,
)
score = int(response.choices[0].message.content.strip()) / 5
return score
@exponential_backoff(retries=5, base_wait_time=1) # type: ignore[misc, no-untyped-call] # FIX ME
def ask_confidence(self, query: str, gist: str) -> float:
response = self.model.chat.completions.create( # type: ignore[attr-defined] # FIX ME
model="gpt-4-0125-preview",
messages=[
{
"role": "user",
"content": "How confidence do you think the information ({}) is a mustk? Answer with a number from 0 to 5 and do not add any other thing.".format( # type: ignore[str-format] # FIX ME
gist, query
),
},
],
max_tokens=50,
)
score = int(response.choices[0].message.content.strip()) / 5
return score
@exponential_backoff(retries=5, base_wait_time=1) # type: ignore[misc, no-untyped-call] # FIX ME
def ask_surprise(
self, query: str, gist: str, history_gists: str = None # type: ignore[assignment] # FIX ME
) -> float:
response = self.model.chat.completions.create( # type: ignore[attr-defined] # FIX ME
model="gpt-4-0125-preview",
messages=[
{
"role": "user",
"content": "How surprise do you think the information ({}) is as an output of the processor? Answer with a number from 0 to 5 and do not add any other thing.".format( # type: ignore[str-format] # FIX ME
gist, query
),
},
],
max_tokens=50,
)
score = int(response.choices[0].message.content.strip()) / 5
return score
def ask_score(self, query, gist, verbose=False, *args, **kwargs): # type: ignore[no-untyped-def] # FIX ME
relevance = self.ask_relevance(query, gist, *args, **kwargs)
confidence = self.ask_confidence(query, gist, *args, **kwargs)
surprise = self.ask_surprise(query, gist, *args, **kwargs)
if verbose:
print(
f"Relevance: {relevance}, Confidence: {confidence}, Surprise: {surprise}"
)
return relevance * confidence * surprise
def ask_info(self, query, image_path, *args, **kwargs): # type: ignore[no-untyped-def] # FIX ME
raise NotImplementedError(
"The 'ask_information' method must be implemented in derived classes."
)
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