app.py

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

import gradio as gr
import json
import os
from typing import Any, List
import spaces

from PIL import Image, ImageDraw
import requests
from transformers import AutoModelForImageTextToText, AutoProcessor
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
import torch
import re 

# --- Configuration ---
MODEL_ID = "Hcompany/Holo1-7B"

# --- Model and Processor Loading (Load once) ---
print(f"Loading model and processor for {MODEL_ID}...")
model = None
processor = None
model_loaded = False
load_error_message = ""

try:
    model = AutoModelForImageTextToText.from_pretrained(
        MODEL_ID,
        torch_dtype=torch.bfloat16,
        attn_implementation="flash_attention_2",
        trust_remote_code=True 
    ).to("cuda")
    processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
    model_loaded = True
    print("Model and processor loaded successfully.")
except Exception as e:
    load_error_message = f"Error loading model/processor: {e}\n" \
                         "This might be due to network issues, an incorrect model ID, or missing dependencies (like flash_attention_2 if enabled by default in some config).\n" \
                         "Ensure you have a stable internet connection and the necessary libraries installed."
    print(load_error_message)

# --- Helper functions from the model card (or adapted) ---

def get_localization_prompt(pil_image: Image.Image, instruction: str) -> List[dict[str, Any]]:
    """
    Prepares the prompt structure for the Holo1 model for localization tasks.
    The `pil_image` argument here is primarily for semantic completeness in the prompt structure,
    as the actual image tensor is handled by the processor later.
    """
    guidelines: str = "Localize an element on the GUI image according to my instructions and output a click position as Click(x, y) with x num pixels from the left edge and y num pixels from the top edge."
    
    return [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": pil_image,
                },
                {"type": "text", "text": f"{guidelines}\n{instruction}"},
            ],
        }
    ]

@spaces.GPU(duration=20)
def run_inference_localization(
    messages_for_template: List[dict[str, Any]], 
    pil_image_for_processing: Image.Image
) -> str:
    model.to("cuda")
    torch.cuda.set_device(0)
    """
    Runs inference using the Holo1 model.
    - messages_for_template: The prompt structure, potentially including the PIL image object 
                             (which apply_chat_template converts to an image tag).
    - pil_image_for_processing: The actual PIL image to be processed into tensors.
    """
    # 1. Apply chat template to messages. This will create the text part of the prompt,
    #    including image tags if the image was part of `messages_for_template`.
    text_prompt = processor.apply_chat_template(
        messages_for_template,
        tokenize=False,
        add_generation_prompt=True
    )

    # 2. Process text and image together to get model inputs
    inputs = processor(
        text=[text_prompt],
        images=[pil_image_for_processing], # Provide the actual image data here
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to(model.device)

    # 3. Generate response
    # Using do_sample=False for more deterministic output, as in the model card's structured output example
    generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False) 
    
    # 4. Trim input_ids from generated_ids to get only the generated part
    generated_ids_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    
    # 5. Decode the generated tokens
    decoded_output = processor.batch_decode(
        generated_ids_trimmed, 
        skip_special_tokens=True, 
        clean_up_tokenization_spaces=False
    )
    
    return decoded_output[0] if decoded_output else ""


# --- Gradio processing function ---
def predict_click_location(input_pil_image: Image.Image, instruction: str):
    if not model_loaded or not processor or not model:
        return f"Model not loaded. Error: {load_error_message}", None
    if not input_pil_image:
        return "No image provided. Please upload an image.", None
    if not instruction or instruction.strip() == "":
        return "No instruction provided. Please type an instruction.", input_pil_image.copy().convert("RGB")

    # 1. Prepare image: Resize according to model's image processor's expected properties
    #    This ensures predicted coordinates match the (resized) image dimensions.
    image_proc_config = processor.image_processor
    try:
        resized_height, resized_width = smart_resize(
            input_pil_image.height,
            input_pil_image.width,
            factor=image_proc_config.patch_size * image_proc_config.merge_size,
            min_pixels=image_proc_config.min_pixels,
            max_pixels=image_proc_config.max_pixels,
        )
        # Using LANCZOS for resampling as it's generally good for downscaling.
        # The model card used `resample=None`, which might imply nearest or default.
        # For visual quality in the demo, LANCZOS is reasonable.
        resized_image = input_pil_image.resize(
            size=(resized_width, resized_height), 
            resample=Image.Resampling.LANCZOS # type: ignore
        )
    except Exception as e:
        print(f"Error resizing image: {e}")
        return f"Error resizing image: {e}", input_pil_image.copy().convert("RGB")

    # 2. Create the prompt using the resized image (for correct image tagging context) and instruction
    messages = get_localization_prompt(resized_image, instruction)

    # 3. Run inference
    #    Pass `messages` (which includes the image object for template processing)
    #    and `resized_image` (for actual tensor conversion).
    try:
        coordinates_str = run_inference_localization(messages, resized_image)
    except Exception as e:
        print(f"Error during model inference: {e}")
        return f"Error during model inference: {e}", resized_image.copy().convert("RGB")

