diff --git a/README.md b/README.md index 89cbd8d..c881862 100644 --- a/README.md +++ b/README.md @@ -8,14 +8,29 @@ # 🩺 GUI-DR: GUI Domain-Randomization for generating diagnostic GUI grounding evaluation data -

- - Technical Report | Fig - Hugging Face Data - GUI-DR GitHub - Contribute on Discord - +

+ + Technical Reports | Fig + Hugging Face Data + GUI-DR GitHub + Contribute on Discord + +

+ Dataset & Methodology + Model Robustness Evaluation + Finetuning Experiments +

+
+ +
+ + Result Viewers + +

+ Baseline Result Viewer + Finetuned Result Viewer

+
### _GUI-DR is a part of a collaborative effort on Software Control Agents between Manifold Research and Fig_ @@ -246,7 +261,60 @@ Use **this repo** to reproduce or extend the data; use the **Hugging Face datase ## Evaluation -Download the [GUI-Perturbed](https://huggingface.co/datasets/figai/GUI-Perturbed) dataset to evaluate your models. An evaluation script will be released soon. +The evaluation script loads data directly from [GUI-Perturbed](https://huggingface.co/datasets/figai/GUI-Perturbed) on HuggingFace and runs inference against a model served via an OpenAI-compatible API (e.g., [vLLM](https://docs.vllm.ai/)). + +### Prerequisites + +**Serve your model** with vLLM (or any OpenAI-compatible endpoint): + +```bash +# Example: serve your local_model with vLLM +vllm serve "/mnt/disks/eval-data/exp_2_checkpoint_1_epoch/" \ + --tensor-parallel-size 2 \ + --max-model-len 16384 \ + --gpu-memory-utilization 0.9 +``` + +### Running evaluation + +```bash +uv run scripts/gui_perturbed_evaluator.py \ + --output_dir data/predictions \ + --config_id uitars15_no_reasoning_direct_query \ + --dataset_variant original \ + --model_name /mnt/disks/eval-data/exp_2_checkpoint_1_epoch/ +``` + +### Arguments + +| Argument | Default | Description | +|----------|---------|-------------| +| `--output_dir` | _(required)_ | Directory for prediction output files. | +| `--config_id` | _(required)_ | Preset configuration ID. Use `--list_presets` to see all options. | +| `--dataset_variant` | `None` (all) | Filter by variant: `original`, `style`, `precision`, `text_zoom`. | +| `--model_name` | _(from preset)_ | Override the HuggingFace model identifier sent to the API. | +| `--api_url` | `http://localhost:8000/v1` | API endpoint (or set `VLLM_API_URL` env var). | +| `--api_key` | `EMPTY` | API key (or set `VLLM_API_KEY` env var). | +| `--temperature` | `0.0` | Sampling temperature. | +| `--max_tokens` | _(model-specific)_ | Max tokens for generation. | +| `--seed` | `None` | Random seed for reproducibility. | +| `--save_interval` | `10` | Save predictions to disk every N steps. | + +### Available presets + +Presets are generated for all combinations of **model** Γ— **reasoning** Γ— **instruction type**: + +- **Models:** `gta1` (GTA1-7B), `qwen25vl` (Qwen2.5-VL-7B), `uitars15` (UI-TARS-1.5-7B) +- **Reasoning:** `no_reasoning`, `reasoning` +- **Instruction type:** `direct_query`, `relational_query` + +Example preset IDs: `gta1_no_reasoning_direct_query`, `qwen25vl_reasoning_relational_query`, `uitars15_no_reasoning_direct_query`. + +List all presets: + +```bash +uv run scripts/gui_perturbed_evaluator.py --list_presets +``` --- @@ -272,6 +340,12 @@ We welcome contributions: new perturbation types, bug reports, and improvements. --- +## Acknowledgments + +Our finetuning experiments were built on [Qwen-VL-Series-Finetune](https://github.com/2U1/Qwen-VL-Series-Finetune). We thank the authors for their open-source contributions. + +--- + ## πŸ“„ Citation If you find GUI-Perturbed or this pipeline useful, please consider citing the dataset and technical report series. @@ -300,4 +374,20 @@ If you find GUI-Perturbed or this pipeline useful, please consider citing the da url = {https://blog.fig.inc/gui-perturbed-a-domain-randomization-dataset-for-gui-grounding}, note = {Part 1: Dataset \& methodology} } + +@online{measuring_gui_models_robustness_technical_report_2026, + title = {Measuring Brittleness in GUI Grounding Models using GUI-Perturbed}, + author = {Wang, Yangyue and Mathur, Yash, and Zhou, Tony and Nyachhyon, Jinu and Guruprasad, Pranav and Sikka, Harsh}, + year = {2026}, + url = {https://blog.fig.inc/measuring-brittleness-in-gui-grounding-models-using-gui-perturbed}, + note = {Part 2: Baseline evaluation} +} + +@online{training_on_gui_perturbed_technical_report_2026, + title = {Training on GUI-Perturbed: Why More Data Isn’t Enough}, + author = {Wang, Yangyue and Mathur, Yash, and Zhou, Tony and Nyachhyon, Jinu and Guruprasad, Pranav and Sikka, Harsh}, + year = {2026}, + url = {https://blog.fig.inc/training-on-gui-perturbed-why-more-data-isnt-enough}, + note = {Part 3: Finetuning Experiments} +} ``` diff --git a/media/gui-dr.png b/media/gui-dr.png deleted file mode 100644 index 04acb81..0000000 Binary files a/media/gui-dr.png and /dev/null differ diff --git a/pyproject.toml b/pyproject.toml index e6e73e6..f23c1cd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,11 +9,14 @@ dependencies = [ "fastparquet>=2024.11.0", "loguru>=0.7.2", "matplotlib>=3.10.7", + "anthropic>=0.52.0", + "openai>=2.26.0", "pandas>=2.3.3", "pd>=0.0.4", "pillow>=12.0.0", "playwright>=1.55.0", "streamlit>=1.54.0", + "python-dotenv>=1.2.2", ] [project.urls] diff --git a/scripts/gui_perturbed_evaluator.py b/scripts/gui_perturbed_evaluator.py index 8c98d28..884a647 100644 --- a/scripts/gui_perturbed_evaluator.py +++ b/scripts/gui_perturbed_evaluator.py @@ -1,12 +1,13 @@ """ -Standalone CSV-based evaluation script. +Standalone evaluation script. -Loads evaluation data from CSV, runs model inference, and saves raw predictions. +Loads evaluation data from HuggingFace (figai/GUI-Perturbed), runs model inference, and saves raw predictions. """ import argparse import json import os +import re import sys from dataclasses import dataclass from pathlib import Path @@ -14,7 +15,11 @@ from datetime import datetime from enum import Enum -import pandas as pd +from anthropic import Anthropic +from datasets import load_dataset +from dotenv import load_dotenv + +load_dotenv() from openai import OpenAI from PIL import Image from loguru import logger @@ -24,9 +29,11 @@ sys.path.insert(0, str(eval_dir)) from prompts import ( + build_claude_messages, build_gta1_messages, build_uitars15_messages, build_qwen25vl_messages, + resize_image, ) @@ -42,7 +49,7 @@ MAX_PIXELS = 16384 * 28 * 28 MAX_RATIO = 200 -VALID_MODEL_TYPES = {"gta1", "qwen25vl", "uitars15"} +VALID_MODEL_TYPES = {"gta1", "qwen25vl", "uitars15", "claude"} # Model-specific default max_tokens # GTA1 only needs ~32 tokens for coordinate output (x,y), but we use 64 to be safe @@ -53,8 +60,129 @@ ("qwen25vl", True): 1000, ("uitars15", False): 1000, ("uitars15", True): 1000, + ("claude", False): 1024, + ("claude", True): 4096, } +# ============================================================================ +# Coordinate Extraction and Hit Detection +# ============================================================================ + +def extract_coordinates(raw_prediction: str, model_type: str) -> Optional[Tuple[float, float]]: + """Extract (x, y) coordinates from a model's raw prediction. + + Each model type has a different output format: + - gta1: "(x,y)" or "Thought: ... Action: (x,y)" or "Thought: ... (x,y)" + - uitars15: "Action: click(start_box='(x,y)')" or with Thought prefix + - qwen25vl: '{"name":"computer_use","arguments":{"coordinate":[x,y]}}' + - claude: JSON array with tool_use blocks containing {"input":{"coordinate":[x,y]}} + + Returns (x, y) in the model's native coordinate space, or None if parsing fails. + """ + if not raw_prediction or not raw_prediction.strip(): + return None + + try: + if model_type == "claude": + return _extract_claude_coordinates(raw_prediction) + elif model_type == "gta1": + return _extract_gta1_coordinates(raw_prediction) + elif model_type == "uitars15": + return _extract_uitars15_coordinates(raw_prediction) + elif model_type == "qwen25vl": + return _extract_qwen25vl_coordinates(raw_prediction) + except Exception: + return None + return None + + +def _extract_claude_coordinates(raw_prediction: str) -> Optional[Tuple[float, float]]: + """Claude: JSON array of content blocks. Coordinates are already scaled to 1920x1080.""" + blocks = json.loads(raw_prediction) + for block in blocks: + if block.get("type") == "tool_use" and "input" in block: + coord = block["input"].get("coordinate") + if coord and len(coord) == 2: + return (float(coord[0]), float(coord[1])) + return None + + +def _extract_gta1_coordinates(raw_prediction: str) -> Optional[Tuple[float, float]]: + """GTA1: '(x,y)' or 'Thought: ... Action: (x,y)' or 'Thought: ... (x,y)'.""" + # Find last (x,y) pattern β€” in reasoning mode the coordinate comes after the thought + matches = re.findall(r'\((\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\)', raw_prediction) + if matches: + x, y = matches[-1] + return (float(x), float(y)) + return None + + +def _extract_uitars15_coordinates(raw_prediction: str) -> Optional[Tuple[float, float]]: + """UITARS: "click(start_box='(x,y)')" or similar action format.""" + # Match click(start_box='(x,y)') or similar patterns with box coordinates + match = re.search(r"start_box='(?:<\|box_start\|>)?\((\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\)", raw_prediction) + if match: + return (float(match.group(1)), float(match.group(2))) + # Fallback: any (x,y) pattern + matches = re.findall(r'\((\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\)', raw_prediction) + if matches: + return (float(matches[-1][0]), float(matches[-1][1])) + return None + + +def _extract_qwen25vl_coordinates(raw_prediction: str) -> Optional[Tuple[float, float]]: + """Qwen2.5VL: '{"name":"computer_use","arguments":{"coordinate":[x,y]}}'.""" + match = re.search(r'\s*(\{.*?\})\s*', raw_prediction, re.DOTALL) + if match: + tool_call = json.loads(match.group(1)) + coord = tool_call.get("arguments", {}).get("coordinate") + if coord and len(coord) == 2: + return (float(coord[0]), float(coord[1])) + # Fallback: find "coordinate": [x, y] anywhere + match = re.search(r'"coordinate"\s*:\s*\[(\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\]', raw_prediction) + if match: + return (float(match.group(1)), float(match.group(2))) + return None + + +def renormalize_to_original( + coord: Tuple[float, float], + model_type: str, + original_width: int = EXPECTED_IMAGE_WIDTH, + original_height: int = EXPECTED_IMAGE_HEIGHT, +) -> Tuple[float, float]: + """Map predicted coordinates back to the original image dimensions. + + - gta1, uitars15, qwen25vl: coordinates are in the smart_resize'd space. + Renormalize by multiplying by (original / resized) per axis. + - claude: coordinates are already in 1920x1080 space (scaled back in + _parse_claude_response), so no further transformation is needed. + """ + if model_type == "claude": + # Already scaled back to original dimensions during response parsing + return coord + + # Compute the resized dimensions that the VLM models used + resized_image = resize_image(Image.new("RGB", (original_width, original_height))) + resized_w, resized_h = resized_image.size + + x = coord[0] * (original_width / resized_w) + y = coord[1] * (original_height / resized_h) + return (x, y) + + +def is_hit(coord: Tuple[float, float], gt_bbox) -> bool: + """Check whether the predicted coordinate falls inside the ground truth bbox. + + gt_bbox format: [x, y, width, height] where (x, y) is the top-left corner. + Accepts either a list of floats or a JSON string representation. + """ + if isinstance(gt_bbox, str): + gt_bbox = json.loads(gt_bbox) + bx, by, bw, bh = [float(v) for v in gt_bbox] + return bx <= coord[0] <= bx + bw and by <= coord[1] <= by + bh + + # ============================================================================ # Configuration # ============================================================================ @@ -93,13 +221,12 @@ class DatasetConfig: @dataclass class EvaluationConfig: """Overall evaluation configuration.""" - csv_path: Path - screenshots_base_dir: Path output_dir: Path model_config: ModelConfig dataset_config: DatasetConfig api_url: str api_key: str + config_id: str = "" save_interval: int = 10 # Save predictions every N steps @@ -157,37 +284,35 @@ def setup_logging(output_dir: Path) -> Path: # ============================================================================ class DataLoader: - """Loads and filters evaluation data from CSV.""" - - def __init__(self, csv_path: