app.py

import os
# 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 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

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 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 = []
    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


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='Compositional-VLM')
            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)
# 
# 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()