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import os
from .common import TaskSpec, ParsedAnswer, Question
from .exceptions import GPTOutputParseException, GPTMaxTriesExceededException
import threading
import base64
import io
from typing import List, Tuple, Union
from loguru import logger
from google.generativeai.types import generation_types
from copy import deepcopy
import google.generativeai as genai
import time
import PIL
class GeminiModel(object):
def __init__(self, api_key:str,
task:TaskSpec,
model:str="gemini-pro-vision"):
self.gemini_key:str = api_key
self.task:TaskSpec = task
self.model:str = model
def ask(self, payload:dict, n_choices=1) -> Tuple[List[dict], List[dict]]:
"""
args:
payload: json dictionary, prepared by `prepare_payload`
"""
def gemini_thread(idx, payload, results):
mod_payload = payload
config_instance = generation_types.GenerationConfig(
max_output_tokens=payload["max_tokens"],
)
try:
raw_response = client.generate_content(
contents=payload["messages"],
generation_config=config_instance
)
except Exception as e:
raise e
response = {'content' : raw_response.text}
results[idx] = {"message": response, "metadata": raw_response}
return
genai.configure(api_key=self.gemini_key)
client = genai.GenerativeModel(model_name=self.model,
safety_settings= None,
generation_config = None
)
assert n_choices >= 1
results = [None] * n_choices
if n_choices > 1:
gemini_jobs = [threading.Thread(target=gemini_thread,
args=(idx, payload, results))
for idx in range(n_choices)]
for job in gemini_jobs:
job.start()
for job in gemini_jobs:
job.join()
else:
gemini_thread(0, payload, results)
messages:List[dict] = [ res["message"] for res in results]
metadata:List[dict] = [ res["metadata"] for res in results]
return messages, metadata
@staticmethod
def prepare_payload(question:Question,
max_tokens=1000,
verbose:bool=False,
prepend:Union[dict, None]=None,
**kwargs
) -> dict:
strings = []
images = []
for el in question.get_json(save_local=True):
if 'text' in el:
strings.append(el['text'])
elif 'image_url' in el:
#Convert the binary encoded version to PIL.image
base64enc_image = el['image_url']['url'].split(',', 1)[1]
base64_image_str = base64enc_image # the Base64-encoded string
image_data = base64.b64decode(base64_image_str)
image_data_io = io.BytesIO(image_data)
# Read the image from the BytesIO object
pil_image = PIL.Image.open(image_data_io)
images.append(pil_image)
string_message = "\n".join(strings)
messages = [string_message]
for image in images:
messages.append(image)
payload = {
"messages": messages,
"max_tokens": max_tokens,
}
return payload
def rough_guess(self, question:Question, max_tokens=1000,
max_tries=1, query_id:int=0,
verbose=False,
**kwargs):
p = self.prepare_payload(question, max_tokens = max_tokens, verbose=verbose, prepend=None,
model=self.model)
ok = False
while not ok:
response, meta_data = self.ask(p)
response = response [0]
try:
parsed_response = self.task.answer_type.parser(response["content"])
except GPTOutputParseException as e:
if not os.path.exists('errors/'):
# Create the directory if it doesn't exist
os.makedirs('errors/')
error_saved = f'errors/{time.strftime("%Y-%m-%d-%H-%M-%S")}.json'
with open(error_saved, "w") as f:
f.write(p_ans.code)
# logger.warning(f"The following was not parseable. Saved in {error_saved}.")
reattempt += 1
if reattempt > max_tries:
logger.error(f"max tries ({max_tries}) exceeded.")
raise GPTMaxTriesExceededException
logger.warning(f"Reattempt #{reattempt} querying LLM")
continue
ok = True
return parsed_response, response, meta_data, p
def many_rough_guesses(self, num_threads:int,
question:Question, max_tokens=1000,
verbose=False, max_tries=1,
) -> List[Tuple[ParsedAnswer, str, dict, dict]]:
"""
Args:
num_threads : number of independent threads.
all other arguments are same as those of `rough_guess()`
Returns
List of elements, each element is a tuple following the
return signature of `rough_guess()`
"""
p = self.prepare_payload(question, max_tokens = max_tokens, verbose=verbose, prepend=None,
model=self.model)
#print('In many rough: ', p)
n_choices = num_threads
ok = False
reattempt = 0
while not ok:
response, meta_data = self.ask(p, n_choices=n_choices)
try:
parsed_response = [self.task.answer_type.parser(r["content"]) for r in response]
except GPTOutputParseException as e:
# logger.warning(f"The following was not parseable:\n\n{response}\n\nBecause\n\n{e}")
reattempt += 1
if reattempt > max_tries:
logger.error(f"max tries ({max_tries}) exceeded.")
raise GPTMaxTriesExceededException
logger.warning(f"Reattempt #{reattempt} querying LLM")
continue
ok = True
return parsed_response, response, meta_data, p
def run_once(self, question:Question, max_tokens=1000, **kwargs):
q = self.task.first_question(question)
p_ans, ans, meta, p = self.rough_guess(q, max_tokens=max_tokens, **kwargs)
return p_ans, ans, meta, p |