Path, dataset_config: DatasetConfig, screenshots_base_dir: Path): - self.csv_path = csv_path + """Loads and filters evaluation data from HuggingFace (figai/GUI-Perturbed).""" + + def __init__(self, dataset_config: DatasetConfig): self.dataset_config = dataset_config - self.screenshots_base_dir = screenshots_base_dir - self.df = self._load_and_filter() - - def _load_and_filter(self) -> pd.DataFrame: - """Load CSV and filter by dataset variant configuration.""" - df = pd.read_csv(self.csv_path) - + self.rows = self._load_and_filter() + + def _load_and_filter(self) -> List[Dict]: + """Load dataset from HuggingFace and filter by dataset variant configuration.""" + logger.info("Loading dataset from figai/GUI-Perturbed...") + ds = load_dataset("figai/GUI-Perturbed", split="eval") + # Filter by dataset variant type if self.dataset_config.dataset_variant is not None: variant_value = self.dataset_config.dataset_variant.value - df = df[df["variant"] == variant_value] + ds = ds.filter(lambda row: row["visual_variant"] == variant_value) + + # Filter by instruction type + instruction_type_value = self.dataset_config.instruction_type.value + ds = ds.filter(lambda row: row["instruction_type"] == instruction_type_value) + + # Sort by task_id and step_index + ds = ds.sort(["task_id", "step_index"]) + + logger.info(f"Loaded {len(ds)} rows after filtering") + return [ds[i] for i in range(len(ds))] - return df.sort_values(["task_id", "step_index"]).reset_index(drop=True) - def get_rows(self) -> List[Dict]: - """Get all filtered rows with resolved screenshot paths.""" - rows = self.df.to_dict("records") - - for row in rows: - # Get instruction based on instruction type - if self.dataset_config.instruction_type == InstructionType.DIRECT_QUERY: - row["instruction"] = row.get("step_instruction", "") - else: - row["instruction"] = row.get("multi_element_instruction", "") - - return rows + """Get all filtered rows.""" + return self.rows # ============================================================================ @@ -199,27 +324,36 @@ class ModelClient: def __init__(self, config: ModelConfig, api_url: str, api_key: str): self.config = config - self.client = OpenAI(base_url=api_url, api_key=api_key) + if config.model_type == "claude": + self.anthropic_client = Anthropic(api_key=api_key, max_retries=5) + self.client = None + else: + self.client = OpenAI(base_url=api_url, api_key=api_key) + self.anthropic_client = None - def predict(self, instruction: str, image_path: Path, + def predict(self, instruction: str, image: Image.Image, metadata: Optional[Dict[str, Any]] = None) -> str: """ Run model inference on instruction and image. - + Args: instruction: Text instruction - image_path: Path to image file + image: PIL Image for inference metadata: Optional dict with task_id, step_index, variant for logging - + Returns raw prediction text from model. """ - # Load and process image + # Validate image metadata = metadata or {} - image = self._load_image(image_path, **metadata) - + image = self._validate_image(image, **metadata) + + # Claude uses a separate API path + if self.config.model_type == "claude": + return self._predict_claude(instruction, image) + # Build messages messages = self.build_messages(instruction, image, self.config.model_type, self.config.use_reasoning) - + # Make API request request_kwargs = { "model": self.config.name, @@ -230,10 +364,106 @@ def predict(self, instruction: str, image_path: Path, } if self.config.seed is not None: request_kwargs["seed"] = self.config.seed - + response = self.client.chat.completions.create(**request_kwargs) return response.choices[0].message.content.strip() + def _predict_claude(self, instruction: str, image: Image.Image) -> str: + """Run Claude computer use inference via the beta API. + + Implements a mini agent loop matching the official Computer Use pattern: + 1. Send instruction β†’ Claude requests a screenshot via the tool + 2. Return the screenshot as a tool_result β†’ Claude responds with a click + The loop runs for at most MAX_TURNS to avoid runaway costs. + """ + MAX_TURNS = 3 + msg_data = build_claude_messages(instruction, image, self.config.use_reasoning) + encoded_image = msg_data["encoded_image"] + scale_factor = msg_data["scale_factor"] + + base_kwargs = { + "model": self.config.name, + "max_tokens": self.config.max_tokens, + "tools": msg_data["tools"], + } + if self.config.use_reasoning: + base_kwargs["thinking"] = { + "type": "enabled", + "budget_tokens": 2048, + } + else: + base_kwargs["temperature"] = self.config.temperature + + messages = list(msg_data["messages"]) + + for turn in range(MAX_TURNS): + response = self.anthropic_client.beta.messages.create( + betas=["computer-use-2025-01-24"], + messages=messages, + **base_kwargs, + ) + + # Check if any tool_use block has an action with coordinates (i.e. a click) + for block in response.content: + if block.type == "tool_use" and hasattr(block, "input"): + action = block.input.get("action", "") + if action != "screenshot": + # Got a click or other action with coordinates β€” done + return self._parse_claude_response(response, scale_factor) + + # Claude requested a screenshot β€” return the image as tool_result + # Build the assistant message and tool_result for the next turn + messages.append({"role": "assistant", "content": response.content}) + tool_results = [] + for block in response.content: + if block.type == "tool_use": + tool_results.append({ + "type": "tool_result", + "tool_use_id": block.id, + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": encoded_image, + }, + } + ], + }) + messages.append({"role": "user", "content": tool_results}) + + # Fallback: return whatever the last response was + return self._parse_claude_response(response, scale_factor) + + def _parse_claude_response(self, response, scale_factor: float) -> str: + """Parse Claude response into a JSON string of content blocks. + + Coordinates are scaled back to the original 1920x1080 space using + scale_factor, since the image was downscaled before sending to Claude. + """ + blocks = [] + for block in response.content: + if block.type == "thinking": + blocks.append({"type": "thinking", "thinking": block.thinking}) + elif block.type == "text": + blocks.append({"type": "text", "text": block.text}) + elif