diff --git a/.gitignore b/.gitignore index e7437f7..8c442ce 100644 --- a/.gitignore +++ b/.gitignore @@ -10,4 +10,7 @@ docs/_build *.egg-info # MacOS files -.DS_Store \ No newline at end of file +.DS_Store + +# data +web_app/test_example/ diff --git a/UV_MIGRATION.md b/UV_MIGRATION.md new file mode 100644 index 0000000..a25cdba --- /dev/null +++ b/UV_MIGRATION.md @@ -0,0 +1,45 @@ +# UV Migration Guide + +This project has been migrated from pixi to uv for dependency management. + +## Installation + +1. Install uv if not already installed: + ```bash + curl -LsSf https://astral.sh/uv/install.sh | sh + ``` + +2. Sync dependencies: + ```bash + uv sync + ``` + +3. Install the project in editable mode: + ```bash + uv pip install -e . + ``` + +## Common Commands + +### Development +- Install dependencies: `uv sync` +- Add a dependency: `uv add package-name` +- Add a dev dependency: `uv add --dev package-name` +- Remove a dependency: `uv remove package-name` + +### Running the project +- Activate virtual environment: `source .venv/bin/activate` (or use `uv run`) +- Run scripts with uv: `uv run python script.py` +- Run the visualizer: `uv run quac-visualizer` (replaces `pixi run visualizer`) + +### Environments +The project supports different environments through optional dependencies: +- Development: `uv sync --extra dev` (installs pytest, black, mypy, etc.) +- Web: `uv sync --extra web` (installs flask, werkzeug) +- Docs: `uv sync --extra docs` (installs sphinx and related packages) + +### Migration Notes +- Pixi's conda channels (nvidia, pytorch) are replaced by PyPI packages +- The `funlib.learn.torch` git dependency is handled via `[tool.uv.sources]` +- The pixi task `visualizer` is replaced by the `quac-visualizer` script +- Python version is constrained to `>=3.10,<3.13` for PyTorch compatibility diff --git a/pyproject.toml b/pyproject.toml index 83fc276..b6c8d60 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ requires = ["setuptools", "wheel"] name = "quac" description = "Quantitative Attribution with Counterfactuals" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.10,<3.13" classifiers = ["Programming Language :: Python :: 3"] keywords = [] license = { text = "BSD 3-Clause License" } @@ -18,9 +18,8 @@ dynamic = ["version"] dependencies = [ "captum", "numpy>=2.0", - "torch==2.4.0", - "torchvision", - "funlib.learn.torch@git+https://github.com/funkelab/funlib.learn.torch", + "torch>=2.4.0,<2.5", + "torchvision>=0.19.0", "opencv-python", "pydantic", "scipy", @@ -33,6 +32,7 @@ dependencies = [ [project.optional-dependencies] dev = ['pytest', 'black', 'mypy', 'pdoc', 'pre-commit'] +web = ['flask>=2.0.0', 'werkzeug>=2.0.0'] docs = [ "ipykernel", "jupyter_sphinx", @@ -53,16 +53,21 @@ docs = [ homepage = "https://funkelab.github.io/quac/" repository = "https://github.com/funkelab/quac" -[tool.pixi.project] -channels = ["nvidia/label/cuda-11.8.0", "nvidia", "conda-forge", "pytorch"] -platforms = ["linux-64"] +[tool.setuptools.dynamic] +version = { attr = "quac.__version__" } -[tool.pixi.pypi-dependencies] -quac = { path = ".", editable = true } +[tool.uv] +dev-dependencies = [ + "pytest", + "black", + "mypy", + "pdoc", + "pre-commit" +] -[tool.pixi.environments] -default = { solve-group = "default" } -dev = { features = ["dev"], solve-group = "default" } -docs = { features = ["docs"], solve-group = "default" } +[tool.uv.sources] +# Handle the git dependency for funlib.learn.torch +funlib-learn-torch = { git = "https://github.com/funkelab/funlib.learn.torch" } -[tool.pixi.tasks] +[tool.mypy] +python_version = "3.10" diff --git a/src/quac/explanation.py b/src/quac/explanation.py index 0f96f19..c6f7a62 100644 --- a/src/quac/explanation.py +++ b/src/quac/explanation.py @@ -42,6 +42,7 @@ def __init__( score_changes: Optional[Union[list, np.ndarray]] = None, optimal_threshold: Optional[float] = None, method: Optional[str] = None, + annotation: str = "", ): self._query_path = query_path self._counterfactual_path = counterfactual_path @@ -59,6 +60,7 @@ def __init__( self._score_changes = score_changes self._optimal_threshold = optimal_threshold self._method = method + self.annotation = annotation # Computed self._query: Optional[torch.Tensor] = None @@ -78,6 +80,7 @@ def __eq__(self, value): and self.source_class == value.source_class and self.target_class == value.target_class and self.score == value.score + and self.annotation == value.annotation ) # The other, optional, attributes are not checked for equality return False @@ -179,4 +182,5 @@ def explanation_encoder(explanation: Explanation): "generated_prediction": serialize(explanation.generated_prediction), "optimal_threshold": explanation._optimal_threshold, "method": explanation._method, + "annotation": explanation.annotation, } diff --git a/tests/test_evaluation.py b/tests/test_evaluation.py index a21bcf0..b79bc83 100644 --- a/tests/test_evaluation.py +++ b/tests/test_evaluation.py @@ -1,11 +1,11 @@ import pytest from quac.evaluation import Evaluator -from funlib.learn.torch.models import Vgg2D +from torchvision.models.resnet import resnet18 import numpy as np @pytest.mark.skip("Skip test until we have usable data in CI/CD") -@pytest.mark.parametrize("input_shape", [(1, 128, 128)]) +@pytest.mark.parametrize("input_shape", [(3, 224, 224)]) @pytest.mark.parametrize("num_thresholds", [50, 100]) def test_evaluator(input_shape, num_thresholds): """Checks that evaluator runs without errors""" @@ -16,9 +16,7 @@ def test_evaluator(input_shape, num_thresholds): attribution = np.random.rand(*input_shape) vmin = -1 vmax = 1 - classifier = Vgg2D( - (input_shape[1], input_shape[2]), output_classes=2, input_fmaps=input_shape[0] - ) + classifier = resnet18(num_classes=2) predictions = { "original": np.random.rand(2), diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..c665552 --- /dev/null +++ b/uv.lock @@ -0,0 +1,3035 @@ +version = 1 +revision = 3 +requires-python = ">=3.10, <3.13" +resolution-markers = [ + "python_full_version >= '3.12' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or 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dependencies feature, so no separate installation is needed: + +```bash +# Run directly with uv (will automatically install dependencies) +uv run web_app/quac_visualizer.py --report-path /path/to/your/report.json +``` + +### Traditional Installation + +Alternatively, you can install the web dependencies manually: + +```bash +# Install the main QuAC package with web dependencies +pip install -e .