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| import gradio as gr | |
| import argparse | |
| import os | |
| import random | |
| import numpy as np | |
| import torch | |
| import torch.backends.cudnn as cudnn | |
| 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 esm | |
| # ProteinGPT Initialization Function | |
| def initialize_chat(args): | |
| cfg = Config(args) | |
| model_config = cfg.model_cfg | |
| model_config.device_8bit = 0 | |
| model_cls = registry.get_model_class(model_config.arch) | |
| model = model_cls.from_config(model_config).to('cpu') | |
| 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='cpu') | |
| return chat | |
| # Gradio Reset Function | |
| 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 None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your protein structure and sequence first', interactive=False), gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list | |
| # Upload Function | |
| def upload_protein(structure, sequence, text_input, chat_state): | |
| # Check if structure and sequence files are valid | |
| if structure is None or not structure.endswith(".pt"): | |
| return (None, None, None, gr.update(placeholder="Invalid structure file, must be a .pt file.", interactive=True), chat_state, None) | |
| if sequence is None or not sequence.endswith(".pt"): | |
| return (None, None, None, gr.update(placeholder="Invalid sequence file, must be a .pt file.", interactive=True), chat_state, None) | |
| # Load protein structure and sequence | |
| pdb_embedding = torch.load(structure, map_location=torch.device('cpu')) | |
| sample_pdb = pdb_embedding.to('cpu') | |
| seq_embedding = torch.load(sequence, map_location=torch.device('cpu')) | |
| sample_seq = seq_embedding.to('cpu') | |
| # Initialize the conversation state | |
| chat_state = CONV_VISION.copy() | |
| img_list = [] | |
| # Upload protein data | |
| llm_message = chat.upload_protein(sample_pdb, sample_seq, chat_state, img_list) | |
| # Return the required outputs | |
| return (gr.update(interactive=False), # Disable structure file input | |
| gr.update(interactive=False), # Disable sequence file input | |
| gr.update(interactive=True, placeholder='Type and press Enter'), # Enable the text input box | |
| gr.update(value="Start Chatting", interactive=False), # Update upload button state | |
| chat_state, # Return the conversation state | |
| img_list) # Return the list of images (if any) | |
| # Ask Function | |
| def gradio_ask(user_message, chatbot, chat_state): | |
| if len(user_message) == 0: | |
| return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state | |
| chat.ask(user_message, chat_state) | |
| chatbot = chatbot + [[user_message, None]] | |
| return '', chatbot, chat_state | |
| # Answer Function | |
| def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature): | |
| img_list = [mat.half() for mat in img_list] | |
| llm_message = chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=num_beams, temperature=temperature, max_length=2000)[0] | |
| chatbot[-1][1] = llm_message | |
| return chatbot, chat_state, img_list | |
| # Command-line Argument Parsing | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description="Demo") | |
| parser.add_argument("--cfg-path", help="path to configuration file.", default='configs/evaluation.yaml') | |
| 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 | |
| # Demo Gradio Interface | |
| title = """<h1 align="center">Demo of ProteinGPT</h1>""" | |
| description = """<h3>Upload your protein sequence and structure and start chatting with your protein!</h3>""" | |
| article = """<div style='display:flex; gap: 0.25rem; '><a href='https://huggingface.co/AI-BIO/ProteinGPT-Llama3'><img src='https://img.shields.io/badge/Project-Page-Green'></a><a href='https://github.com'><img src='https://img.shields.io/badge/Github-Code-blue'></a></div>""" | |
| args = parse_args() # Parse arguments to get config and model info | |
| chat = initialize_chat(args) # Initialize ProteinGPT model | |
| with gr.Blocks() as demo: | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| gr.Markdown(article) | |
| with gr.Row(): | |
| with gr.Column(scale=0.5): | |
| structure = gr.File(type="filepath", label="Upload Protein Structure", show_label=True) | |
| sequence = gr.File(type="filepath", label="Upload Protein Sequence", show_label=True) | |
| upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary") | |
| clear = gr.Button("Restart") | |
| num_beams = gr.Slider(minimum=1, maximum=5, value=1, step=1, interactive=True, label="Beam search numbers") | |
| temperature = gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, interactive=True, label="Temperature") | |
| with gr.Column(): | |
| chat_state = gr.State() | |
| img_list = gr.State() | |
| chatbot = gr.Chatbot(label='ProteinGPT') | |
| text_input = gr.Textbox(label='User', placeholder='Please upload your image first', interactive=False) | |
| upload_button.click(upload_protein, | |
| [structure, sequence, text_input, chat_state], | |
| [structure, sequence, text_input, upload_button, chat_state, img_list]) | |
| text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list]) | |
| clear.click(gradio_reset, [chat_state, img_list], [chatbot, structure, sequence, text_input, upload_button, chat_state, img_list], queue=False) | |
| demo.launch(share=True) | |