block.type == "tool_use": + input_data = dict(block.input) + # Scale coordinates back to original image dimensions + if "coordinate" in input_data and scale_factor < 1.0: + x, y = input_data["coordinate"] + input_data["coordinate"] = [ + round(x / scale_factor), + round(y / scale_factor), + ] + blocks.append({ + "type": "tool_use", + "name": block.name, + "input": input_data, + }) + return json.dumps(blocks) + def build_messages(self, instruction: str, image: Image.Image, model_type: str, use_reasoning: bool) -> List[Dict[str, Any]]: """Build messages for model inference.""" if model_type == "gta1": @@ -242,55 +472,37 @@ def build_messages(self, instruction: str, image: Image.Image, model_type: str, return build_uitars15_messages(instruction, image, use_reasoning) elif model_type == "qwen25vl": return build_qwen25vl_messages(instruction, image, use_reasoning) + elif model_type == "claude": + return build_claude_messages(instruction, image, use_reasoning) else: raise ValueError(f"Invalid model type: {model_type}") - def _load_image(self, image_path: Path, - task_id: Optional[str] = None, - step_index: Optional[int] = None, - variant: Optional[str] = None) -> Image.Image: + def _validate_image(self, image: Image.Image, + task_id: Optional[str] = None, + step_index: Optional[int] = None, + variant: Optional[str] = None) -> Image.Image: """ - Load, validate, and resize image using smart_resize. - + Validate and prepare image for inference. + Args: - image_path: Path to image file + image: PIL Image from dataset task_id: Optional task ID for logging step_index: Optional step index for logging variant: Optional variant for logging - + Returns: - Resized image ready for inference + Image ready for inference """ - # the image_path can be inaccurate with the final file name which has the format of step__.png - # and the action can be wrong, so we need to get the correct image path from the task_id and step_index - image_folder = image_path.parent - # use step index and the image folder only because image filename in the csv file sometimes has the wrong action name in the filename. - search_pattern = f"step_{step_index}_*.png" - image_files = list(image_folder.glob(search_pattern)) - - if len(image_files) == 0: - raise FileNotFoundError( - f"Image files not found: pattern '{search_pattern}' in folder {image_folder} " - f"for task {task_id} and step {step_index}" - ) - - image_file = image_files[0] - if len(image_files) > 1: - logger.warning(f"Multiple images found for task {task_id} step {step_index}, using: {image_file}") - - image = Image.open(image_file) if image.mode != "RGB": image = image.convert("RGB") - - # Store original dimensions + original_width, original_height = image.size - - # Check if image is 1920x1080 (expected resolution) + if original_width != EXPECTED_IMAGE_WIDTH or original_height != EXPECTED_IMAGE_HEIGHT: metadata_str = format_metadata_string(task_id, step_index, variant) logger.warning( f"[Image Dimension Check] Image is not {EXPECTED_IMAGE_WIDTH}x{EXPECTED_IMAGE_HEIGHT}: " - f"actual={original_width}x{original_height}{metadata_str} path={image_file}" + f"actual={original_width}x{original_height}{metadata_str}" ) return image @@ -307,9 +519,7 @@ class Evaluator: def __init__(self, config: EvaluationConfig): self.config = config self.data_loader = DataLoader( - config.csv_path, config.dataset_config, - config.screenshots_base_dir ) self.model_client = ModelClient( config.model_config, @@ -323,83 +533,111 @@ def _get_output_path(self) -> Path: """Generate output file path based on configuration.""" timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = ( - f"predictions_" - f"{self.config.model_config.model_type}_" - f"{'reasoning' if self.config.model_config.use_reasoning else 'no_reasoning'}_" - f"{self.config.dataset_config.instruction_type.value}_" - f"{timestamp}.jsonl" + f"predictions_{self.config.config_id}_{timestamp}.jsonl" ) return self.config.output_dir / filename def run(self): - """Run evaluation on all CSV rows.""" + """Run evaluation on all dataset rows.""" rows = self.data_loader.get_rows() total_rows = len(rows) - + hits = 0 + parsed = 0 + logger.info(f"Starting evaluation on {total_rows} rows") - + self.output_path.parent.mkdir(parents=True, exist_ok=True) - + try: with open(self.output_path, "w", encoding="utf-8") as f: for idx, row in enumerate(rows, 1): prediction = self._process_row(row, step_num=idx, total_rows=total_rows) json.dump(prediction, f, ensure_ascii=False) f.write("\n") - + + if prediction["predicted_coordinate"] is not None: + parsed += 1 + if prediction["is_hit"]: + hits += 1 + if idx % self.save_interval == 0: f.flush() - + if idx % 100 == 0: - logger.info(f"Processed {idx}/{total_rows} rows ({idx/total_rows*100:.1f}%)") + acc = hits / idx * 100 + logger.info( + f"Processed {idx}/{total_rows} ({idx/total_rows*100:.1f}%) | " + f"Running accuracy: {acc:.1f}% ({hits}/{idx}) | " + f"Parse rate: {parsed}/{idx}" + ) except Exception as e: logger.error(f"Error processing row {idx}: {e}") raise e + # Final accuracy summary + acc = hits / total_rows * 100 if total_rows > 0 else 0 + logger.info("=" * 80) logger.info(f"Evaluation completed. Processed {total_rows} rows") + logger.info(f"Hit accuracy: {acc:.2f}% ({hits}/{total_rows})") + logger.info(f"Parse rate: {parsed}/{total_rows} ({parsed/total_rows*100:.1f}%)") + logger.info(f"Results saved to: {self.output_path}") + logger.info("=" * 80) def _process_row(self, row: Dict, step_num: int, total_rows: int) -> Dict: - """Process a single CSV row and return prediction.""" + """Process a single dataset row and return prediction with hit detection.""" logger.info("=" * 80) logger.info(f"Step {step_num}/{total_rows} ({step_num/total_rows*100:.1f}%)") logger.info("=" * 80) - + + model_type = self.config.model_config.model_type instruction = row["instruction"] - image_path = self.data_loader.screenshots_base_dir / row["image_path"] - + image = row["screenshot"] + # Prepare metadata for logging metadata = { "task_id": row.get("task_id"), "step_index": row.get("step_index"), - "variant": row.get("variant"), + "variant": row.get("visual_variant"), } - - raw_prediction = self.model_client.predict(instruction, image_path, metadata=metadata) + + raw_prediction = self.model_client.predict(instruction, image, metadata=metadata) logger.info(f"Instruction: {instruction}") - logger.info(f"Image path: {image_path}") - + # Truncate very long predictions in logs (likely model hallucination) if len(raw_prediction) > 500: logger.warning(f"Raw prediction is unusually long ({len(raw_prediction)} chars), truncating log output") logger.info(f"Raw prediction (first 500 chars): \n{raw_prediction[:500]}...") else: logger.info(f"Raw prediction: \n{raw_prediction}") - - logger.info(f"Ground truth bbox: {row['target_bounding_box']}") + + # Extract and renormalize coordinates, then check hit + gt_bbox = row["gt_bbox"] + raw_coord = extract_coordinates(raw_prediction, model_type) + predicted_coord = None + hit = False + + if raw_coord is not None: + predicted_coord = renormalize_to_original(raw_coord, model_type) + hit = is_hit(predicted_coord, gt_bbox) + + logger.info(f"Ground truth bbox: {gt_bbox}") + logger.info(f"Predicted coordinate: {predicted_coord}") + logger.info(f"Is hit: {hit}") logger.info("=" * 80) - + return { - "model": self.config.model_config.model_type, + "config_id": self.config.config_id, + "model": model_type, "use_reasoning": self.config.model_config.use_reasoning, "query_type": self.config.dataset_config.instruction_type.value, - "test_split": row['split'], "variant": metadata["variant"], "task_id": row["task_id"], "step_index": row["step_index"], "instruction": instruction, "raw_prediction": raw_prediction, - "ground_truth_bbox": row["target_bounding_box"], - "image_path": str(image_path), + "ground_truth_bbox": gt_bbox, + "predicted_coordinate": list(predicted_coord) if predicted_coord else None, + "is_hit": hit, } @@ -440,7 +678,7 @@ def _generate_all_presets() -> Dict[str, EvaluationPreset]: """Generate all possible evaluation configuration presets. Generates 12 total combinations: - - 3 models (gta1, qwen25vl, uitars15) + - 4 models (gta1, qwen25vl, uitars15, claude) - 2 reasoning modes (with/without) - 2 instruction types (direct_query, relational_query) @@ -450,14 +688,15 @@ def _generate_all_presets() -> Dict[str, EvaluationPreset]: presets = {} # Define all model types explicitly - MODEL_TYPES = ["gta1", "qwen25vl", "uitars15"] - + MODEL_TYPES = ["gta1", "qwen25vl", "uitars15", "claude"] + # Define all other dimensions explicitly REASONING_MODES = [False, True] MODEL_NAMES = { "gta1": "HelloKKMe/GTA1-7B", "qwen25vl": "Qwen/Qwen2.5-VL-7B-Instruct", "uitars15": "ByteDance-Seed/UI-TARS-1.5-7B", + "claude": "claude-sonnet-4-20250514", } INSTRUCTION_TYPES = [ InstructionType.DIRECT_QUERY, @@ -507,9 +746,7 @@ def parse_args() -> argparse.Namespace: """Parse command line arguments.""" parser = argparse.ArgumentParser(description="evaluation script") - # CSV and output - parser.add_argument("--csv_path", type=Path, required=True, help="Path to CSV file") - parser.add_argument("--screenshots_base_dir", type=Path, required=True, help="Base directory containing screenshot folders") + # Output parser.add_argument("--output_dir", type=Path, required=True, help="Output directory") # Configuration selection @@ -518,7 +755,7 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--dataset_variant", default=None, type=str, choices=["style", "precision", "text_zoom", "original"], help="Dataset variant to evaluate") # Model configuration (optional overrides) - parser.add_argument("--model_name", type=str, default='ByteDance-Seed/UI-TARS-1.5-7B', help="HuggingFace model identifier for vLLM (e.g., 'ByteDance-Seed/UI-TARS-1.5-7B')") + parser.add_argument("--model_name", type=str, default=None, help="Override model name for vLLM (e.g., path to local checkpoint)") parser.add_argument("--temperature", type=float, default=0.0) parser.add_argument("--max_tokens", type=int, default=1000) parser.add_argument("--top_p", type=float, default=0.9) @@ -587,7 +824,7 @@ def build_config(args: argparse.Namespace) -> EvaluationConfig: logger.info(f" max_tokens: {max_tokens}") model_config = ModelConfig( - name=preset.model_name, + name=args.model_name if args.model_name is not None else preset.model_name, model_type=preset.model_type, use_reasoning=preset.use_reasoning, temperature=args.temperature, @@ -610,16 +847,18 @@ def build_config(args: argparse.Namespace) -> EvaluationConfig: ) api_url = args.api_url or os.environ.get("VLLM_API_URL", "http://localhost:8000/v1") - api_key = args.api_key or os.environ.get("VLLM_API_KEY", "EMPTY") + if preset.model_type == "claude": + api_key = args.api_key or os.environ.get("ANTHROPIC_API_KEY", "") + else: + api_key = args.api_key or os.environ.get("VLLM_API_KEY", "EMPTY") return EvaluationConfig( - csv_path=args.csv_path, - screenshots_base_dir=args.screenshots_base_dir, output_dir=args.output_dir, model_config=model_config, dataset_config=dataset_config, api_url=api_url, api_key=api_key, + config_id=args.config_id, save_interval=args.save_interval, ) @@ -641,9 +880,7 @@ def main(): """ -uv run eval/gui_perturbed_evaluator.py \ - --csv_path /Users/lockewang/FIG/WebDomainRandomizer/data/variant_data_cleaned.csv \ - --screenshots_base_dir /Users/lockewang/FIG/WebDomainRandomizer/test_splits/ \ +uv run scripts/gui_perturbed_evaluator.py \ --output_dir data/gui_perturbed_eval/predictions \ --seed 42 \ --config_id gta1_no_reasoning_direct_query diff --git a/scripts/prompts.py b/scripts/prompts.py new file mode 100644 index 0000000..960022d --- /dev/null +++ b/scripts/prompts.py @@ -0,0 +1,385 @@ +""" +Prompt templates for different model types and reasoning configurations. +""" + +import base64 +from io import BytesIO +from PIL import Image +from typing import List, Dict, Any, Tuple +import math + +EXPECTED_IMAGE_WIDTH = 1920 +EXPECTED_IMAGE_HEIGHT = 1080 + +IMAGE_FACTOR = 28 +MIN_PIXELS = 100 * 28 * 28 +MAX_PIXELS = 16384 * 28 * 28 +MAX_RATIO = 200 + +try: + from qwen_vl_utils import smart_resize +except ImportError: + # Fallback implementation when qwen_vl_utils is not available + def _round_by_factor(number: int, factor: int) -> int: + """Returns the closest integer to 'number' that is divisible by 'factor'.""" + return round(number / factor) * factor + + def _ceil_by_factor(number: int, factor: int) -> int: + """Returns the smallest integer >= 'number' that is divisible by 'factor'.""" + return math.ceil(number / factor) * factor + + def _floor_by_factor(number: int, factor: int) -> int: + """Returns the largest integer <= 'number' that is divisible by 'factor'.""" + return math.floor(number / factor) * factor + + def smart_resize(height: int, width: int, factor: int = IMAGE_FACTOR, + min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS) -> Tuple[int, int]: + """Rescale image dimensions to meet constraints.""" + if max(height, width) / min(height, width) > MAX_RATIO: + raise ValueError(f"Aspect ratio must be < {MAX_RATIO}") + h_bar = max(factor, _round_by_factor(height, factor)) + w_bar = max(factor, _round_by_factor(width, factor)) + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = _floor_by_factor(height / beta, factor) + w_bar = _floor_by_factor(width / beta, factor) + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = _ceil_by_factor(height * beta, factor) + w_bar = _ceil_by_factor(width * beta, factor) + return h_bar, w_bar + +def resize_image(image: Image.Image) -> Image.Image: + """Resize image to expected resolution.""" + original_width, original_height = image.size + + # Apply smart resize + resized_height, resized_width = smart_resize( + original_height, + original_width, + factor=IMAGE_FACTOR, + min_pixels=MIN_PIXELS, + max_pixels=MAX_PIXELS, + ) + + # Resize the image + resized_image = image.resize((resized_width, resized_height), Image.Resampling.LANCZOS) + return resized_image + +def convert_pil_image_to_base64(image: Image.Image) -> str: + """Convert PIL Image to base64 string.""" + buffered = BytesIO() + image.save(buffered, format="PNG") + return base64.b64encode(buffered.getvalue()).decode() + + +# ============================================================================ +# Prompt Templates +# ============================================================================ + +UITARS_USR_PROMPT_THOUGHT = """You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. + +## Output Format +``` +Thought: ... +Action: ... +``` + +## Action Space +click(start_box='<|box_start|>(x1,y1)<|box_end|>') +left_double(start_box='<|box_start|>(x1,y1)<|box_end|>') +right_single(start_box='<|box_start|>(x1,y1)<|box_end|>') +drag(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x3,y3)<|box_end|>') +hotkey(key='') +type(content='') #If you want to submit your input, use "\\n" at the end of `content`. +scroll(start_box='<|box_start|>(x1,y1)<|box_end|>', direction='down or up or right or left') +wait() #Sleep for 5s and take a screenshot to check for any changes. +finished() +call_user() # Submit the task and call the user when the task is unsolvable, or when you need the user's help. + +## Note +- Use English in `Thought` part. +- Write a small plan and finally summarize your next action (with its target element) in one sentence in `Thought` part. + +## User Instruction +{instruction} +""" + +UITARS_USR_PROMPT_NOTHOUGHT = """You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. +## Output Format +``` +Action: ... +``` +## Action Space +click(start_box='<|box_start|>(x1,y1)<|box_end|>') +left_double(start_box='<|box_start|>(x1,y1)<|box_end|>') +right_single(start_box='<|box_start|>(x1,y1)<|box_end|>') +drag(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x3,y3)<|box_end|>') +hotkey(key='') +type(content='') #If you want to submit your input, use "\\n" at the end of `content`. +scroll(start_box='<|box_start|>(x1,y1)<|box_end|>', direction='down or up or right or left') +wait() #Sleep for 5s and take a screenshot to check for any changes. +finished() +call_user() # Submit the task and call the user when the task is unsolvable, or when you need the user's help. +## User Instruction +{instruction} +""" + +GTA1_SYSTEM_PROMPT = """ +You are an expert UI element locator. Given a GUI image and a user's element description, provide the coordinates of the specified element as a single (x,y) point. The image resolution is height {height} and width {width}. For elements with area, return the center point. + +Output the coordinate pair exactly: +(x,y) +""" + +GTA1_SYSTEM_PROMPT_THOUGHT = """ +You are an expert UI element locator. Given a GUI image and a user's element description, provide the coordinates of the specified element as a single (x,y) point. The image resolution is height {height} and width {width}. For elements with area, return the center point. + +## Output Format +``` +Thought: ... +Action: (x,y) +``` + +## Note +- Use English in `Thought` part. +- Write a small plan and finally summarize your next action (with its target element) in one sentence in `Thought` part. + +Then output the coordinate pair exactly: +(x,y) +""" + + +# Claude Computer Use: Anthropic auto-generates the base computer-use system prompt +# when the tool is present. Computer Use is designed for an agent loop where Claude +# first requests a screenshot, then acts on it. The evaluator handles this by +# returning the screenshot as a tool_result when Claude requests it. + + +QWEN25_SYSTEM_PROMPT_NOTHOUGHT = ( + "You are a helpful assistant.\n\n\n" + "# Tools\n\n" + "You may call one or more functions to assist with the user query.\n\n" + "You are provided with function signatures within XML tags:\n" + "\n" + "{\"type\": \"function\", \"function\": {\"name_for_human\": \"computer_use\", \"name\": \"computer_use\", \"description\": \"Use a mouse and keyboard to interact with a computer, and take screenshots.\\n* This is an interface to a desktop GUI. You do not have access to a terminal or applications menu. You must click on desktop icons to start applications.\\n* Some applications may take time to start or process actions, so you may need to wait and take successive screenshots to see the results of your actions. E.g. if you click on Firefox and a window doesn't open, try wait and taking another screenshot.\\n* The screen's resolution is {screen_width}x{screen_height}.\\n* Whenever you intend to move the cursor to click on an element like an icon, you should consult a screenshot to determine the coordinates of the element before moving the cursor.\\n* If you tried clicking on a program or link but it failed to load, even after waiting, try adjusting your cursor position so that the tip of the cursor visually falls on the element that you want to click.