app.py

import os
import sys
from pathlib import Path
# os.system("cd transformers && pip install .")
os.system("cd multimodal && pip install .")
os.system("cd multimodal/YOLOX && pip install .")
import numpy as np
import torch
from PIL import Image
import tempfile


import string
import cv2


import gradio as gr
import torch
from PIL import Image
from huggingface_hub import hf_hub_download, login

from open_flamingo.src.factory import create_model_and_transforms
from open_flamingo.chat.conversation import Chat, CONV_VISION

sys.path.append(str(Path(__file__).parent.parent.parent))
TEMP_FILE_DIR = Path(__file__).parent / 'temp'
TEMP_FILE_DIR.mkdir(parents=True, exist_ok=True)

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://compositionalvlm.github.io/).

'''

flamingo, image_processor, tokenizer, vis_embed_size = create_model_and_transforms(
    "ViT-L-14",
    "datacomp_xl_s13b_b90k",
    "EleutherAI/pythia-1.4b",
    "EleutherAI/pythia-1.4b",
    location_token_num=1000,
    lora=False,
    lora_r=16,
    use_sam=None,
    add_visual_token=True,
    use_format_v2=True,
    add_box=True,
    add_pe=False,
    add_relation=False,
    enhance_data=False,
)


checkpoint_path = hf_hub_download("chendl/compositional_test", "pythiaS.pt")
checkpoint = torch.load(checkpoint_path, map_location="cpu")["model_state_dict"]
model_state_dict = {}
for key in checkpoint.keys():
    model_state_dict[key.replace("module.", "")] = checkpoint[key]
if "vision_encoder.logit_scale"in model_state_dict:
    # previous checkpoint has some unnecessary weights
    del model_state_dict["vision_encoder.logit_scale"]
    del model_state_dict["vision_encoder.visual.proj"]
    del model_state_dict["vision_encoder.visual.ln_post.weight"]
    del model_state_dict["vision_encoder.visual.ln_post.bias"]
flamingo.load_state_dict(model_state_dict, strict=True)
chat = Chat(flamingo, image_processor, tokenizer, vis_embed_size )

def get_outputs(

    model,

    batch_images,

    attention_mask,

    max_generation_length,

    min_generation_length,

    num_beams,

    length_penalty,

    input_ids,

    image_start_index_list=None,

    image_nums=None,

    bad_words_ids=None,

):
    #  and torch.cuda.amp.autocast(dtype=torch.float16)
    with torch.inference_mode():
        outputs = model(
            vision_x=batch_images,
            lang_x=input_ids,
            attention_mask=attention_mask,
            labels=None,
            image_nums=image_nums,
            image_start_index_list=image_start_index_list,
            added_bbox_list=None,
            add_box=False,
        )
        # outputs = model.generate(
        #     batch_images,
        #     input_ids,
        #     attention_mask=attention_mask,
        #     max_new_tokens=max_generation_length,
        #     min_length=min_generation_length,
        #     num_beams=num_beams,
        #     length_penalty=length_penalty,
        #     image_start_index_list=image_start_index_list,
        #     image_nums=image_nums,
        #     bad_words_ids=bad_words_ids,
        # )

    return outputs




def generate(

    idx,

    image,

    text,

    vis_embed_size=256,

    rank=0,

    world_size=1,

):
    if image is None:
        raise gr.Error("Please upload an image.")
    flamingo.eval()
    loc_token_ids = []
    for i in range(1000):
        loc_token_ids.append(int(tokenizer(f"<loc_{i}>", add_special_tokens=False)["input_ids"][-1]))
    media_token_id = tokenizer("<|#image#|>", add_special_tokens=False)["input_ids"][-1]
    endofmedia_token_id = tokenizer("<|#endofimage#|>", add_special_tokens=False)["input_ids"][-1]
    pad_token_id = tokenizer(tokenizer.pad_token, add_special_tokens=False)["input_ids"][-1]
    bos_token_id = tokenizer(tokenizer.bos_token, add_special_tokens=False)["input_ids"][-1]
    prebox_token_id = tokenizer("<|#prebox#|>", add_special_tokens=False)["input_ids"][-1]

