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import time
from tqdm import tqdm
def generate_response_remote_wrapper(generate_fn,
queries, model_path, api_key, client, init_sleep=1,
max_retries=10, sleep_factor=1.6):
for k in tqdm(queries):
sleep_time = init_sleep
query = queries[k]['question']
image = queries[k]["figure_path"]
curr_retries = 0
result = None
while curr_retries < max_retries and result is None:
try:
result = generate_fn(image, query, model_path,
api_key=api_key, client=client, random_baseline=False)
except:
print(f"Error {curr_retries}, sleeping for {sleep_time} seconds...")
time.sleep(sleep_time)
curr_retries += 1
sleep_time *= sleep_factor
if result is None:
result = "Error in generating response."
print(f"Error in generating response for {k}")
queries[k]['response'] = result
def get_client_fn(model_path):
if model_path in ['claude-3-sonnet-20240229',
'claude-3-opus-20240229',
'claude-3-haiku-20240307',
'claude-3-5-sonnet-20240620']:
from .claude import get_client_model
# gemini
elif model_path in ['gemini-1.5-pro-001',
'gemini-1.0-pro-vision-001',
'gemini-1.5-flash-001',
'gemini-1.5-pro-exp-0801']:
from .gemini import get_client_model
# gpt
elif model_path in ['gpt-4o-2024-05-13',
'gpt-4-turbo-2024-04-09',
'gpt-4o-mini-2024-07-18']:
from .gpt import get_client_model
# reka
elif model_path in ['reka-core-20240415',
'reka-flash-20240226',
'reka-core-20240415']:
from .reka import get_client_model
# qwen
elif model_path in ['qwen-vl-max',
'qwen-vl-plus']:
from .qwen import get_client_model
# internvl2pro
elif model_path in ['InternVL2-Pro']:
from .internvl2pro import get_client_model
else:
raise ValueError(f"Model {model_path} not supported")
return get_client_model
def get_generate_fn(model_path):
model_name = model_path.split('/')[-1]
# cambrian
if model_name in ['cambrian-34b']:
from .cambrian import generate_response
# chartgemma
elif model_name in ['chartgemma']:
from .chartgemma import generate_response
# claude
elif model_name in ['claude-3-sonnet-20240229',
'claude-3-opus-20240229',
'claude-3-haiku-20240307',
'claude-3-5-sonnet-20240620']:
from .claude import generate_response
# deepseekvl
elif model_name in ['deepseek-vl-7b-chat']:
from .deepseekvl import generate_response
# gemini
elif model_name in ['gemini-1.5-pro-001',
'gemini-1.0-pro-vision-001',
'gemini-1.5-flash-001',
'gemini-1.5-pro-exp-0801']:
from .gemini import generate_response
# gpt
elif model_name in ['gpt-4o-2024-05-13',
'gpt-4-turbo-2024-04-09',
'gpt-4o-mini-2024-07-18']:
from .gpt import generate_response
# idefics2
elif model_name in ['idefics2-8b',
'idefics2-8b-chatty',
'Idefics3-8B-Llama3']:
from .idefics import generate_response
# ixc2
elif model_name in ['internlm-xcomposer2-4khd-7b',
'internlm-xcomposer2-vl-7b']:
from .ixc2 import generate_response
# internvl2
elif model_name in ['InternVL2-26B',
'InternVL2-Llama3-76B']:
from .internvl2 import generate_response
# internvl15
elif model_name in ['InternVL-Chat-V1-5']:
from .internvl15 import generate_response
# llava16
elif model_name in ['llava-v1.6-34b-hf',
'llava-v1.6-mistral-7b-hf']:
from .llava16 import generate_response
# mgm
elif model_name in ['MGM-34B-HD',
'MGM-8B-HD']:
from .mgm import generate_response
# minicpm
elif model_name in ['MiniCPM-Llama3-V-2_5',
elif model_name in ['glm-4v-9b']:
from .glm import generate_response
# moai
elif model_name in ['MoAI-7B']:
from .moai import generate_response
# paligemma
elif model_name in ['paligemma-3b-mix-448']:
from .paligemma import generate_response
# phi3
elif model_name in ['Phi-3-vision-128k-instruct']:
from .phi3 import generate_response
# qwen
elif model_name in ['qwen-vl-max',
'qwen-vl-plus']:
from .qwen import generate_response
# reka
elif model_name in ['reka-core-20240415',
'reka-flash-20240226',
'reka-core-20240415']:
from .reka import generate_response
# sphinx
elif model_name in ['SPHINX-v2-1k']:
from .sphinx2 import generate_response
# vila
elif model_name in ['VILA1.5-40b']:
from .vila15 import generate_response
# ovis
elif model_name in ['Ovis1.5-Llama3-8B',
'Ovis1.5-Gemma2-9B']:
from .ovis import generate_response
# internvl2pro
elif model_name in ['InternVL2-Pro']:
from .internvl2pro import generate_response
elif model_name in ['ChartLlama-13b']:
from .chartllama import generate_response
elif model_name in ['TinyChart-3B-768']:
from .tinychart import generate_response
elif model_name in ['ChartInstruct-LLama2',
'ChartInstruct-FlanT5-XL']:
from .chartinstruct import generate_response
elif model_name in ['unichart-chartqa-960']:
from .unichart import generate_response
elif model_name in ['ChartAssistant']:
from .chartast import generate_response
elif model_name in ['DocOwl1.5-Omni',
'DocOwl1.5-Chat',]:
from .docowl15 import generate_response
elif model_name in ['ureader-v1']:
from .ureader import generate_response
elif model_name in ['TextMonkey',
'Monkey-Chat',]:
from .textmonkey import generate_response
elif model_name in ['cogagent-vqa-hf']:
from .cogagent import generate_response
else:
raise ValueError(f"Model {model_name} not supported")
return generate_response