\\n* Make sure to click any buttons, links, icons, etc with the cursor tip in the center of the element. Don't click boxes on their edges unless asked.\", \"parameters\": {\"properties\": {\"action\": {\"description\": \"The action to perform. The available actions are:\\n* `key`: Performs key down presses on the arguments passed in order, then performs key releases in reverse order.\\n* `type`: Type a string of text on the keyboard.\\n* `mouse_move`: Move the cursor to a specified (x, y) pixel coordinate on the screen.\\n* `left_click`: Click the left mouse button.\\n* `left_click_drag`: Click and drag the cursor to a specified (x, y) pixel coordinate on the screen.\\n* `right_click`: Click the right mouse button.\\n* `middle_click`: Click the middle mouse button.\\n* `double_click`: Double-click the left mouse button.\\n* `scroll`: Performs a scroll of the mouse scroll wheel.\\n* `wait`: Wait specified seconds for the change to happen.\\n* `terminate`: Terminate the current task and report its completion status.\", \"enum\": [\"key\", \"type\", \"mouse_move\", \"left_click\", \"left_click_drag\", \"right_click\", \"middle_click\", \"double_click\", \"scroll\", \"wait\", \"terminate\"], \"type\": \"string\"}, \"keys\": {\"description\": \"Required only by `action=key`.\", \"type\": \"array\"}, \"text\": {\"description\": \"Required only by `action=type`.\", \"type\": \"string\"}, \"coordinate\": {\"description\": \"(x, y): The x (pixels from the left edge) and y (pixels from the top edge) coordinates to move the mouse to. Required only by `action=mouse_move` and `action=left_click_drag`.\", \"type\": \"array\"}, \"pixels\": {\"description\": \"The amount of scrolling to perform. Positive values scroll up, negative values scroll down. Required only by `action=scroll`.\", \"type\": \"number\"}, \"time\": {\"description\": \"The seconds to wait. Required only by `action=wait`.\", \"type\": \"number\"}, \"status\": {\"description\": \"The status of the task. Required only by `action=terminate`.\", \"type\": \"string\", \"enum\": [\"success\", \"failure\"]}}, \"required\": [\"action\"], \"type\": \"object\"}, \"args_format\": \"Format the arguments as a JSON object.\"}\n" + "\n\n" + "For each function call, return a json object with function name and arguments within XML tags:\n" + "\n{\"name\": , \"arguments\": }\n\n" +) + +QWEN25_SYSTEM_PROMPT_THOUGHT = ( + "You are a helpful assistant.\n\n\n" + "# Output Format\n\n" + "Before making a tool call, you should think through your approach. Use the following format:\n\n" + "Thought: [Write a small plan analyzing the current screenshot, identifying the target element(s), and summarizing your next action with its target element in one sentence.]\n\n" + "Then make your tool call.\n\n\n" + "# Tools\n\n" + "You may call one or more functions to assist with the user query.\n\n" + "You are provided with function signatures within XML tags:\n" + "\n" + "{\"type\": \"function\", \"function\": {\"name_for_human\": \"computer_use\", \"name\": \"computer_use\", \"description\": \"Use a mouse and keyboard to interact with a computer, and take screenshots.\\n* This is an interface to a desktop GUI. You do not have access to a terminal or applications menu. You must click on desktop icons to start applications.\\n* Some applications may take time to start or process actions, so you may need to wait and take successive screenshots to see the results of your actions. E.g. if you click on Firefox and a window doesn't open, try wait and taking another screenshot.\\n* The screen's resolution is {screen_width}x{screen_height}.\\n* Whenever you intend to move the cursor to click on an element like an icon, you should consult a screenshot to determine the coordinates of the element before moving the cursor.\\n* If you tried clicking on a program or link but it failed to load, even after waiting, try adjusting your cursor position so that the tip of the cursor visually falls on the element that you want to click.\\n* Make sure to click any buttons, links, icons, etc with the cursor tip in the center of the element. Don't click boxes on their edges unless asked.\", \"parameters\": {\"properties\": {\"action\": {\"description\": \"The action to perform. The available actions are:\\n* `key`: Performs key down presses on the arguments passed in order, then performs key releases in reverse order.\\n* `type`: Type a string of text on the keyboard.\\n* `mouse_move`: Move the cursor to a specified (x, y) pixel coordinate on the screen.\\n* `left_click`: Click the left mouse button.\\n* `left_click_drag`: Click and drag the cursor to a specified (x, y) pixel coordinate on the screen.\\n* `right_click`: Click the right mouse button.\\n* `middle_click`: Click the middle mouse button.\\n* `double_click`: Double-click the left mouse button.\\n* `scroll`: Performs a scroll of the mouse scroll wheel.\\n* `wait`: Wait specified seconds for the change to happen.\\n* `terminate`: Terminate the current task and report its completion status.\", \"enum\": [\"key\", \"type\", \"mouse_move\", \"left_click\", \"left_click_drag\", \"right_click\", \"middle_click\", \"double_click\", \"scroll\", \"wait\", \"terminate\"], \"type\": \"string\"}, \"keys\": {\"description\": \"Required only by `action=key`.\", \"type\": \"array\"}, \"text\": {\"description\": \"Required only by `action=type`.\", \"type\": \"string\"}, \"coordinate\": {\"description\": \"(x, y): The x (pixels from the left edge) and y (pixels from the top edge) coordinates to move the mouse to. Required only by `action=mouse_move` and `action=left_click_drag`.\", \"type\": \"array\"}, \"pixels\": {\"description\": \"The amount of scrolling to perform. Positive values scroll up, negative values scroll down. Required only by `action=scroll`.\", \"type\": \"number\"}, \"time\": {\"description\": \"The seconds to wait. Required only by `action=wait`.\", \"type\": \"number\"}, \"status\": {\"description\": \"The status of the task. Required only by `action=terminate`.\", \"type\": \"string\", \"enum\": [\"success\", \"failure\"]}}, \"required\": [\"action\"], \"type\": \"object\"}, \"args_format\": \"Format the arguments as a JSON object.