app.py

import gradio as gr
from transformers import AutoModel, AutoTokenizer, AutoImageProcessor
import torch
import torchvision.transforms as T
from PIL import Image

from torchvision.transforms.functional import InterpolationMode
# Define the path to your model
import os
from huggingface_hub import login
hf_token = os.environ.get('hf_token', None)

# path = 'h2oai/h2o-mississippi-2b'
path = "h2oai/h2o-mississippi1-2b-preview"

# image preprocesing
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform


def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float('inf')
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images, target_aspect_ratio


def dynamic_preprocess2(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False, prior_aspect_ratio=None):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    new_target_ratios = []
    if prior_aspect_ratio is not None:
        for i in target_ratios:
            if prior_aspect_ratio[0]%i[0] != 0 and prior_aspect_ratio[1]%i[1] != 0:
                new_target_ratios.append(i)
            else:
                continue

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, new_target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images
def load_image1(image_file, input_size=448, min_num=1, max_num=12):
    if isinstance(image_file, str):
        image = Image.open(image_file).convert('RGB')
    else:
        image = image_file
    transform = build_transform(input_size=input_size)
    images, target_aspect_ratio = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, min_num=min_num, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values, target_aspect_ratio

def load_image2(image_file, input_size=448, min_num=1, max_num=12, target_aspect_ratio=None):
    
    if isinstance(image_file, str):
        image = Image.open(image_file).convert('RGB')
    else:
        image = image_file
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess2(image, image_size=input_size, use_thumbnail=True, min_num=min_num, max_num=max_num, prior_aspect_ratio=target_aspect_ratio)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values

def load_image_msac(file_name):
    pixel_values, target_aspect_ratio = load_image1(file_name, min_num=1, max_num=6)
    pixel_values = pixel_values.to(torch.bfloat16).cuda()
    pixel_values2 = load_image2(file_name, min_num=3, max_num=6, target_aspect_ratio=target_aspect_ratio)
    pixel_values2 = pixel_values2.to(torch.bfloat16).cuda()
    pixel_values = torch.cat([pixel_values2[:-1], pixel_values[:-1], pixel_values2[-1:]], 0)
    return pixel_values
# Load the model and tokenizer
model = AutoModel.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True,
    use_auth_token=hf_token
).eval().cuda()

tokenizer = AutoTokenizer.from_pretrained(
    path, 
    trust_remote_code=True, 
    use_fast=False,
    use_auth_token=hf_token
)
tokenizer.pad_token = tokenizer.unk_token
tokenizer.eos_token = "<|end|>"
model.generation_config.pad_token_id = tokenizer.pad_token_id


def inference(image, prompt):
    # Check if both image and prompt are provided
    if image is None or prompt.strip() == "":
        return "Please provide both an image and a prompt."
    
    # Process the image and get pixel_values
    pixel_values = load_image_msac(image)

    # Set generation config
    generation_config = dict(
        num_beams=1,
        max_new_tokens=2048,
        do_sample=False,
    )

    # Generate the response
    response = model.chat(
        tokenizer, 
        pixel_values, 
        prompt, 
        generation_config
    )

    return response

# Build the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("H2O-Mississippi")

    with gr.Row():
        image_input = gr.Image(type="pil", label="Upload an Image")
        prompt_input = gr.Textbox(label="Enter your prompt here")

    response_output = gr.Textbox(label="Model Response")

    with gr.Row():
        submit_button = gr.Button("Submit")
        clear_button = gr.Button("Clear")

    # When the submit button is clicked, call the inference function
    submit_button.click(
        fn=inference, 
        inputs=[image_input, prompt_input], 
        outputs=response_output
    )

    # Define the clear button action
    def clear_all():
        return None, "", ""

    clear_button.click(
        fn=clear_all, 
        inputs=None, 
        outputs=[image_input, prompt_input, response_output]
    )

demo.launch()