[web] + +# Or install QuAC package separately and add web dependencies +pip install flask werkzeug +``` + +## Usage + +### Using uv (Recommended) + +```bash +# Load a specific report file +uv run web_app/quac_visualizer.py --report-path /path/to/your/report.json + +# Load from a directory with multiple reports +uv run web_app/quac_visualizer.py --report-dir /path/to/reports/directory + +# Additional options +uv run web_app/quac_visualizer.py --report-path /path/to/report.json \ + --host 0.0.0.0 \ + --port 8000 \ + --debug +``` + +### Traditional Usage + +```bash +cd web_app +python quac_visualizer.py --report-path /path/to/your/report.json +``` + +### Options + +- `--report-path`: Path to a specific report JSON file +- `--report-dir`: Path to directory containing report subdirectories +- `--host`: Host to bind to (default: 127.0.0.1) +- `--port`: Port to run on (default: 5000) +- `--debug`: Enable Flask debug mode + +## Testing with Dummy Data + +To test the functionality, you can use the provided test example: + +```bash +uv run web_app/quac_visualizer.py --report-path web_app/test_example/report.json +``` + +This will load a dummy report with sample images and masks to demonstrate all features. + +## Interface Guide + +### Main Components + +1. **Sidebar (Left Panel)**: + - **Report Info**: Basic statistics about the loaded report + - **Filters**: Source class, target class, and score range filters + - **QuAC Curve**: Aggregate curve visualization for filtered data + - **Downloads**: Export filtered results + +2. **Explanation List (Middle Panel)**: + - Scrollable list of explanations sorted by QuAC score + - Color-coded score badges (green=high, yellow=medium, red=low) + - Click to select an explanation for detailed viewing + +3. **Image Viewer (Right Panel)**: + - Toggle between Query and Counterfactual images + - Optional mask overlay with opacity-based visualization + - Individual QuAC curve for the selected explanation + - Explanation metadata display + +### Controls + +#### Image Navigation +- **Query/Counterfactual Toggle**: Radio buttons to switch between images +- **Mask Overlay**: Checkbox to show/hide importance mask +- **Keyboard Shortcuts**: + - `Q`: Show query image + - `C`: Show counterfactual image + - `M`: Toggle mask overlay + - `Escape`: Close image viewer + +#### Filtering +- **Source Class**: Filter by the original class of images +- **Target Class**: Filter by the target class for transformations +- **Score Ranges**: Use sliders to filter by QuAC score thresholds +- **Apply Filters**: Updates both the explanation list and QuAC curve + +#### Downloads +- **Download Filtered JSON**: Export current filtered explanations as JSON +- **Download Filtered Images**: Export images and masks as organized ZIP file + +### Mask Overlay Logic + +The mask overlay uses an opacity-based visualization: +- **Higher mask values (→ 1.0)**: Lower opacity overlay → **more visible** image regions +- **Lower mask values (→ 0.0)**: Higher opacity overlay → **more hidden** image regions + +This means bright/unmasked areas in the overlay indicate regions the model found **less important**, while transparent areas show **important regions**. + +### Understanding the Data + +#### Explanation Objects +Each explanation contains: +- **Images**: Query (original), counterfactual (transformed), and importance mask +- **Predictions**: Model confidence scores for each image +- **Classes**: Source and target class information +- **QuAC Score**: Overall quality metric (higher = better explanation) +- **Curve Data**: Mask size vs. score change for detailed analysis + +#### Report Structure +Reports aggregate multiple explanations and provide: +- **Filtering methods**: Select subsets by class or score +- **Curve analysis**: Statistical summaries across explanations +- **Metadata**: Information about the evaluation process + +## Expected Data Format + +### Report JSON Structure +```json +{ + "name": "report_name", + "metadata": {}, + "results": [ + { + "query_path": "path/to/query_image.jpg", + "counterfactual_path": "path/to/counterfactual_image.jpg", + "mask_path": "path/to/mask.npy", + "query_prediction": [0.1, 0.9, 0.0], + "counterfactual_prediction": [0.0, 0.1, 0.9], + "source_class": 1, + "target_class": 2, + "score": 0.85, + "normalized_mask_sizes": [0.0, 0.1, 0.2, ...], + "score_changes": [0.0, 0.1, 0.3, ...], + "method": "attribution_method_name" + } + ] +} +``` + +### File Organization +``` +your_data/ +├── report.json +├── images/ +│ ├── query_001.jpg +│ ├── counterfactual_001.jpg +│ └── ... +└── masks/ + ├── mask_001.npy + └── ... +``` + +## Troubleshooting + +### Common Issues + +1. **Images not loading**: Check that image paths in the report JSON are correct relative to the report file location + +2. **Mask overlay not showing**: Ensure mask files are valid NumPy arrays with values between 0-1 + +3. **QuAC curves not displaying**: Verify that `normalized_mask_sizes` and `score_changes` arrays are present and have matching lengths + +4. **Memory issues with large datasets**: Use the pagination (load more) feature for reports with many explanations + +### Performance Tips + +- For large reports, consider filtering to reduce the number of explanations loaded +- The web interface loads images on-demand to minimize memory usage +- Use the download functionality to work with subsets of data offline + +## Development + +The visualizer is built with: +- **Backend**: Flask with REST API endpoints +- **Frontend**: Vanilla JavaScript with Bootstrap 5 and Chart.js +- **Styling**: Color-blind friendly purple/green theme + +To modify or extend the visualizer, see the source files: +- `quac_visualizer.py`: Flask backend and API endpoints +- `templates/index.html`: Main interface template +- `static/css/style.css`: Styling and color theme +- `static/js/app.js`: JavaScript application logic \ No newline at end of file diff --git a/web_app/create_test_data.py b/web_app/create_test_data.py new file mode 100644 index 0000000..1c07528 --- /dev/null +++ b/web_app/create_test_data.py @@ -0,0 +1,210 @@ +#!/usr/bin/env python3 +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "numpy>=2.0", +# "pillow>=9.0.0", +# ] +# /// +""" +Create dummy test data for the QuAC Visualizer. +This generates sample images and masks to demonstrate functionality. +""" + +import json +import numpy as np +from PIL import Image, ImageDraw, ImageFont +from pathlib import Path + + +def create_test_images(): + """Create dummy test images and masks.""" + + # Create directories + test_dir = Path("test_example") + test_dir.mkdir(exist_ok=True) + (test_dir / "images").mkdir(exist_ok=True) + (test_dir / "masks").mkdir(exist_ok=True) + + # Image dimensions + img_size = (256, 256) + + # Sample classes + classes = ["dog", "cat", "bird"] + colors = { + "dog": (139, 69, 19), # Brown + "cat": (128, 128, 128), # Gray + "bird": (65, 105, 225), # Blue + } + + explanations = [] + + # Generate dummy explanations + for i in range(15): + source_class = classes[i % 3] + target_class = classes[(i + 1) % 3] + + # Create query image (simple colored rectangle with text) + query_img = Image.new("RGB", img_size, color=(240, 240, 240)) + draw = ImageDraw.Draw(query_img) + + # Draw main shape + shape_color = colors[source_class] + if i % 3 == 0: # Rectangle + draw.rectangle( + [50, 50, 200, 200], fill=shape_color, outline=(0, 0, 0), width=2 + ) + elif i % 3 == 1: # Circle + draw.ellipse( + [50, 50, 200, 200], fill=shape_color, outline=(0, 0, 0), width=2 + ) + else: # Triangle + draw.polygon( + [(125, 50), (50, 200), (200, 200)], + fill=shape_color, + outline=(0, 0, 0), + width=2, + ) + + # Add some distinguishing features + if i % 4 == 0: # Add dots + draw.ellipse([80, 80, 100, 100], fill=(255, 255, 255)) + draw.ellipse([150, 150, 170, 170], fill=(255, 255, 255)) + elif i % 4 == 1: # Add lines + draw.line([70, 70, 180, 180], fill=(255, 255, 255), width=3) + draw.line([70, 180, 180, 70], fill=(255, 255, 255), width=3) + + try: + # Try to add text (may fail if no font available) + font = ImageFont.load_default() + draw.text((10, 10), source_class, fill=(0, 0, 0), font=font) + except (OSError, ImportError): + # Fallback without font + draw.text((10, 10), source_class, fill=(0, 0, 0)) + + query_path = test_dir / "images" / f"query_{i:03d}.png" + query_img.save(query_path) + + # Create counterfactual image (similar but with target class color and slight modification) + cf_img = query_img.copy() + draw_cf = ImageDraw.Draw(cf_img) + + # Change main shape color + target_color = colors[target_class] + if i % 3 == 0: # Rectangle + draw_cf.rectangle( + [50, 50, 200, 200], fill=target_color, outline=(0, 