    image_ori = image
    image = image.convert("RGB")
    width = image.width
    height = image.height
    image = image.resize((224, 224))
    batch_images = image_processor(image).unsqueeze(0).unsqueeze(1).unsqueeze(0)
    if idx == 1:
        prompt = [f"{tokenizer.bos_token}<|#image#|>{tokenizer.pad_token * vis_embed_size}<|#endofimage#|><|#object#|> {text.rstrip('.').strip()}<|#endofobject#|><|#visual#|>"]
        bad_words_ids = None
        max_generation_length = 5
    else:
        prompt = [f"<|#image#|>{tokenizer.pad_token * vis_embed_size}<|#endofimage#|>{text.rstrip('.')}"]
        bad_words_ids = loc_word_ids
        max_generation_length = 30
    encodings = tokenizer(
        prompt,
        padding="longest",
        truncation=True,
        return_tensors="pt",
        max_length=2000,
    )
    input_ids = encodings["input_ids"]
    attention_mask = encodings["attention_mask"]
    image_start_index_list = ((input_ids == media_token_id).nonzero(as_tuple=True)[-1] + 1).tolist()
    image_start_index_list = [[x] for x in image_start_index_list]
    image_nums = [1] * len(input_ids)
    outputs = get_outputs(
        model=flamingo,
        batch_images=batch_images,
        attention_mask=attention_mask,
        max_generation_length=max_generation_length,
        min_generation_length=4,
        num_beams=1,
        length_penalty=1.0,
        input_ids=input_ids,
        bad_words_ids=bad_words_ids,
        image_start_index_list=image_start_index_list,
        image_nums=image_nums,
    )

    boxes = outputs["boxes"]
    scores = outputs["scores"]
    if len(scores) > 0:
        box = boxes[scores.argmax()]/224
    print(f"{box}")

    if idx == 1:
        open_cv_image = np.array(image_ori)
        # Convert RGB to BGR
        open_cv_image = open_cv_image[:, :, ::-1].copy()
        box = box*[width,height,width,height]
        # for box in boxes:
        open_cv_image = cv2.rectangle(open_cv_image, box[:2].astype(int), box[2:].astype(int), (255, 0, 0), 2)
        out_image = Image.fromarray(cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2RGB))
        return f"Output:{box}", out_image
    elif idx == 2:
        gen_text = tokenizer.batch_decode(outputs)
        return (f"Question: {text.strip()} Answer: {gen_text}")
    else:
        gen_text = tokenizer.batch_decode(outputs)
        return (f"Output:{gen_text}")


title = """<h1 align="center">Demo of Compositional-VLM</h1>"""
description = """<h3>This is the demo of Compositional-VLM. Upload your images and start chatting!</h3>"""
article = """<div style='display:flex; gap: 0.25rem; '><a href='https://compositionalvlm.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

# ========================================
#             Gradio Setting
# ========================================

def gradio_reset(chat_state, img_list):
    if chat_state is not None:
        chat_state = []
    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 build_image(image):
    if image is None:
        return  None
    # res = draw_bounding_boxes(image=image, boxes=boxes_to_draw, colors=color_to_draw, width=8)
    from torchvision.transforms import ToPILImage
    # res = ToPILImage()(res)
    _, path = tempfile.mkstemp(suffix='.jpg', dir=TEMP_FILE_DIR)
    image.save(path)

    return  path

def upload_img(gr_img, text_input, chat_state,chatbot):
    if gr_img is None:
        return None, None, gr.update(interactive=True), chat_state, None
    chat_state = []
    img_list = []
    path = build_image(gr_img)
    chatbot = chatbot + [[(path,), None]]
    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,chatbot


def gradio_ask(user_message, chatbot, chat_state,radio):
    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,radio)
    chatbot = chatbot + [[user_message, None]]
    return '', chatbot, chat_state


def gradio_answer(chatbot, chat_state, img_list,  radio, text,num_beams, temperature):
    llm_message,image = \
    chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature,
                max_length=2000,radio = radio,text_input = text)
    
    chatbot[-1][1] = llm_message
    if image==None:
        return chatbot, chat_state, img_list
    else:
        path = build_image(image)
        chatbot = chatbot + [[None,(path,)]]
        return chatbot, chat_state, img_list

task_template = {
                "Cap": "Summarize the content of the photo <image>.",
                "VQA": "For this image <image>, I want a simple and direct answer to my question: <question>",
                "REC": "Can you point out <expr> in the image <image> and provide the coordinates of its location?",
                "GC": "Can you give me a description of the region <boxes> in image <image>?",
                "Advanced": "<question>",
            }

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")
            radio = gr.Radio(
                ["Cap", "VQA", "REC", "Advanced"], label="Task Template", value='Cap',
            )

            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='Compositional-VLM')


