app.py

import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
from PIL import Image
import subprocess
from datetime import datetime
import numpy as np
import os


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

# models = {
#     "Qwen/Qwen2-VL-7B-Instruct": AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", trust_remote_code=True, torch_dtype="auto", _attn_implementation="flash_attention_2").cuda().eval()


# }
def array_to_image_path(image_array):
    if image_array is None:
        raise ValueError("No image provided. Please upload an image before submitting.")
    # Convert numpy array to PIL Image
    img = Image.fromarray(np.uint8(image_array))

    # Generate a unique filename using timestamp
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"image_{timestamp}.png"

    # Save the image
    img.save(filename)

    # Get the full path of the saved image
    full_path = os.path.abspath(filename)

    return full_path


models = {
    "Qwen/Qwen2-VL-7B-Instruct": Qwen2VLForConditionalGeneration.from_pretrained(
        "Qwen/Qwen2-VL-7B-Instruct", trust_remote_code=True, torch_dtype="auto"
    )
    .cuda()
    .eval()
}

processors = {
    "Qwen/Qwen2-VL-7B-Instruct": AutoProcessor.from_pretrained(
        "Qwen/Qwen2-VL-7B-Instruct", trust_remote_code=True
    )
}

DESCRIPTION = "[Qwen2-VL-7B Demo](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)"

kwargs = {}
kwargs["torch_dtype"] = torch.bfloat16

user_prompt = "<|user|>\n"
assistant_prompt = "<|assistant|>\n"
prompt_suffix = "<|end|>\n"


@spaces.GPU
def run_example(image, text_input=None, model_id="Qwen/Qwen2-VL-7B-Instruct"):
    image_path = array_to_image_path(image)

    print(image_path)
    model = models[model_id]
    processor = processors[model_id]

    prompt = f"{user_prompt}<|image_1|>\n{text_input}{prompt_suffix}{assistant_prompt}"
    image = Image.fromarray(image).convert("RGB")
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image_path,
                },
                {"type": "text", "text": text_input},
            ],
        }
    ]

    # Preparation for inference
    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to("cuda")

    # Inference: Generation of the output
    generated_ids = model.generate(**inputs, max_new_tokens=1024)
    generated_ids_trimmed = [
        out_ids[len(in_ids) :]
        for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=False,
    )

    return output_text[0]


css = """
  #output {
    height: 500px;
    overflow: auto;
    border: 1px solid #ccc;
  }
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Tab(label="Qwen2-VL-7B Input"):
        with gr.Row():
            with gr.Column():
                input_img = gr.Image(label="Input Picture")
                model_selector = gr.Dropdown(
                    choices=list(models.keys()),
                    label="Model",
                    value="Qwen/Qwen2-VL-7B-Instruct",
                )
                text_input = gr.Textbox(label="Question")
                submit_btn = gr.Button(value="Submit")
            with gr.Column():
                output_text = gr.Textbox(label="Output Text")

        submit_btn.click(
            run_example, [input_img, text_input, model_selector], [output_text]
        )

demo.queue(api_open=False)
demo.launch(debug=True)