0, 0), width=2 + ) + elif i % 3 == 1: # Circle + draw_cf.ellipse( + [50, 50, 200, 200], fill=target_color, outline=(0, 0, 0), width=2 + ) + else: # Triangle + draw_cf.polygon( + [(125, 50), (50, 200), (200, 200)], + fill=target_color, + outline=(0, 0, 0), + width=2, + ) + + # Add small difference (this is what the user should spot when toggling) + if i % 2 == 0: + # Add a small square in corner + draw_cf.rectangle([210, 210, 230, 230], fill=(255, 0, 0)) + else: + # Add a small circle + draw_cf.ellipse([20, 210, 40, 230], fill=(0, 255, 0)) + + try: + draw_cf.text((10, 230), target_class, fill=(0, 0, 0), font=font) + except (OSError, ImportError): + draw_cf.text((10, 230), target_class, fill=(0, 0, 0)) + + cf_path = test_dir / "images" / f"counterfactual_{i:03d}.png" + cf_img.save(cf_path) + + # Create mask (importance map) + # Higher values (closer to 1) where the difference is + mask = np.ones((256, 256), dtype=np.float32) * 0.2 # Base low importance + + if i % 2 == 0: + # High importance around the small red square + mask[200:240, 200:240] = 0.9 + else: + # High importance around the small green circle + mask[200:240, 10:50] = 0.9 + + # Medium importance around the main shape + mask[40:210, 40:210] = 0.6 + + # Add some noise + noise = np.random.normal(0, 0.1, mask.shape) + mask = np.clip(mask + noise, 0, 1) + + mask_path = test_dir / "masks" / f"mask_{i:03d}.npy" + np.save(mask_path, mask) + + # Generate QuAC curve data + mask_sizes = np.linspace(0, 1, 21) + # Score changes should decrease as mask size increases (more masking = lower score) + base_score = 0.8 + 0.2 * np.random.random() # Random base between 0.8-1.0 + score_changes = [] + for size in mask_sizes: + # Simulate decreasing performance as more is masked + change = base_score * (1 - size * 0.7) + np.random.normal(0, 0.05) + score_changes.append(max(0, change)) + + # Overall QuAC score (area under curve) + quac_score = np.trapz(score_changes, mask_sizes) + + # Create explanation entry + explanation = { + "query_path": f"images/query_{i:03d}.png", + "counterfactual_path": f"images/counterfactual_{i:03d}.png", + "mask_path": f"masks/mask_{i:03d}.npy", + "query_prediction": ( + [0.1, 0.1, 0.8] + if source_class == "dog" + else ([0.8, 0.1, 0.1] if source_class == "cat" else [0.1, 0.8, 0.1]) + ), + "counterfactual_prediction": ( + [0.1, 0.1, 0.8] + if target_class == "dog" + else ([0.8, 0.1, 0.1] if target_class == "cat" else [0.1, 0.8, 0.1]) + ), + "source_class": classes.index(source_class), + "target_class": classes.index(target_class), + "score": float(quac_score), + "normalized_mask_sizes": mask_sizes.tolist(), + "score_changes": score_changes, + "optimal_threshold": 0.5, + "method": ["gradcam", "integrated_gradients", "lime"][i % 3], + } + + explanations.append(explanation) + + # Create report JSON + report = { + "name": "test_report", + "metadata": { + "created_by": "test_data_generator", + "description": "Dummy data for QuAC Visualizer testing", + "num_classes": len(classes), + "class_names": classes, + }, + "results": explanations, + } + + report_path = test_dir / "report.json" + with open(report_path, "w") as f: + json.dump(report, f, indent=2) + + print(f"Created test data in {test_dir}/") + print(f"Generated {len(explanations)} explanations") + print(f"Report saved to: {report_path}") + print("\nTo test the visualizer, run:") + print(f"uv run web_app/quac_visualizer.py --report-path {report_path}") + + +if __name__ == "__main__": + create_test_images() diff --git a/web_app/quac_visualizer.py b/web_app/quac_visualizer.py new file mode 100644 index 0000000..1cd0277 --- /dev/null +++ b/web_app/quac_visualizer.py @@ -0,0 +1,675 @@ +#!/usr/bin/env python3 +""" +QuAC Visualizer - Web-based GUI for exploring Explanation and Report objects. + +Features: +- Toggle between query/counterfactual images to spot differences +- Mask overlay with opacity based on mask values (0-1) +- Filtering by source/target class, score thresholds +- Download functionality for results and images + +Usage: + # Use configuration file (reads report directory from config) + uv run web_app/quac_visualizer.py --config config.yaml + + # Use configuration file with report override + uv run web_app/quac_visualizer.py --config config.yaml --report /path/to/report.json + uv run web_app/quac_visualizer.py --config config.yaml --report /path/to/reports/ +""" + +import argparse +import json +import logging +import zipfile +import yaml +from io import BytesIO +from pathlib import Path +from typing import Dict, Optional, Union + +import numpy as np +import torch +from flask import Flask, jsonify, render_template, request, send_file +from PIL import Image + +from quac.explanation import Explanation, explanation_encoder +from quac.report import Report +from quac.config import ExperimentConfig, get_data_config +from quac.data import create_transform + +app = Flask(__name__) +app.config["MAX_CONTENT_LENGTH"] = 16 * 1024 * 1024 # 16MB max file size + +# Create module-level logger +logger = logging.getLogger(__name__) + +# Global variable to store the current report +current_report: Optional[Report] = None +report_base_path: Optional[Path] = None +blinding_enabled: bool = False +image_transform = None # Will store the transform for images + + +def load_report_from_path(path: Union[str, Path]) -> Report: + """Load a report from a file path or directory.""" + path = Path(path) + if path.is_file(): + report = Report() + report.load(path) + return report + elif path.is_dir(): + return Report.from_directory(str(path)) + else: + raise FileNotFoundError(f"Report path not found: {path}") + + +def find_explanation_by_id(exp_id: str) -> Optional[Explanation]: + """Find an explanation by its string ID.""" + if current_report is None: + return None + + for exp in current_report.explanations: + if str(hash(exp)) == exp_id: + return exp + return None + + +def tensor_to_image_response(tensor: torch.Tensor) -> bytes: + """Convert a CHW tensor to a PNG image response.""" + # Apply transform if available + if image_transform is not None: + tensor = image_transform(tensor) + + # Convert from CHW to HWC + if tensor.dim() == 3: + tensor = tensor.permute(1, 2, 0) + + # Convert to numpy and ensure values are in [0, 1] + img_array = tensor.cpu().numpy() + + # Normalize to [0, 1] if needed + if img_array.max() > 1.0 or img_array.min() < 0.0: + img_array = (img_array - img_array.min()) / (img_array.max() - img_array.min()) + + # Convert to [0, 255] uint8 + img_array = (img_array * 255).astype(np.uint8) + + # Handle grayscale (single channel) + if img_array.shape[2] == 1: + img_array = img_array.squeeze(2) + pil_image = Image.fromarray(img_array, mode="L") + else: + pil_image = Image.fromarray(img_array, mode="RGB") + + # Save to bytes buffer + buffer = BytesIO() + pil_image.save(buffer, format="PNG") + buffer.seek(0) + return buffer.getvalue() + + +def serialize_explanation(explanation: Explanation) -> Dict: + """Convert an Explanation object to a JSON-serializable dictionary.""" + # Use the standard explanation_encoder from the package + data = explanation_encoder(explanation) + + # Add web-app specific fields + data["id"] = str( + hash(explanation) + ) # Unique identifier as string to avoid JS precision loss + + # Add prediction confidence scores for web display + data["source_confidence"] = ( + max(explanation.query_prediction) if explanation.query_prediction else 0 + ) + data["target_confidence"] = ( + max(explanation.counterfactual_prediction) + if explanation.counterfactual_prediction + else 0 + ) + + return data + + +@app.route("/") +def