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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_eval.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=1,
max_length=2000)[0]
chatbot[-1][1] = llm_message
return chatbot
if __name__ == "__main__":
# Run the FastAPI app with Uvicorn
uvicorn.run("main:app", host="0.0.0.0", port=7860)
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