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minigpt_v1
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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 typing import List
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


chat_state = CONV_VISION.copy()
img_list = []
chatbot = []


@app.get("/")
async def root():
    return RedirectResponse(url="/docs")


@app.post("/upload_img/")
async def upload_img(

        file: UploadFile = File(...),

):
    pil_image = Image.open(io.BytesIO(await file.read()))
    chat.upload_img(pil_image, chat_state, img_list)
    return {"message": "image uploaded  successfully."}



@app.post("/process/")
async def process_item(prompts: List[str] = Form(...)):
    if not img_list:  # Check if img_list is empty or None
        raise HTTPException(status_code=400, detail="No images uploaded.")

    global chatbot
    responses = []

    for prompt in prompts:
        # Process each prompt individually
        chat.ask(prompt, chat_state)
        chatbot.append([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
        responses.append({
            "prompt": prompt,
            "response": llm_message
        })

    return responses


@app.post("/reset/")
async def reset(

):
    global chat_state, img_list, chatbot  # Use global keyword to reassign
    img_list = []
    if chat_state is not None:
        chat_state.messages = []
    if img_list is not None:
        img_list = []
    if chatbot is not None:
        chatbot = []


if __name__ == "__main__":
    # Run the FastAPI app with Uvicorn
    uvicorn.run("main:app", host="0.0.0.0", port=7860)