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import argparse
import os
import random
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
import esm
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation_esm import Chat, CONV_VISION
import json
# Imports PIL module
from PIL import Image
# imports modules for registration
from minigpt4.datasets.builders import *
from minigpt4.models import *
from minigpt4.processors import *
from minigpt4.runners import *
from minigpt4.tasks import *
import esm
import esm.inverse_folding
def parse_args():
parser = argparse.ArgumentParser(description="Demo")
parser.add_argument("--cfg-path", required=True, help="path to configuration file.")
parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.")
# parser.add_argument("--json-path", default='/home/h5guo/shared/Mini-GPT4/coco_json/cocoval2014_img_prompt.json', help="path to the classification json file")
# parser.add_argument("--caption-save-path", default='/home/h5guo/shared/Mini-GPT4/coco_json_result/results.json', help="path to saved generated captions")
parser.add_argument(
"--options",
nargs="+",
help="override some settings in the used config, the key-value pair "
"in xxx=yyy format will be merged into config file (deprecate), "
"change to --cfg-options instead.",
)
args = parser.parse_args()
return args
def setup_seeds(config):
seed = config.run_cfg.seed + get_rank()
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
cudnn.benchmark = False
cudnn.deterministic = True
# ========================================
# Model Initialization
# ========================================
print('Initializing Chat')
args = parse_args()
cfg = Config(args)
model_config = cfg.model_cfg
model_config.device_8bit = args.gpu_id
model_cls = registry.get_model_class(model_config.arch)
model = model_cls.from_config(model_config).to('cuda:{}'.format(args.gpu_id))
vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train
vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id))
print('Initialization Finished')
# ========================================
# Gradio Setting
# ========================================
def gradio_reset(chat_state, img_list):
if chat_state is not None:
chat_state.messages = []
if img_list is not None:
img_list = []
return chat_state, img_list
def upload_protein(gr_img):
chat_state = CONV_VISION.copy()
img_list = []
llm_message = chat.upload_protein(gr_img, chat_state, img_list)
return chat_state, img_list
def gradio_ask(user_message, chat_state):
chat.ask(user_message, chat_state)
return chat_state
def gradio_answer(chat_state, img_list, num_beams=1, temperature=1e-3):
llm_message = chat.answer(conv=chat_state,
img_list=img_list,
num_beams=num_beams,
temperature=temperature,
max_new_tokens=300,
max_length=2000)[0]
return llm_message, chat_state, img_list
if __name__ == "__main__":
start = time.time()
print("******************")
protein_embedding_path = "/home/h5guo/data/esm_subset/pt/2wge.pt"
protein_embedding = torch.load(protein_embedding_path, map_location=torch.device('cpu'))
sample_protein = protein_embedding.to('cuda:{}'.format(args.gpu_id))
user_message = "Describe this protein in a short paragraph."
chat_state, img_list = upload_protein(sample_protein)
chat_state = gradio_ask(user_message, chat_state)
llm_message, chat_state, img_list = gradio_answer(chat_state, img_list)
print(f"llm_message: {llm_message}")
end = time.time()
print(end - start)
# i += 1
print("******************")
# f.close()
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