            # template = gr.Textbox(label='Template', show_label=True, lines=1, interactive=False,
            #                       value='Provide a comprehensive description of the image <image> and specify the positions of any mentioned objects in square brackets.')
            # text_input = gr.Textbox(label='<question>', show_label=True, placeholder="Please upload your image first， then input...", lines=3,
            #                         value=None, visible=False, interactive=False)

            text_input = gr.Textbox(label='User', placeholder='Please upload your image first， then input...', interactive=False)

    upload_button.click(upload_img, [image, text_input, chat_state,chatbot],
                        [image, text_input, upload_button, chat_state, img_list,chatbot])

    text_input.submit(gradio_ask, [text_input, chatbot, chat_state,radio], [text_input, chatbot, chat_state]).then(
        gradio_answer, [chatbot, chat_state, img_list,  radio, text_input,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)
# 
# with gr.Blocks() as demo:
#     gr.Markdown(
#         """
#     🍜 Object Centric Pretraining Demo  
#     In this demo we showcase the in-context learning and grounding capabilities of the Object-Centric Pretrained model, a large multimodal model. Note that we add two additional demonstrations to the ones presented to improve the demo experience.
#     The model is trained on an interleaved mixture of text, images and bounding box and is able to generate text conditioned on sequences of images/text.
#     """
#     )
# 
#     with gr.Accordion("See terms and conditions"):
#         gr.Markdown(
#             """**Please read the following information carefully before proceeding.**This demo does NOT store any personal information on its users, and it does NOT store user queries.""")
# 
#     with gr.Tab("📷 Image Captioning"):
#         with gr.Row():
# 
# 
#             query_image = gr.Image(type="pil")
#         with gr.Row():
#             chat_input = gr.Textbox(lines=1, label="Chat Input")
#         text_output = gr.Textbox(value="Output:", label="Model output")
# 
#         run_btn = gr.Button("Run model")
# 
# 
# 
#         def on_click_fn(img,text): return generate(0, img, text)
# 
#         run_btn.click(on_click_fn, inputs=[query_image,chat_input], outputs=[text_output])
# 
#     with gr.Tab("🦓 Grounding"):
#         with gr.Row():
#             with gr.Column(scale=1):
#                 query_image = gr.Image(type="pil")
#             with gr.Column(scale=1):
#                 out_image = gr.Image(type="pil")
#         with gr.Row():
#             chat_input = gr.Textbox(lines=1, label="Chat Input")
#         text_output = gr.Textbox(value="Output:", label="Model output")
# 
#         run_btn = gr.Button("Run model")
# 
# 
#         def on_click_fn(img, text): return generate(1, img, text)
# 
# 
#         run_btn.click(on_click_fn, inputs=[query_image, chat_input], outputs=[text_output, out_image])
# 
#     with gr.Tab("🔢 Counting objects"):
#         with gr.Row():
#             query_image = gr.Image(type="pil")
#         with gr.Row():
#             chat_input = gr.Textbox(lines=1, label="Chat Input")
#         text_output = gr.Textbox(value="Output:", label="Model output")
# 
#         run_btn = gr.Button("Run model")
# 
# 
#         def on_click_fn(img,text): return generate(0, img, text)
# 
# 
#         run_btn.click(on_click_fn, inputs=[query_image, chat_input], outputs=[text_output])
# 
#     with gr.Tab("🕵️ Visual Question Answering"):
#         with gr.Row():
#             query_image = gr.Image(type="pil")
#         with gr.Row():
#             question = gr.Textbox(lines=1, label="Question")
#         text_output = gr.Textbox(value="Output:", label="Model output")
# 
#         run_btn = gr.Button("Run model")
# 
# 
#         def on_click_fn(img, txt): return generate(2, img, txt)
# 
# 
#         run_btn.click(
#             on_click_fn, inputs=[query_image, question], outputs=[text_output]
#         )
# 
#     with gr.Tab("🌎 Custom"):
#         gr.Markdown(
#             """### Customize the demonstration by uploading your own images and text samples. 
#                     ### **Note: Any text prompt you use will be prepended with an 'Output:', so you don't need to include it in your prompt.**"""
#         )
#         with gr.Row():
#             query_image = gr.Image(type="pil")
#         with gr.Row():
#             question = gr.Textbox(lines=1, label="Question")
#         text_output = gr.Textbox(value="Output:", label="Model output")
# 
#         run_btn = gr.Button("Run model")
# 
# 
#         def on_click_fn(img, txt): return generate(2, img, txt)
# 
# 
#         run_btn.click(
#             on_click_fn, inputs=[query_image, question], outputs=[text_output]
#         )
# 
# demo.queue(concurrency_count=1)
# demo.launch()