app.py

import base64
import json
from datetime import datetime
import gradio as gr
import torch
import spaces
from PIL import Image, ImageDraw
from qwen_vl_utils import process_vision_info
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
import ast
import os
import numpy as np
from huggingface_hub import hf_hub_download, list_repo_files
import boto3
from botocore.exceptions import NoCredentialsError

# Define constants
DESCRIPTION = "[ShowUI Demo](https://huggingface.co/showlab/ShowUI-2B)"
_SYSTEM = "Based on the screenshot of the page, I give a text description and you give its corresponding location. The coordinate represents a clickable location [x, y] for an element, which is a relative coordinate on the screenshot, scaled from 0 to 1."
MIN_PIXELS = 256 * 28 * 28
MAX_PIXELS = 1344 * 28 * 28

# Specify the model repository and destination folder
model_repo = "showlab/ShowUI-2B"
destination_folder = "./showui-2b"

# Ensure the destination folder exists
os.makedirs(destination_folder, exist_ok=True)

# List all files in the repository
files = list_repo_files(repo_id=model_repo)

# Download each file to the destination folder
for file in files:
    file_path = hf_hub_download(repo_id=model_repo, filename=file, local_dir=destination_folder)
    print(f"Downloaded {file} to {file_path}")

model = Qwen2VLForConditionalGeneration.from_pretrained(
    destination_folder,
    torch_dtype=torch.bfloat16,
    device_map="cpu",
)

# Load the processor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=MIN_PIXELS, max_pixels=MAX_PIXELS)

# Helper functions
def draw_point(image_input, point=None, radius=5):
    """Draw a point on the image."""
    if isinstance(image_input, str):
        image = Image.open(image_input)
    else:
        image = Image.fromarray(np.uint8(image_input))

    if point:
        x, y = point[0] * image.width, point[1] * image.height
        ImageDraw.Draw(image).ellipse((x - radius, y - radius, x + radius, y + radius), fill='red')
    return image

def array_to_image_path(image_array, session_id):
    """Save the uploaded image and return its path."""
    if image_array is None:
        raise ValueError("No image provided. Please upload an image before submitting.")
    img = Image.fromarray(np.uint8(image_array))
    filename = f"{session_id}.png"
    img.save(filename)
    return os.path.abspath(filename)

def upload_to_s3(file_name, bucket, object_name=None):
    """Upload a file to an S3 bucket."""
    if object_name is None:
        object_name = file_name

    s3 = boto3.client('s3')

    try:
        s3.upload_file(file_name, bucket, object_name)
        return True
    except FileNotFoundError:
        return False
    except NoCredentialsError:
        return False

@spaces.GPU
def run_showui(image, query, session_id):
    """Main function for inference."""
    image_path = array_to_image_path(image, session_id)
    
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": _SYSTEM},
                {"type": "image", "image": image_path, "min_pixels": MIN_PIXELS, "max_pixels": MAX_PIXELS},
                {"type": "text", "text": query}
            ],
        }
    ]

    global model
    model = model.to("cuda")
    
    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")

    generated_ids = model.generate(**inputs, max_new_tokens=128)
    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
    )[0]

    click_xy = ast.literal_eval(output_text)
    result_image = draw_point(image_path, click_xy, radius=10)
    return result_image, str(click_xy), image_path

def save_and_upload_data(image_path, query, session_id, is_example_image, votes=None):
    """Save the data to a JSON file and upload to S3."""
    if is_example_image == "True":  # Updated to handle string values from Dropdown
        return

    votes = votes or {"upvotes": 0, "downvotes": 0}
    data = {
        "image_path": image_path,
        "query": query,
        "votes": votes,
        "timestamp": datetime.now().isoformat()
    }
    
    local_file_name = f"{session_id}.json"
    
    with open(local_file_name, "w") as f:
        json.dump(data, f)
    
    upload_to_s3(local_file_name, 'altair.storage', object_name=f"ootb/{local_file_name}")
    upload_to_s3(image_path, 'altair.storage', object_name=f"ootb/{os.path.basename(image_path)}")

    return data

# Examples with the `is_example` flag
examples = [
    ["./examples/app_store.png", "Download Kindle.", True],
    ["./examples/ios_setting.png", "Turn off Do not disturb.", True],
    ["./examples/apple_music.png", "Star to favorite.", True],
    ["./examples/map.png", "Boston.", True],
    ["./examples/wallet.png", "Scan a QR code.", True],
    ["./examples/word.png", "More shapes.", True],
    ["./examples/web_shopping.png", "Proceed to checkout.", True],
    ["./examples/web_forum.png", "Post my comment.", True],
    ["./examples/safari_google.png", "Click on search bar.", True],
]

def build_demo():
    with gr.Blocks() as demo:
        state_image_path = gr.State(value=None)
        state_session_id = gr.State(value=None)

        with gr.Row():
            with gr.Column(scale=3):
                imagebox = gr.Image(type="numpy", label="Input Screenshot")
                textbox = gr.Textbox(
                    show_label=True,
                    placeholder="Enter a query (e.g., 'Click Nahant')",
                    label="Query",
                )
                submit_btn = gr.Button(value="Submit", variant="primary")

                # Examples component
                gr.Examples(
                    examples=[[e[0], e[1]] for e in examples],
                    inputs=[imagebox, textbox],
                    outputs=[textbox],  # Only update the query textbox
                    examples_per_page=3,
                )

                # Add a hidden dropdown to pass the `is_example` flag
                is_example_dropdown = gr.Dropdown(
                    choices=["True", "False"],
                    value="False",
                    visible=False,
                    label="Is Example Image",
                )

                def set_is_example(query):
                    # Find the example and return its `is_example` flag
                    for _, example_query, is_example in examples:
                        if query.strip() == example_query.strip():
                            return str(is_example)  # Return as string for Dropdown compatibility
                    return "False"

                textbox.change(
                    set_is_example,
                    inputs=[textbox],
                    outputs=[is_example_dropdown],
                )

            with gr.Column(scale=8):
                output_img = gr.Image(type="pil", label="Output Image")
                output_coords = gr.Textbox(label="Clickable Coordinates")

                with gr.Row(equal_height=True):
                    clear_btn = gr.Button(value="🗑️ Clear", interactive=True)

            # Submit button logic
            submit_btn.click(
                lambda image, query, is_example: on_submit(image, query, is_example),
                inputs=[imagebox, textbox, is_example_dropdown],
                outputs=[output_img, output_coords, state_image_path, state_session_id],
            )

            # Clear button logic
            clear_btn.click(
                lambda: (None, None, None, None),
                inputs=None,
                outputs=[imagebox, textbox, output_img, output_coords, state_image_path, state_session_id],
            )

    return demo

if __name__ == "__main__":
    demo = build_demo()
    demo.launch()