app_huggingface.py

from io import BytesIO
import string
import gradio as gr
import requests
from caption_anything import CaptionAnything
import torch
import json
import sys
import argparse
from caption_anything import parse_augment
import numpy as np
import PIL.ImageDraw as ImageDraw
from image_editing_utils import create_bubble_frame
import copy
from tools import mask_painter
from PIL import Image
import os

def download_checkpoint(url, folder, filename):
    os.makedirs(folder, exist_ok=True)
    filepath = os.path.join(folder, filename)

    if not os.path.exists(filepath):
        response = requests.get(url, stream=True)
        with open(filepath, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                if chunk:
                    f.write(chunk)

    return filepath
checkpoint_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
folder = "segmenter"
filename = "sam_vit_h_4b8939.pth"

download_checkpoint(checkpoint_url, folder, filename)


title = """<h1 align="center">Caption-Anything</h1>"""
description = """Gradio demo for Caption Anything, image to dense captioning generation with various language styles. To use it, simply upload your image, or click one of the examples to load them. Code: https://github.com/ttengwang/Caption-Anything
"""

examples = [
    ["test_img/img2.jpg"],
    ["test_img/img5.jpg"],
    ["test_img/img12.jpg"],
    ["test_img/img14.jpg"],
]

args = parse_augment()
args.captioner = 'blip2'
args.seg_crop_mode = 'wo_bg'
args.regular_box = True
# args.device = 'cuda:5'
# args.disable_gpt = False
# args.enable_reduce_tokens = True
# args.port=20322
model = CaptionAnything(args)

def init_openai_api_key(api_key):
    os.environ['OPENAI_API_KEY'] = api_key
    model.init_refiner()


def get_prompt(chat_input, click_state):    
    points = click_state[0]
    labels = click_state[1]
    inputs = json.loads(chat_input)
    for input in inputs:
        points.append(input[:2])
        labels.append(input[2])
    
    prompt = {
        "prompt_type":["click"],
        "input_point":points,
        "input_label":labels,
        "multimask_output":"True",
    }
    return prompt

def chat_with_points(chat_input, click_state, state):
    if not hasattr(model, "text_refiner"):
        response = "Text refiner is not initilzed, please input openai api key."
        state = state + [(chat_input, response)]
        return state, state
    
    points, labels, captions = click_state
    # point_chat_prompt = "I want you act as a chat bot in terms of image. I will give you some points (w, h) in the image and tell you what happed on the point in natural language. Note that (0, 0) refers to the top-left corner of the image, w refers to the width and h refers the height. You should chat with me based on the fact in the image instead of imagination. Now I tell you the points with their visual description:\n{points_with_caps}\nNow begin chatting! Human: {chat_input}\nAI: "
    # # "The image is of width {width} and height {height}." 
    point_chat_prompt = "a) Revised prompt: I am an AI trained to chat with you about an image based on specific points (w, h) you provide, along with their visual descriptions. Please note that (0, 0) refers to the top-left corner of the image, w refers to the width, and h refers to the height. Here are the points and their descriptions you've given me: {points_with_caps}. Now, let's chat! Human: {chat_input} AI:"
    prev_visual_context = ""
    pos_points = [f"{points[i][0]}, {points[i][1]}" for i in range(len(points)) if labels[i] == 1]
    if len(captions):
        prev_visual_context = ', '.join(pos_points) + captions[-1] + '\n'
    else:
        prev_visual_context = 'no point exists.'
    chat_prompt = point_chat_prompt.format(**{"points_with_caps": prev_visual_context, "chat_input": chat_input})
    response = model.text_refiner.llm(chat_prompt)
    state = state + [(chat_input, response)]
    return state, state

def inference_seg_cap(image_input, point_prompt, language, sentiment, factuality, length, state, click_state, evt:gr.SelectData):

    if point_prompt == 'Positive':
        coordinate = "[[{}, {}, 1]]".format(str(evt.index[0]), str(evt.index[1]))
    else:
        coordinate = "[[{}, {}, 0]]".format(str(evt.index[0]), str(evt.index[1]))
        
    controls = {'length': length,
             'sentiment': sentiment,
             'factuality': factuality,
             'language': language}

    # click_coordinate = "[[{}, {}, 1]]".format(str(evt.index[0]), str(evt.index[1])) 
    # chat_input = click_coordinate
    prompt = get_prompt(coordinate, click_state)
    print('prompt: ', prompt, 'controls: ', controls)

    out = model.inference(image_input, prompt, controls)
    state = state + [(None, "Image point: {}, Input label: {}".format(prompt["input_point"], prompt["input_label"]))]
    # for k, v in out['generated_captions'].items():
    #     state = state + [(f'{k}: {v}', None)]
    state = state + [("caption: {}".format(out['generated_captions']['raw_caption']), None)]
    wiki = out['generated_captions'].get('wiki', "")
    click_state[2].append(out['generated_captions']['raw_caption'])
    
