import argparse import os import random import io from PIL import Image 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 import Chat, CONV_VISION from fastapi import FastAPI, HTTPException, File, UploadFile,Form from fastapi.responses import RedirectResponse from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel from PIL import Image import io import uvicorn # 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 * def parse_args(): parser = argparse.ArgumentParser(description="Demo") parser.add_argument("--cfg-path", type=str, default='eval_configs/minigpt4.yaml', help="path to configuration file.") 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 # ======================================== SHARED_UI_WARNING = f'''### [NOTE] It is possible that you are waiting in a lengthy queue. You can duplicate and use it with a paid private GPU. Duplicate Space Alternatively, you can also use the demo on our [project page](https://minigpt-4.github.io). ''' print('Initializing Chat') cfg = Config(parse_args()) model_config = cfg.model_cfg model_cls = registry.get_model_class(model_config.arch) model = model_cls.from_config(model_config).to('cuda:0') vis_processor_cfg = cfg.datasets_cfg.cc_align.vis_processor.train vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg) chat = Chat(model, vis_processor) print('Initialization Finished') # ======================================== # Gradio Setting # ======================================== app = FastAPI() app.add_middleware( CORSMiddleware, allow_origins=["*"], # Replace "*" with your frontend domain allow_credentials=True, allow_methods=["GET", "POST"], allow_headers=["*"], ) class Item(BaseModel): gr_img: UploadFile = File(..., description="Image file") text_input: str = None @app.get("/") async def root(): return RedirectResponse(url="/docs") @app.post("/process/") async def process_item( file: UploadFile = File(...), prompt: str = Form(...), ): chat_state = CONV_VISION.copy() img_list = [] chatbot=[] pil_image = Image.open(io.BytesIO(await file.read())) chat.upload_img(pil_image, chat_state, img_list) chat.ask(prompt, chat_state) chatbot = chatbot + [[prompt, None]] llm_message = chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature, max_length=2000)[0] chatbot[-1][1] = llm_message return chatbot, chat_state, img_list # if __name__ == "__main__": # # Run the FastAPI app with Uvicorn # uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True) # 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 image first', # interactive=False), gr.update( # value="Upload & Start Chat", interactive=True), chat_state, img_list # # # def upload_img(gr_img, text_input, chat_state): # if gr_img is None: # return None, None, gr.update(interactive=True), chat_state, None # chat_state = CONV_VISION.copy() # img_list = [] # llm_message = chat.upload_img(gr_img, chat_state, img_list) # return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update( # value="Start Chatting", interactive=False), chat_state, img_list # # # 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 # # # def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature): # llm_message = \ # chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature, # max_length=2000)[0] # chatbot[-1][1] = llm_message # return chatbot, chat_state, img_list # # # title = """

Demo of MiniGPT-4

""" # description = """

This is the demo of MiniGPT-4. Upload your images and start chatting!

""" # article = """
# """ # # # TODO show examples below # # with gr.Blocks() as demo: # gr.Markdown(title) # gr.Markdown(SHARED_UI_WARNING) # gr.Markdown(description) # gr.Markdown(article) # # with gr.Row(): # with gr.Column(scale=0.5): # image = gr.Image(type="pil") # 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='MiniGPT-4') # text_input = gr.Textbox(label='User', placeholder='Please upload your image first', interactive=False) # # upload_button.click(upload_img, [image, text_input, chat_state], # [image, 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, image, text_input, upload_button, chat_state, img_list], # queue=False) # # demo.launch(enable_queue=True)