app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, StoppingCriteria
import gradio as gr
import spaces
import torch
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
from PIL import Image, ExifTags
import cv2
import numpy as np
import torch
from html2image import Html2Image
import tempfile
import os
import uuid
from scipy.ndimage import gaussian_filter
from threading import Thread
import re
import time 
from PIL import Image
import torch
import spaces
import subprocess
import os
from moviepy.editor import VideoFileClip, AudioFileClip

subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

torch.set_default_device('cuda')


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=12, 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 correct_image_orientation(image_path):
    # Mở ảnh
    image = Image.open(image_path)

    # Kiểm tra dữ liệu Exif (nếu có)
    try:
        exif = image._getexif()
        if exif is not None:
            for tag, value in exif.items():
                if ExifTags.TAGS.get(tag) == "Orientation":
                    # Sửa hướng dựa trên Orientation
                    if value == 3:
                        image = image.rotate(180, expand=True)
                    elif value == 6:
                        image = image.rotate(-90, expand=True)
                    elif value == 8:
                        image = image.rotate(90, expand=True)
                    break
    except Exception as e:
        print("Không thể xử lý Exif:", e)

    return image

def load_image(image_file, input_size=448, max_num=12, target_aspect_ratio=False):
    image = correct_image_orientation(image_file).convert('RGB')
    transform = build_transform(input_size=input_size)
    images, target_aspect_ratio = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    if target_aspect_ratio:
        return pixel_values, target_aspect_ratio
    else:
        return pixel_values
        
model = AutoModel.from_pretrained(
    "khang119966/Vintern-1B-v3_5-explainableAI",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True,
).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained("khang119966/Vintern-1B-v3_5-explainableAI", trust_remote_code=True, use_fast=False)
    
@spaces.GPU
def generate_video(image, prompt, max_tokens):
    print(image)
    pixel_values, target_aspect_ratio = load_image(image, max_num=6).to(torch.bfloat16).cuda()
    generation_config = dict(max_new_tokens= int(max_tokens), do_sample=False, num_beams = 3, repetition_penalty=2.5)
    response, query = model.chat(tokenizer, pixel_values, '<image>\n'+prompt, generation_config, return_history=False, \
                            attention_visualize=True,last_visualize_layers=7,raw_image_path=test_image,target_aspect_ratio=target_aspect_ratio)
    print(response)
    return "path_to_generated_video.mp4"

with gr.Blocks() as demo:
    gr.Markdown("### Simple VLM Demo")
    
    with gr.Row():
        with gr.Column():
            image = gr.Image(label="Upload your image")
            prompt = gr.Textbox(label="Describe your prompt", value="List all the text." )
            max_tokens = gr.Slider(label="Max token output (⚠️ Choose <100 for faster response)", minimum=1, maximum=512, value=50)
            btn = gr.Button("Attenion Video")
        video = gr.Video(label="Attenion Video")

    btn.click(fn=generate_video, inputs=[image, prompt, max_tokens], outputs=video)
    
if __name__ == "__main__":
    demo.launch()