    text = out['generated_captions']['raw_caption']
    # draw = ImageDraw.Draw(image_input)
    # draw.text((evt.index[0], evt.index[1]), text, textcolor=(0,0,255), text_size=120)
    input_mask = np.array(Image.open(out['mask_save_path']).convert('P'))
    image_input = mask_painter(np.array(image_input), input_mask)
    origin_image_input = image_input
    image_input = create_bubble_frame(image_input, text, (evt.index[0], evt.index[1]))

    yield state, state, click_state, chat_input, image_input, wiki
    if not args.disable_gpt and hasattr(model, "text_refiner"):
        refined_caption = model.text_refiner.inference(query=text, controls=controls, context=out['context_captions'])
        # new_cap = 'Original: ' + text + '. Refined: ' + refined_caption['caption']
        new_cap = refined_caption['caption']
        refined_image_input = create_bubble_frame(origin_image_input, new_cap, (evt.index[0], evt.index[1]))
        yield state, state, click_state, chat_input, refined_image_input, wiki


def upload_callback(image_input, state):
    state = [] + [('Image size: ' + str(image_input.size), None)]
    click_state = [[], [], []]
    model.segmenter.image = None
    model.segmenter.image_embedding = None
    model.segmenter.set_image(image_input)
    return state, image_input, click_state

with gr.Blocks(
    css='''
    #image_upload{min-height:400px}
    #image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 600px}
    '''
) as iface:
    state = gr.State([])
    click_state = gr.State([[],[],[]])
    origin_image = gr.State(None)

    gr.Markdown(title)
    gr.Markdown(description)

    with gr.Row():
        with gr.Column(scale=1.0):
            image_input = gr.Image(type="pil", interactive=True, elem_id="image_upload")
            with gr.Row(scale=1.0):
                point_prompt = gr.Radio(
                    choices=["Positive",  "Negative"],
                    value="Positive",
                    label="Point Prompt",
                    interactive=True)
                clear_button_clike = gr.Button(value="Clear Clicks", interactive=True)
                clear_button_image = gr.Button(value="Clear Image", interactive=True)
            with gr.Row(scale=1.0):
                language = gr.Dropdown(['English', 'Chinese', 'French', "Spanish", "Arabic", "Portuguese", "Cantonese"], value="English", label="Language", interactive=True)
               
                sentiment = gr.Radio(
                    choices=["Positive", "Natural", "Negative"],
                    value="Natural",
                    label="Sentiment",
                    interactive=True,
                )
            with gr.Row(scale=1.0):
                factuality = gr.Radio(
                    choices=["Factual", "Imagination"],
                    value="Factual",
                    label="Factuality",
                    interactive=True,
                )
                length = gr.Slider(
                    minimum=10,
                    maximum=80,
                    value=10,
                    step=1,
                    interactive=True,
                    label="Length",
                )
        
        with gr.Column(scale=0.5):
            openai_api_key = gr.Textbox(
                placeholder="Input your openAI API key and press Enter",
                show_label=False,
                label = "OpenAI API Key",
                lines=1,
                type="password"
                )
            openai_api_key.submit(init_openai_api_key, inputs=[openai_api_key])
            wiki_output = gr.Textbox(lines=6, label="Wiki")
            chatbot = gr.Chatbot(label="Chat about Selected Object",).style(height=450,scale=0.5)
            chat_input = gr.Textbox(lines=1, label="Chat Input")
            with gr.Row():
                clear_button_text = gr.Button(value="Clear Text", interactive=True)
                submit_button_text = gr.Button(value="Submit", interactive=True, variant="primary")
    clear_button_clike.click(
        lambda x: ([[], [], []], x, ""),
        [origin_image],
        [click_state, image_input, wiki_output],
        queue=False,
        show_progress=False
    )
    clear_button_image.click(
        lambda: (None, [], [], [[], [], []], ""),
        [],
        [image_input, chatbot, state, click_state, wiki_output],
        queue=False,
        show_progress=False
    )
    clear_button_text.click(
        lambda: ([], [], [[], [], []]),
        [],
        [chatbot, state, click_state],
        queue=False,
        show_progress=False
    )
    image_input.clear(
        lambda: (None, [], [], [[], [], []], ""),
        [],
        [image_input, chatbot, state, click_state, wiki_output],
        queue=False,
        show_progress=False
    )

    examples = gr.Examples(
        examples=examples,
        inputs=[image_input],
    )

    image_input.upload(upload_callback,[image_input, state], [state, origin_image, click_state])
    chat_input.submit(chat_with_points, [chat_input, click_state, state], [chatbot, state])

    # select coordinate
    image_input.select(inference_seg_cap, 
        inputs=[
        origin_image,
        point_prompt,
        language,
        sentiment,
        factuality,
        length,
        state,
        click_state
        ],
        outputs=[chatbot, state, click_state, chat_input, image_input, wiki_output],
        show_progress=False, queue=True)
    
iface.queue(concurrency_count=1, api_open=False, max_size=10)
iface.launch(server_name="0.0.0.0", enable_queue=True)