index(): + """Serve the main dashboard page.""" + return render_template("index.html") + + +@app.route("/api/report/info") +def report_info(): + """Get basic information about the loaded report.""" + if current_report is None: + return jsonify({"error": "No report loaded"}), 400 + + # Get unique classes from explanations + source_classes = set() + target_classes = set() + for exp in current_report.explanations: + source_classes.add(exp.source_class) + target_classes.add(exp.target_class) + + return jsonify( + { + "name": current_report.name, + "metadata": current_report.metadata, + "num_explanations": len(current_report), + "source_classes": ( + sorted(list(source_classes)) if not blinding_enabled else [] + ), + "target_classes": ( + sorted(list(target_classes)) if not blinding_enabled else [] + ), + "score_range": { + "min": 0.0, # QuAC scores theoretically range from 0 to 1 + "max": 1.0, + "data_min": ( + min(exp.score for exp in current_report.explanations) + if len(current_report) > 0 + else 0 + ), + "data_max": ( + max(exp.score for exp in current_report.explanations) + if len(current_report) > 0 + else 1 + ), + }, + "blinding_enabled": blinding_enabled, + } + ) + + +@app.route("/api/explanations") +def get_explanations(): + """Get explanations with optional filtering.""" + if current_report is None: + return jsonify({"error": "No report loaded"}), 400 + + # Get query parameters with better handling + source_class_str = request.args.get("source_class") + target_class_str = request.args.get("target_class") + min_score = request.args.get("min_score", type=float) + max_score = request.args.get("max_score", type=float) + limit = request.args.get("limit", type=int) + offset = request.args.get("offset", default=0, type=int) + + # Convert class parameters properly + source_class = ( + None + if source_class_str is None or source_class_str == "" + else int(source_class_str) + ) + target_class = ( + None + if target_class_str is None or target_class_str == "" + else int(target_class_str) + ) + + logger.debug( + f"Filter params: source={source_class}, target={target_class}, min_score={min_score}, max_score={max_score}, offset={offset}, limit={limit}" + ) + + # Start with the full report + filtered_report = current_report + + # Apply filters + if source_class is not None: + logger.debug(f"Filtering by source_class: {source_class}") + filtered_report = filtered_report.from_source(source_class) + logger.debug( + f"After source filter: {len(filtered_report.explanations)} explanations" + ) + + if target_class is not None: + logger.debug(f"Filtering by target_class: {target_class}") + filtered_report = filtered_report.to_target(target_class) + logger.debug( + f"After target filter: {len(filtered_report.explanations)} explanations" + ) + + if min_score is not None: + logger.debug(f"Filtering by min_score: {min_score}") + filtered_report = filtered_report.score_threshold(min_score) + logger.debug( + f"After min_score filter: {len(filtered_report.explanations)} explanations" + ) + + # Apply max score filter (custom logic since Report doesn't have this built-in) + if max_score is not None: + logger.debug(f"Filtering by max_score: {max_score}") + explanations = [ + exp for exp in filtered_report.explanations if exp.score <= max_score + ] + new_report = Report(name=f"{filtered_report.name}_filtered") + new_report.explanations = explanations + filtered_report = new_report + logger.debug( + f"After max_score filter: {len(filtered_report.explanations)} explanations" + ) + + # Sort by score (highest first) + explanations = sorted( + filtered_report.explanations, key=lambda x: x.score, reverse=True + ) + + logger.debug(f"After filtering: {len(explanations)} explanations found") + + # Apply offset and limit + if limit is not None: + explanations = explanations[offset : offset + limit] + else: + explanations = explanations[offset:] + + # Serialize explanations + serialized = [serialize_explanation(exp) for exp in explanations] + + return jsonify( + { + "explanations": serialized, + "total": len(filtered_report.explanations), + "offset": offset, + "returned": len(serialized), + } + ) + + +@app.route("/api/explanation/") +def get_explanation_details(explanation_id: int): + """Get detailed information for a specific explanation.""" + if current_report is None: + return jsonify({"error": "No report loaded"}), 400 + + # Find explanation by hash ID + explanation = None + for exp in current_report.explanations: + if hash(exp) == explanation_id: + explanation = exp + break + + if explanation is None: + return jsonify({"error": "Explanation not found"}), 404 + + return jsonify(serialize_explanation(explanation)) + + +@app.route("/api/explanation//annotation", methods=["POST"]) +def save_annotation(exp_id: str): + """Save annotation for a specific explanation.""" + if current_report is None: + return jsonify({"error": "No report loaded"}), 400 + + try: + data = request.get_json() + annotation = data.get("annotation", "") + + # Find the explanation by ID + explanation = None + + print(f"Looking for explanation with ID: {exp_id}") + + for exp in current_report.explanations: + exp_hash = str(hash(exp)) + print(f"Checking explanation hash: {exp_hash}") + if exp_hash == exp_id: + explanation = exp + break + + if explanation is None: + print(f"No explanation found with ID {exp_id}") + return jsonify({"error": "Explanation not found"}), 404 + + # Update the annotation + explanation.annotation = annotation + + return jsonify({"success": True, "message": "Annotation saved"}) + + except Exception as e: + return jsonify({"error": f"Failed to save annotation: {str(e)}"}), 500 + + +@app.route("/api/curve") +def get_quac_curve(): + """Get QuAC curve data for the current report or filtered subset.""" + if current_report is None: + return jsonify({"error": "No report loaded"}), 400 + + # Apply same filtering as get_explanations for consistency + source_class = request.args.get("source_class", type=int) + target_class = request.args.get("target_class", type=int) + min_score = request.args.get("min_score", type=float) + max_score = request.args.get("max_score", type=float) + + filtered_report = current_report + + if source_class is not None: + filtered_report = filtered_report.from_source(source_class) + if target_class is not None: + filtered_report = filtered_report.to_target(target_class) + if min_score is not None: + filtered_report = filtered_report.score_threshold(min_score) + if max_score is not None: + explanations = [ + exp for exp in filtered_report.explanations if exp.score <= max_score + ] + new_report = Report(name=f"{filtered_report.name}_filtered") + new_report.explanations = explanations + filtered_report = new_report + + try: + if len(filtered_report.explanations) == 0: + return jsonify({"error": "No explanations match the current filters"}), 400 + + median, p25, p75 = filtered_report.get_curve() + return jsonify( + { + "x_values": filtered_report.interp_mask_values.tolist(), + "median": median.tolist(), + "p25": p25.tolist(), + "p75": p75.tolist(), + "num_samples": len(filtered_report.explanations), + } + ) + except Exception as e: + return jsonify({"error": f"Could not generate curve: {str(e)}"}), 500 + + +@app.route("/api/image//") +def serve_image(exp_id: str, image_type: str): + """Serve image data from explanation object.""" + try: + # Find the explanation + explanation = find_explanation_by_id(exp_id) + if explanation is None: + return jsonify({"error": "Explanation not found"}), 404 + + # Get the appropriate tensor + if image_type == "query": + tensor = explanation.query + elif image_type == "counterfactual": + tensor = explanation.counterfactual + else: + return jsonify({"error": f"Invalid image type: {image_type}"}), 400 + + # Convert tensor to image + image_data = tensor_to_image_response(tensor) + + # Create response + buffer = BytesIO(image_data) + buffer.seek(0) + + return send_file(buffer, mimetype="image/png") + + except Exception as e: + return jsonify({"error": f"Error serving image: {str(e)}"}), 500 + + +@app.route("/api/mask/") +def serve_mask(exp_id: str): + """Serve mask data from explanation object as JSON array.""" + try: + # Find the explanation + explanation = find_explanation_by_id(exp_id) + if explanation is None: + return jsonify({"error": "Explanation not found"}), 404 + + # Get mask tensor + mask_tensor = explanation.mask + + # Convert to numpy + mask_data = mask_tensor.cpu().numpy() + + # Normalize to channels-last format (height, width, channels) + if mask_data.ndim == 3: + # If channels-first (channels, height, width), transpose to channels-last + if mask_data.shape[0] <= 3 and mask_data.shape[0] < mask_data.shape[1]: + mask_data = np.transpose(mask_data, (1, 2, 0)) + # Squeeze singleton channel dimensions + if mask_data.shape[2] == 1: + mask_data = mask_data.squeeze(2) + elif mask_data.ndim > 3: + # Squeeze extra dimensions + mask_data = mask_data.squeeze() + if ( + mask_data.ndim == 3 + and mask_data.shape[0] <= 3 + and mask_data.shape[0] < mask_data.shape[1] + ): + mask_data = np.transpose(mask_data, (1, 2, 0)) + + # Normalize values to 0-255 range for image display + if mask_data.max() > mask_data.min(): + mask_data = ( + (mask_data - mask_data.min()) + / (mask_data.max() - mask_data.min()) + * 255 + ).astype(np.uint8) + else: + mask_data = np.zeros_like(mask_data, dtype=np.uint8) + + # Ensure mask is always 3-channel RGB + if mask_data.ndim == 2: + # Convert grayscale to RGB by putting mask in red channel, zeros in green/blue + height, width = mask_data.shape + rgb_mask = np.zeros((height, width, 3), dtype=np.uint8) + rgb_mask[:, :, 0] = mask_data # Red channel gets the mask + # Green and blue channels remain 0 + mask_data = rgb_mask + + return jsonify( + { + "mask": mask_data.tolist(), + "shape": list(mask_data.shape), + } + ) + + except Exception as e: + return jsonify({"error": f"Error serving mask: {str(e)}"}), 500 + + +@app.route("/api/download/explanations") +def download_explanations(): + """Download filtered explanations as JSON.""" + if current_report is None: + return jsonify({"error": "No report loaded"}), 400 + + # Apply same filtering as get_explanations + source_class = request.args.get("source_class", type=int) + target_class = request.args.get("target_class", type=int) + min_score = request.args.get("min_score", type=float) + max_score = request.args.get("max_score", type=float) + + filtered_report = current_report + + if source_class is not None: + filtered_report = filtered_report.from_source(source_class) + if target_class is not None: + filtered_report = filtered_report.to_target(target_class) + if min_score is not None: + filtered_report = filtered_report.score_threshold(min_score) + if max_score is not None: + explanations = [ + exp for exp in filtered_report.explanations if exp.score <= max_score + ] + new_report = Report(name=f"{filtered_report.name}_filtered") + new_report.explanations = explanations + filtered_report = new_report + + # Create JSON data with filters added to metadata + metadata_with_filters = ( + dict(filtered_report.metadata) if filtered_report.metadata else {} + ) + metadata_with_filters["filters"] = { + "source_class": source_class, + "target_class": target_class, + "min_score": min_score, + "max_score": max_score, + } + + data = { + "report_name": filtered_report.name, + "metadata": metadata_with_filters, + "explanations": [ + serialize_explanation(exp) for exp in filtered_report.explanations + ], + } + + # Create in-memory file + json_data = json.dumps(data, indent=2) + buffer = BytesIO(json_data.encode("utf-8")) + buffer.seek(0) + + return send_file( + buffer, + mimetype="application/json", + as_attachment=True, + download_name=f"{filtered_report.name}_filtered_explanations.json", + ) + + +@app.route("/api/download/images") +def download_images(): + """Download images for filtered explanations as a ZIP file.""" + if current_report is None: + return jsonify({"error": "No report loaded"}), 400 + + # Apply same filtering as get_explanations + source_class = request.args.get("source_class", type=int) + target_class = request.args.get("target_class", type=int) + min_score = request.args.get("min_score", type=float) + max_score = request.args.get("max_score", type=float) + + filtered_report = current_report + + if source_class is not None: + filtered_report = filtered_report.from_source(source_class) + if target_class is not None: + filtered_report = filtered_report.to_target(target_class) + if min_score is not None: + filtered_report = filtered_report.score_threshold(min_score) + if max_score is not None: + explanations = [ + exp for exp in filtered_report.explanations if exp.score <= max_score + ] + new_report = Report(name=f"{filtered_report.name}_filtered") + new_report.explanations = explanations + filtered_report = new_report + + # Create ZIP file in memory + zip_buffer = BytesIO() + + try: + with zipfile.ZipFile(zip_buffer, "w", zipfile.ZIP_DEFLATED) as zip_file: + for i, exp in enumerate(filtered_report.explanations): + score_str = f"score_{exp.score:.4f}" + folder_name = f"explanation_{i:04d}_{score_str}_{exp.source_class}to{exp.target_class}" + + # Add query image + if exp._query_path and Path(exp._query_path).exists(): + zip_file.write( + exp._query_path, + f"{folder_name}/query.{Path(exp._query_path).suffix}", + ) + + # Add counterfactual image + if exp._counterfactual_path and Path(exp._counterfactual_path).exists(): + zip_file.write( + exp._counterfactual_path, + f"{folder_name}/counterfactual.{Path(exp._counterfactual_path).suffix}", + ) + + # Add mask + if exp._mask_path and Path(exp._mask_path).exists(): + zip_file.write(exp._mask_path, f"{folder_name}/mask.npy") + + # Add explanation metadata + metadata = serialize_explanation(exp) + metadata_json = json.dumps(metadata, indent=2) + zip_file.writestr(f"{folder_name}/metadata.json", metadata_json) + + zip_buffer.seek(0) + + return send_file( + zip_buffer, + mimetype="application/zip", + as_attachment=True, + download_name=f"{filtered_report.name}_filtered_images.zip", + ) + + except Exception as e: + return jsonify({"error": f"Error creating ZIP file: {str(e)}"}), 500 + + +def main(): + parser = argparse.ArgumentParser(description="QuAC Visualizer") + parser.add_argument( + "--config", "-c", type=str, required=True, help="Path to configuration file" + ) + parser.add_argument( + "--report", type=str, help="Override report path (file or directory)" + ) + parser.add_argument( + "--port", type=int, default=5000, help="Port to run the server on" + ) + parser.add_argument( + "--host", type=str, default="127.0.0.1", help="Host to run the server on" + ) + parser.add_argument("--debug", action="store_true", help="Run in debug mode") + parser.add_argument( + "--blind", + action="store_true", + help="Enable blinding mode (hide class information)", + ) + + args = parser.parse_args() + + # Configure logging + log_level = logging.DEBUG if args.debug else logging.INFO + logging.basicConfig( + level=log_level, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + datefmt="%H:%M:%S", + ) + + # Load configuration + with open(args.config, "r") as file: + config_data = yaml.safe_load(file) + experiment_config = ExperimentConfig(**config_data) + logger.info(f"Loaded configuration from: {args.config}") + + # Create image transform from data config + data_config = get_data_config(experiment_config, "test") + global image_transform + image_transform = create_transform( + img_size=data_config.img_size, + grayscale=data_config.grayscale, + rgb=data_config.rgb, + ) + logger.info( + f"Created image transform: img_size={data_config.img_size}, grayscale={data_config.grayscale}, rgb={data_config.rgb}" + ) + + # Set report path from config, then override if provided + report_path = str(Path(experiment_config.solver.root_dir) / "reports") + if args.report: + report_path = args.report + logger.info(f"Using report: {report_path}") + + global current_report, report_base_path, blinding_enabled + blinding_enabled = args.blind + + current_report = load_report_from_path(report_path) + report_base_path = Path(report_path).resolve() + if report_base_path.is_file(): + report_base_path = report_base_path.parent + + logger.info(f"Loaded report: {current_report.name}") + logger.info(f"Number of explanations: {len(current_report)}") + logger.info(f"Report base path: {report_base_path}") + logger.info(f"Blinding mode: {'Enabled' if blinding_enabled else 'Disabled'}") + + logger.info(f"Starting QuAC Visualizer on http://{args.host}:{args.port}") + app.run(host=args.host, port=args.port, debug=args.debug) + + +if __name__ == "__main__": + main() diff --git a/web_app/static/css/style.css b/web_app/static/css/style.css new file mode 100644 index 0000000..fb8d827 --- /dev/null +++ b/web_app/static/css/style.css @@ -0,0 +1,461 @@ +/* QuAC Visualizer Styles - Color-blind friendly with green/purple theme */ + +:root { + --primary-color: #6c5ce7; /* Purple */ + --secondary-color: #00b894; /* Teal green */ + --accent-color: #a29bfe; /* Light purple */ + --success-color: #00b894; /* Teal green */ + --warning-color: #fdcb6e; /* Yellow-orange */ + --danger-color: #e17055; /* Coral red */ + --light-bg: #f8f9fa; + --border-color: #e9ecef; + --text-muted: #6c757d; +} + +body { + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; + font-size: 14px; + overflow: hidden; /* Prevent page-level scrolling */ + margin: 0; + padding: 0; +} + +/* Ensure full height layout */ +.vh-100 { + height: 100vh !important; + max-height: 100vh !important; +} + +/* Remove default Bootstrap margins that cause overflow */ +.container-fluid.vh-100 { + padding: 0 !important; + margin: 0 !important; +} + +.row.h-100 { + margin: 0 !important; +} + +/* Sidebar styling for better space usage */ +.col-md-3.bg-light { + padding: 0.75rem !important; +} + +/* Main content area styling */ +.col-md-9.h-100 { + padding: 0.75rem !important; +} + +.col-md-4.d-flex { + padding: 0.5rem !important; +} + +.col-md-8.h-100 { + padding: 0.5rem !important; +} + +/* Chart container sizing */ +#quac-curve { + max-height: 150px; +} + +/* Compact sidebar styling */ +.col-md-3 h6 { + font-size: 0.9rem; + font-weight: 600; + margin-bottom: 0.5rem; +} + +.col-md-3 .small { + font-size: 0.8rem; +} + +/* Compact form elements */ +.form-label.small { + font-size: 0.8rem; + margin-bottom: 0.25rem; +} + +/* Tighter spacing for range inputs */ +.form-range { + margin-bottom: 0.25rem; +} + +.list-group-item { + cursor: pointer; + border-left: 3px solid transparent; + transition: all 0.2s ease; +} + +.list-group-item:hover { + background-color: var(--light-bg); + border-left-color: var(--secondary-color); +} + +.list-group-item.active { + background-color: rgba(108, 92, 231, 0.1); /* Light purple background */ + border-left-color: var(--primary-color); + border-color: var(--primary-color); +} + +.explanation-item { + padding: 12px 16px; +} + +.explanation-header { + font-weight: 600; + margin-bottom: 4px; +} + +.explanation-details { + font-size: 12px; + color: var(--text-muted); + margin-bottom: 6px; +} + +.explanation-score { + font-size: 11px; +} + +.score-badge { + background: var(--success-color); + color: white; + padding: 2px 6px; + border-radius: 12px; + font-weight: 600; +} + +.score-badge.medium-score { + background: var(--warning-color); + color: #333; +} + +.score-badge.low-score { + background: var(--danger-color); + color: white; +} + +/* Image viewer styles */ +#image-container { + max-width: 100%; + box-shadow: 0 4px 8px rgba(0,0,0,0.1); + border-radius: 4px; + overflow: hidden; +} + +#main-image { + display: block; + width: 100%; + height: auto; +} + +#mask-overlay { + position: absolute; + top: 0; + left: 0; + pointer-events: none; +} + +/* Toggle buttons - using purple theme */ +.btn-outline-primary { + color: var(--primary-color); + border-color: var(--primary-color); +} + +.btn-outline-primary:hover { + background-color: var(--primary-color); + border-color: var(--primary-color); +} + +.btn-primary { + background-color: var(--primary-color); + border-color: var(--primary-color); +} + +.btn-primary:hover { + background-color: var(--accent-color); + border-color: var(--accent-color); +} + +.btn-group .btn-check:checked + .btn { + background-color: var(--primary-color); + border-color: var(--primary-color); + color: white; +} + +/* Secondary buttons with green theme */ +.btn-outline-secondary { + color: var(--secondary-color); + border-color: var(--secondary-color); +} + +.btn-outline-secondary:hover { + background-color: var(--secondary-color); + border-color: var(--secondary-color); + color: white; +} + +/* Filter range inputs */ +.form-range::-webkit-slider-thumb { + background: var(--primary-color); +} + +.form-range::-moz-range-thumb { + background: var(--primary-color); + border: none; +} + +.form-range::-webkit-slider-track { + background: var(--border-color); +} + +/* Chart containers */ +canvas { + border: 1px solid var(--border-color); + border-radius: 4px; + background: white; +} + +/* Loading states */ +.loading { + opacity: 0.6; + pointer-events: none; +} + +.spinner-border-sm { + width: 1rem; + height: 1rem; +} + +.spinner-border { + color: var(--primary-color); +} + +/* Badge colors - color-blind friendly */ +.badge.bg-primary { + background-color: var(--primary-color) !important; +} + +.badge.bg-secondary { + background-color: var(--secondary-color) !important; +} + +.badge.bg-success { + background-color: var(--success-color) !important; +} + +/* Responsive adjustments */ +@media (max-width: 768px) { + .container-fluid { + padding: 0; + } + + .col-md-3 { + position: fixed; + top: 0; + left: -300px; + width: 300px; + height: 100vh; + z-index: 1000; + transition: left 0.3s ease; + background: white; + box-shadow: 2px 0 5px rgba(0,0,0,0.1); + } + + .col-md-3.show { + left: 0; + } + + .col-md-9 { + margin-left: 0; + width: 100%; + } +} + +/* Utility classes */ +.text-truncate { + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.cursor-pointer { + cursor: pointer; +} + +/* QuAC curve styling */ +.curve-container { + position: relative; + height: 200px; +} + +/* Image transition effects */ +#main-image { + transition: opacity 0.2s ease-in-out; +} + +#main-image.loading { + opacity: 0.5; +} + +/* Mask overlay styling */ +#mask-overlay { + opacity: 0.7; + mix-blend-mode: multiply; +} + +/* Score visualization - color-blind friendly gradient */ +.score-bar { + height: 4px; + background: var(--border-color); + border-radius: 2px; + overflow: hidden; +} + +.score-fill { + height: 100%; + /* Color-blind friendly gradient from coral to yellow-orange to teal */ + background: linear-gradient(90deg, var(--danger-color), var(--warning-color), var(--success-color)); + transition: width 0.3s ease; +} + +/* Prediction