\"}\n" + "\n\n" + "For each function call, first write your Thought, then return a json object with function name and arguments within XML tags:\n" + "Thought: [Your reasoning and plan]\n" + "\n{\"name\": , \"arguments\": }\n\n" +) + + +# ============================================================================ +# Build Messages +# ============================================================================ + +def build_gta1_messages(instruction: str, image: Image.Image, use_reasoning: bool) -> List[Dict[str, Any]]: + resized_image = resize_image(image) + encoded_resized_image = convert_pil_image_to_base64(resized_image) + + # Select appropriate prompt template + if use_reasoning: + system_prompt = GTA1_SYSTEM_PROMPT_THOUGHT.format(height=image.height, width=image.width) + else: + system_prompt = GTA1_SYSTEM_PROMPT.format(height=image.height, width=image.width) + + system_message = { + "role": "system", + "content": system_prompt + } + + user_message = { + "role": "user", + "content": [ + {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_resized_image}"}}, + {"type": "text", "text": instruction} + ] + } + + return [system_message, user_message] + + +def build_uitars15_messages(instruction: str, image: Image.Image, use_reasoning: bool) -> List[Dict[str, Any]]: + resized_image = resize_image(image) + encoded_resized_image = convert_pil_image_to_base64(resized_image) + + if use_reasoning: + prompt = UITARS_USR_PROMPT_THOUGHT.format(instruction=instruction) + else: + prompt = UITARS_USR_PROMPT_NOTHOUGHT.format(instruction=instruction) + + messages = [ + { + "role": "system", + "content": [{"type": "text", "text": "You are a helpful assistant."}] + }, + { + "role": "user", + "content": [{"type": "text", "text": prompt}] + }, + { + "role": "user", + "content": [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_resized_image}"}}] + } + ] + return messages + + +# Claude vision constraints (from Anthropic docs) +CLAUDE_MAX_LONG_EDGE = 1568 +CLAUDE_MAX_PIXELS = 1_150_000 # ~1.15 megapixels + + +def _claude_scale_factor(width: int, height: int) -> float: + """Compute the downscale factor to fit within Claude's vision constraints. + + Returns a factor <= 1.0 by which both dimensions should be multiplied. + """ + factor = 1.0 + # Constraint 1: longest edge <= 1568 + longest = max(width, height) + if longest > CLAUDE_MAX_LONG_EDGE: + factor = min(factor, CLAUDE_MAX_LONG_EDGE / longest) + # Constraint 2: total pixels <= ~1.15M + pixels = width * height + if pixels * (factor ** 2) > CLAUDE_MAX_PIXELS: + factor = min(factor, (CLAUDE_MAX_PIXELS / pixels) ** 0.5) + return factor + + +def build_claude_messages(instruction: str, image: Image.Image, use_reasoning: bool) -> Dict[str, Any]: + """Build Anthropic-format messages for Claude computer use. + + Unlike the OpenAI-format builders, this returns a dict with separate + messages and tools fields for the Anthropic API. + + Claude Computer Use follows an agent loop: it first requests a screenshot + via the tool, then acts on it. The evaluator handles this loop by returning + the provided screenshot as a tool_result. + + The image is resized to fit Claude's vision constraints (max 1568px longest + edge, ~1.15 megapixels). The display dimensions in the tool definition are + set to the *resized* size so Claude returns coordinates in that space. + A scale_factor is returned so the evaluator can map coordinates back to the + original 1920x1080 space for bbox comparison. + """ + if image.mode != "RGB": + image = image.convert("RGB") + + orig_w, orig_h = image.size + scale = _claude_scale_factor(orig_w, orig_h) + + if scale < 1.0: + new_w = int(orig_w * scale) + new_h = int(orig_h * scale) + image = image.resize((new_w, new_h), Image.Resampling.LANCZOS) + else: + new_w, new_h = orig_w, orig_h + + encoded_image = convert_pil_image_to_base64(image) + + # Initial user message with just the instruction. + # The screenshot is NOT included here β€” it will be returned as a + # tool_result when Claude requests a screenshot in the agent loop. + messages = [ + { + "role": "user", + "content": instruction, + } + ] + + tools = [ + { + "type": "computer_20250124", + "name": "computer", + "display_width_px": new_w, + "display_height_px": new_h, + "display_number": 1, + } + ] + + return { + "messages": messages, + "tools": tools, + "encoded_image": encoded_image, + "scale_factor": scale, + } + + +def build_qwen25vl_messages(instruction: str, image: Image.Image, use_reasoning: bool) -> List[Dict[str, Any]]: + """ + Build messages for Qwen2.5VL following the official example format. + + Args: + instruction: Text instruction for the model + image: PIL Image (should be resized) + screen_width: Width of the resized image + screen_height: Height of the resized image + use_reasoning: Whether to use reasoning prompt template + + Returns: + List of message dictionaries in OpenAI format + """ + # Encode image to base64 + resized_image = resize_image(image) + encoded_resized_image = convert_pil_image_to_base64(resized_image) + + # Select appropriate prompt template + if use_reasoning: + prompt_template = QWEN25_SYSTEM_PROMPT_THOUGHT.replace("{screen_width}", str(resized_image.width)).replace("{screen_height}", str(resized_image.height)) + else: + prompt_template = QWEN25_SYSTEM_PROMPT_NOTHOUGHT.replace("{screen_width}", str(resized_image.width)).replace("{screen_height}", str(resized_image.height)) + + # Split system prompt into first line and rest + # The first line is "You are a helpful assistant." or similar + lines = prompt_template.split('\n', 1) + first_line = lines[0] if lines else "You are a helpful assistant." + rest_of_prompt = lines[1] if len(lines) > 1 else "" + + return [ + { + "role": "system", + "content": [ + { + "type": "text", + "text": first_line + }, + { + "type": "text", + "text": rest_of_prompt + } + ] + }, + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": "data:image/png;base64," + encoded_resized_image + } + }, + { + "type": "text", + "text": instruction + } + ] + } + ] diff --git a/uv.lock b/uv.lock index d8e4c60..2156ee8 100644 --- a/uv.lock +++ b/uv.lock @@ -162,6 +162,34 @@ wheels = [ { url = 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