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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.
<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/Vision-CAIR/minigpt4?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-xl-dark.svg" alt="Duplicate Space"></a>
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 = """<h1 align="center">Demo of MiniGPT-4</h1>"""
# description = """<h3>This is the demo of MiniGPT-4. Upload your images and start chatting!</h3>"""
# article = """<div style='display:flex; gap: 0.25rem; '><a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a><a href='https://github.com/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/Github-Code-blue'></a><a href='https://github.com/TsuTikgiau/blip2-llm/blob/release_prepare/MiniGPT_4.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></div>
# """
#
# # 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)
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