    # 4. Parse coordinates and draw on the image
    output_image_with_click = resized_image.copy().convert("RGB") # Ensure it's RGB for drawing
    parsed_coords = None
    
    # Expected format from the model: "Click(x, y)"
    match = re.search(r"Click\((\d+),\s*(\d+)\)", coordinates_str)
    if match:
        try:
            x = int(match.group(1))
            y = int(match.group(2))
            parsed_coords = (x, y)
            
            draw = ImageDraw.Draw(output_image_with_click)
            # Make the marker somewhat responsive to image size, but not too small/large
            radius = max(5, min(resized_width // 100, resized_height // 100, 15)) 
            
            # Define the bounding box for the ellipse (circle)
            bbox = (x - radius, y - radius, x + radius, y + radius)
            draw.ellipse(bbox, outline="red", width=max(2, radius // 4)) # Draw a red circle
            print(f"Predicted and drawn click at: ({x}, {y}) on resized image ({resized_width}x{resized_height})")
        except ValueError:
            print(f"Could not parse integers from coordinates: {coordinates_str}")
            # Keep original coordinates_str, output_image_with_click will be the resized image without a mark
        except Exception as e:
            print(f"Error drawing on image: {e}")
    else:
        print(f"Could not parse 'Click(x, y)' from model output: {coordinates_str}")
    
    return coordinates_str, output_image_with_click

# --- Load Example Data ---
example_image = None
example_instruction = "Select July 14th as the check-out date"
try:
    example_image_url = "https://huggingface.co/Hcompany/Holo1-7B/resolve/main/calendar_example.jpg"
    example_image = Image.open(requests.get(example_image_url, stream=True).raw)
except Exception as e:
    print(f"Could not load example image from URL: {e}")
    # Create a placeholder image if loading fails, so Gradio example still works
    try:
        example_image = Image.new("RGB", (200, 150), color="lightgray")
        draw = ImageDraw.Draw(example_image)
        draw.text((10, 10), "Example image\nfailed to load", fill="black")
    except: # If PIL itself is an issue (unlikely here but good for robustness)
        pass 


# --- Gradio Interface Definition ---
title = "Holo1-7B: Action VLM Localization Demo"
description = """
This demo showcases **Holo1-7B**, an Action Vision-Language Model developed by HCompany, fine-tuned from Qwen/Qwen2.5-VL-7B-Instruct.
It's designed to interact with web interfaces like a human user. Here, we demonstrate its UI localization capability.

**How to use:**
1. Upload an image (e.g., a screenshot of a UI, like the calendar example).
2. Provide a textual instruction (e.g., "Select July 14th as the check-out date").
3. The model will predict the click coordinates in the format `Click(x, y)`.
4. The predicted click point will be marked with a red circle on the (resized) image.

The model processes a resized version of your input image. Coordinates are relative to this resized image.
"""
article = f"""
<p style='text-align: center'>
Model: <a href='https://huggingface.co/{MODEL_ID}' target='_blank'>{MODEL_ID}</a> by HCompany | 
Paper: <a href='https://cdn.prod.website-files.com/67e2dbd9acff0c50d4c8a80c/683ec8095b353e8b38317f80_h_tech_report_v1.pdf' target='_blank'>HCompany Tech Report</a> |
Blog: <a href='https://www.hcompany.ai/surfer-h' target='_blank'>Surfer-H Blog Post</a>
</p>
"""

if not model_loaded:
    with gr.Blocks() as demo:
        gr.Markdown(f"# <center>⚠️ Error: Model Failed to Load ⚠️</center>")
        gr.Markdown(f"<center>{load_error_message}</center>")
        gr.Markdown("<center>Please check the console output for more details. Reloading the space might help if it's a temporary issue.</center>")
else:
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
        # gr.Markdown(description)

        with gr.Row():
            with gr.Column(scale=1):
                input_image_component = gr.Image(type="pil", label="Input UI Image", height=400)
                instruction_component = gr.Textbox(
                    label="Instruction", 
                    placeholder="e.g., Click the 'Login' button",
                    info="Type the action you want the model to localize on the image."
                )
                submit_button = gr.Button("Localize Click", variant="primary")
            
            with gr.Column(scale=1):
                output_coords_component = gr.Textbox(label="Predicted Coordinates (Format: Click(x,y))", interactive=False)
                output_image_component = gr.Image(type="pil", label="Image with Predicted Click Point", height=400, interactive=False)
        
        if example_image:
            gr.Examples(
                examples=[[example_image, example_instruction]],
                inputs=[input_image_component, instruction_component],
                outputs=[output_coords_component, output_image_component],
                fn=predict_click_location,
                cache_examples="lazy",
            )
        
        gr.Markdown(article)

        submit_button.click(
            fn=predict_click_location,
            inputs=[input_image_component, instruction_component],
            outputs=[output_coords_component, output_image_component]
        )

if __name__ == "__main__":
    demo.launch(debug=True)