app.py

import os
os.system("cd open_flamingo && pip install .")
os.system("cd transformers && pip install .")
import numpy as np
import torch
from PIL import Image

from open_flamingo.train.distributed import init_distributed_device, world_info_from_env
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
flamingo, image_processor, tokenizer, vis_embed_size = create_model_and_transforms(
        "ViT-L-14",
        "datacomp_xl_s13b_b90k",
        "facebook/opt-350m",
        "facebook/opt-350m",
        add_visual_grounding=True,
        location_token_num=1000,
        add_visual_token = True,
        use_format_v2 = True,
    )

checkpoint_path = hf_hub_download("chendl/mm", "checkpoint_opt350m_v2.pt")
checkpoint = torch.load(checkpoint_path, map_location="cpu")
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)
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.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,
        )

    outputs = outputs[:, len(input_ids[0]) :]
    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]
    endofchunk_token_id = tokenizer("<|endofchunk|>", 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]
    all_ids = set(range(flamingo.lang_encoder.lm_head.out_features))
    bad_words_ids = list(all_ids - set(loc_token_ids))
    bad_words_ids = [[b] for b in bad_words_ids]
    loc_word_ids = list(set(loc_token_ids))
    loc_word_ids = [[b] for b in loc_word_ids]

    min_loc_token_id = min(loc_token_ids)
    max_loc_token_id = max(loc_token_ids)
    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"<|#image#|>{tokenizer.pad_token*vis_embed_size}<|#endofimage#|><|#obj#|>{text.rstrip('.')}<|#loc#|>"]
        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 = 100
    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,
    )
    box = []
    out_image = image_ori
    for o in outputs[0]:
        if o >= min_loc_token_id and o <= max_loc_token_id:
            box.append(o.item() - min_loc_token_id)
            if len(box) == 4:
                break
    # else:
    #     tqdm.write(f"output: {tokenizer.batch_decode(outputs)}")
    #     tqdm.write(f"prompt: {prompt}")

    if len(box) == 4:
        img = cv2.cvtColor(np.array(image_ori), cv2.COLOR_RGB2BGR)
        out = cv2.rectangle(img, (int(box[0] * width / 1000), int(box[1] * height / 1000)),
                            (int(box[2] * width / 1000), int(box[3] * height / 1000)), color=(255, 0, 255), thickness=2)
        out = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
        out_image = Image.fromarray(out)
    # else:
    #     tqdm.write(f"output: {tokenizer.batch_decode(outputs)}")
    #     tqdm.write(f"prompt: {prompt}")

    gen_text = tokenizer.batch_decode(outputs)
    if idx == 1:
        return f"Output:{gen_text}", out_image
    elif idx == 2:
        return (f"Question: {text.strip()} Answer: {gen_text}")
    else:
        return (f"Output:{gen_text}")


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()