confidence */ +.prediction-confidence { + font-size: 11px; + color: var(--text-muted); +} + +.confidence-high { + color: var(--success-color); + font-weight: 600; +} + +.confidence-medium { + color: var(--warning-color); + font-weight: 500; +} + +.confidence-low { + color: var(--danger-color); + font-weight: 400; +} + +/* Form controls with theme colors */ +.form-select:focus, +.form-control:focus { + border-color: var(--primary-color); + box-shadow: 0 0 0 0.2rem rgba(108, 92, 231, 0.25); +} + +.form-check-input:checked { + background-color: var(--primary-color); + border-color: var(--primary-color); +} + +/* Card styling */ +.card { + border-color: var(--border-color); +} + +.card-body { + background-color: rgba(255, 255, 255, 0.8); +} + +/* Additional color-blind friendly patterns */ +.pattern-dots { + background-image: radial-gradient(circle, var(--primary-color) 1px, transparent 1px); + background-size: 8px 8px; +} + +.pattern-stripes { + background-image: repeating-linear-gradient( + 45deg, + transparent, + transparent 2px, + var(--secondary-color) 2px, + var(--secondary-color) 4px + ); +} + +/* Sidebar transitions */ +#sidebar { + transition: width 0.3s ease-in-out, padding 0.3s ease-in-out; + overflow: hidden; +} + +#sidebar-divider { + transition: opacity 0.3s ease-in-out; +} + +/* Collapsed sidebar indicator */ +#sidebar-collapsed { + transition: opacity 0.3s ease-in-out; + border-right: 1px solid #dee2e6; +} + +#sidebar-collapsed:hover { + background-color: #e9ecef !important; +} + +#sidebar-toggle { + transition: left 0.3s ease-in-out; +} + +#sidebar-toggle.sidebar-hidden { + left: 10px !important; +} + +#toggle-icon { + font-weight: bold; + font-size: 16px; + transition: transform 0.3s ease-in-out; +} + +/* Resizable panes */ +.resizer { + position: relative; + transition: background-color 0.2s ease; +} + +.resizer:hover { + background-color: var(--primary-color) !important; +} + +.resizer:active { + background-color: var(--primary-color) !important; +} + +/* Prevent text selection during resize */ +.resizing { + user-select: none; +} + +.resizing * { + pointer-events: none; +} + +/* Custom Dual Range Slider Styles */ +.dual-range-container { + user-select: none; +} + +.dual-thumb { + transition: background-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, transform 0.15s ease-in-out; + box-shadow: 0 1px 3px rgba(0, 0, 0, 0.2); +} + +.dual-thumb:hover { + background-color: var(--accent-color) !important; + transform: scale(1.1); +} + +.dual-thumb:active { + background-color: var(--accent-color) !important; + box-shadow: 0 0 0 0.25rem rgba(108, 92, 231, 0.25); +} + +.dual-thumb.dragging { + background-color: var(--accent-color) !important; + box-shadow: 0 0 0 0.25rem rgba(108, 92, 231, 0.25); + transform: scale(1.1); +} \ No newline at end of file diff --git a/web_app/static/js/app.js b/web_app/static/js/app.js new file mode 100644 index 0000000..4d9bd99 --- /dev/null +++ b/web_app/static/js/app.js @@ -0,0 +1,1305 @@ +/** + * QuAC Visualizer JavaScript Application + * Handles image toggling, mask overlays, filtering, and API interactions + */ + +class ReportInfoPane { + constructor() { + this.reportInfo = null; + this.mainChart = null; + } + + async loadReportInfo() { + try { + const response = await fetch('/api/report/info'); + this.reportInfo = await response.json(); + this.updateReportInfoDisplay(); + return this.reportInfo; + } catch (error) { + console.error('Error loading report info:', error); + return null; + } + } + + updateReportInfoDisplay() { + const reportDetails = document.getElementById('report-details'); + if (this.reportInfo.error) { + reportDetails.innerHTML = `
${this.reportInfo.error}
`; + return; + } + + let displayHtml = ` +
Name: ${this.reportInfo.name}
+
Total Explanations: ${this.reportInfo.num_explanations}
+
Data Score Range: ${this.reportInfo.score_range.data_min.toFixed(3)} - ${this.reportInfo.score_range.data_max.toFixed(3)}
+ `; + + if (this.reportInfo.blinding_enabled) { + displayHtml += `
⚠️ Blinding Mode Active - Class information hidden
`; + } + + reportDetails.innerHTML = displayHtml; + } + + async loadMainChart() { + try { + const response = await fetch('/api/curve'); + const data = await response.json(); + + if (data.error) { + console.error('Error loading main chart:', data.error); + return; + } + + this.renderMainChart(data); + } catch (error) { + console.error('Error loading main chart:', error); + } + } + + renderMainChart(data) { + const ctx = document.getElementById('quac-curve').getContext('2d'); + + if (this.mainChart) { + this.mainChart.destroy(); + } + + this.mainChart = new Chart(ctx, { + type: 'line', + data: { + labels: data.x_values, + datasets: [ + { + label: 'Median', + data: data.median, + borderColor: 'rgb(108, 92, 231)', + backgroundColor: 'rgba(108, 92, 231, 0.1)', + borderWidth: 2, + fill: false + }, + { + label: '25th Percentile', + data: data.p25, + borderColor: 'rgba(108, 92, 231, 0.3)', + backgroundColor: 'rgba(108, 92, 231, 0.05)', + borderWidth: 1, + fill: '+1' + }, + { + label: '75th Percentile', + data: data.p75, + borderColor: 'rgba(108, 92, 231, 0.3)', + backgroundColor: 'rgba(108, 92, 231, 0.05)', + borderWidth: 1, + fill: false + } + ] + }, + options: { + responsive: true, + maintainAspectRatio: false, + scales: { + x: { + display: true, + title: { + display: true, + text: 'Mask Size', + font: { size: 10 } + }, + ticks: { font: { size: 8 } } + }, + y: { + display: true, + title: { + display: true, + text: 'Score Change', + font: { size: 10 } + }, + ticks: { font: { size: 8 } } + } + }, + plugins: { + legend: { + labels: { font: { size: 10 } } + } + } + } + }); + } +} + +class FilterPane { + constructor() { + this.onFilterChangeCallback = null; + } + + populateFilterOptions(reportInfo) { + const sourceSelect = document.getElementById('source-class'); + const targetSelect = document.getElementById('target-class'); + + if (reportInfo.blinding_enabled) { + // In blinding mode, disable class filter dropdowns + sourceSelect.disabled = true; + targetSelect.disabled = true; + + // Add a placeholder option explaining blinding mode + const sourceOption = document.createElement('option'); + sourceOption.value = ''; + sourceOption.textContent = 'Hidden (Blinding Mode)'; + sourceSelect.appendChild(sourceOption); + + const targetOption = document.createElement('option'); + targetOption.value = ''; + targetOption.textContent = 'Hidden (Blinding Mode)'; + targetSelect.appendChild(targetOption); + } else { + // Normal mode - populate with actual classes + // Populate source classes + reportInfo.source_classes.forEach(cls => { + const option = document.createElement('option'); + option.value = cls; + option.textContent = cls; + sourceSelect.appendChild(option); + }); + + // Populate target classes + reportInfo.target_classes.forEach(cls => { + const option = document.createElement('option'); + option.value = cls; + option.textContent = cls; + targetSelect.appendChild(option); + }); + } + } + + setupDualRangeSlider() { + const container = document.querySelector('.dual-range-container'); + const minThumb = document.getElementById('min-thumb'); + const maxThumb = document.getElementById('max-thumb'); + const range = document.getElementById('dual-range'); + const minInput = document.getElementById('min-score'); + const maxInput = document.getElementById('max-score'); + const minValue = document.getElementById('min-score-value'); + const maxValue = document.getElementById('max-score-value'); + + let isDragging = false; + let activeThumb = null; + + const updateDisplay = () => { + const minVal = parseFloat(minInput.value); + const maxVal = parseFloat(maxInput.value); + + // Ensure min <= max + if (minVal > maxVal) { + if (activeThumb === minThumb) { + maxInput.value = minVal; + } else { + minInput.value = maxVal; + } + } + + const finalMin = parseFloat(minInput.value); + const finalMax = parseFloat(maxInput.value); + + // Update visual display + minValue.textContent = finalMin.toFixed(3); + maxValue.textContent = finalMax.toFixed(3); + + // Update thumb positions (0-100%) + const minPercent = finalMin * 100; + const maxPercent = finalMax * 100; + + minThumb.style.left = `${minPercent}%`; + maxThumb.style.left = `${maxPercent}%`; + + // Update range bar + range.style.left = `${minPercent}%`; + range.style.width = `${maxPercent - minPercent}%`; + }; + + const startDrag = (thumb, event) => { + isDragging = true; + activeThumb = thumb; + document.body.style.userSelect = 'none'; + event.preventDefault(); + }; + + const handleDrag = (event) => { + if (!isDragging || !activeThumb) return; + + const rect = container.getBoundingClientRect(); + const clientX = event.touches ? event.touches[0].clientX : event.clientX; + const x = clientX - rect.left; + const percent = Math.max(0, Math.min(100, (x / rect.width) * 100)); + const value = percent / 100; + + if (activeThumb === minThumb) { + minInput.value = value.toFixed(3); + } else { + maxInput.value = value.toFixed(3); + } + + updateDisplay(); + }; + + const endDrag = () => { + if (isDragging) { + isDragging = false; + activeThumb = null; + document.body.style.userSelect = ''; + } + }; + + // Mouse events + minThumb.addEventListener('mousedown', (e) => startDrag(minThumb, e)); + maxThumb.addEventListener('mousedown', (e) => startDrag(maxThumb, e)); + + document.addEventListener('mousemove', handleDrag); + document.addEventListener('mouseup', endDrag); + + // Touch events + minThumb.addEventListener('touchstart', (e) => startDrag(minThumb, e.touches[0])); + maxThumb.addEventListener('touchstart', (e) => startDrag(maxThumb, e.touches[0])); + + document.addEventListener('touchmove', (e) => { + e.preventDefault(); + handleDrag(e.touches[0]); + }); + document.addEventListener('touchend', endDrag); + + // Initial display update + updateDisplay(); + } + + setupFilterEventListeners(applyFiltersCallback, resetFiltersCallback) { + // Filter form + document.getElementById('filter-form').addEventListener('submit', (e) => { + e.preventDefault(); + applyFiltersCallback(); + }); + + // Reset filters button + document.getElementById('reset-filters').addEventListener('click', () => { + resetFiltersCallback(); + }); + } + + resetFilters() { + document.getElementById('source-class').value = ''; + document.getElementById('target-class').value = ''; + + // Reset dual range slider to full range + const minSlider = document.getElementById('min-score'); + const maxSlider = document.getElementById('max-score'); + minSlider.value = 0; + maxSlider.value = 1; + document.getElementById('min-score-value').textContent = '0.000'; + document.getElementById('max-score-value').textContent = '1.000'; + + // Update slider visual + const minThumb = document.getElementById('min-thumb'); + const maxThumb = document.getElementById('max-thumb'); + const range = document.getElementById('dual-range'); + + minThumb.style.left = '0%'; + maxThumb.style.left = '100%'; + range.style.left = '0%'; + range.style.width = '100%'; + } + + getFilterValues() { + const formData = new FormData(document.getElementById('filter-form')); + const filters = {}; + for (let [key, value] of formData.entries()) { + if (value) filters[key] = value; + } + return filters; + } + + onFilterChange(callback) { + this.onFilterChangeCallback = callback; + } +} + +class ExplanationListPane { + constructor() { + this.currentExplanations = []; + this.totalExplanations = 0; + this.offset = 0; + this.limit = 20; + this.onExplanationSelectCallback = null; + } + + async loadExplanations(reportInfo, resetOffset = false) { + try { + if (resetOffset) { + this.offset = 0; + } + + const formData = new FormData(document.getElementById('filter-form')); + const params = new URLSearchParams(); + + // Add form parameters with explicit handling + const sourceClass = formData.get('source_class'); + const targetClass = formData.get('target_class'); + const minScore = formData.get('min_score'); + const maxScore = formData.get('max_score'); + + // Only add non-empty parameters + if (sourceClass && sourceClass !== '') { + params.append('source_class', sourceClass); + } + if (targetClass && targetClass !== '') { + params.append('target_class', targetClass); + } + if (minScore && minScore !== '') { + params.append('min_score', minScore); + } + if (maxScore && maxScore !== '') { + params.append('max_score', maxScore); + } + + params.append('offset', this.offset); + params.append('limit', this.limit); + + console.log('Loading explanations with params:', params.toString()); // Debug log + + const response = await fetch(`/api/explanations?${params}`); + const data = await response.json(); + + if (data.error) { + console.error('Error loading explanations:', data.error); + return; + } + + if (this.offset === 0) { + this.currentExplanations = data.explanations; + } else { + this.currentExplanations.push(...data.explanations); + } + + this.totalExplanations = data.total; + this.updateExplanationsList(reportInfo); + this.updateLoadMoreButton(); + } catch (error) { + console.error('Error loading explanations:', error); + } + } + + async loadMoreExplanations(reportInfo) { + this.offset += this.limit; + await this.loadExplanations(reportInfo, false); + } + + updateExplanationsList(reportInfo) { + const listContainer = document.getElementById('explanation-list'); + + if (this.offset === 0) { + listContainer.innerHTML = ''; + } + + this.currentExplanations.slice(this.offset === 0 ? 0 : -this.limit).forEach(exp => { + const item = this.createExplanationListItem(exp, reportInfo); + listContainer.appendChild(item); + }); + + document.getElementById('explanation-count').textContent = this.totalExplanations; + } + + createExplanationListItem(explanation, reportInfo) { + const item = document.createElement('div'); + item.className = 'list-group-item explanation-item'; + item.dataset.explanationId = explanation.id; + + const scoreClass = this.getScoreClass(explanation.score); + + // Hide class information if blinding is enabled + const classDisplay = reportInfo.blinding_enabled + ? 'Hidden → Hidden' + : `${explanation.source_class} → ${explanation.target_class}`; + + item.innerHTML = ` +
+ ${classDisplay} +
+
+ Method: ${explanation.method || 'N/A'} +
+
+ ${explanation.score.toFixed(4)} +
+
+
+
+ `; + + item.addEventListener('click', () => { + this.selectExplanation(explanation); + }); + + return item; + } + + getScoreClass(score) { + if (score >= 0.6) return ''; + if (score >= 0.3) return 'medium-score'; + return 'low-score'; + } + + updateLoadMoreButton() { + const container = document.getElementById('load-more-container'); + const button = document.getElementById('load-more'); + + if (this.currentExplanations.length >= this.totalExplanations) { + container.style.display = 'none'; + } else { + container.style.display = 'block'; + button.textContent = `Load More (${this.currentExplanations.length}/${this.totalExplanations})`; + } + } + + selectExplanation(explanation) { + // Update visual selection efficiently + const listContainer = document.getElementById('explanation-list'); + const currentActive = listContainer.querySelector('.explanation-item.active'); + if (currentActive) { + currentActive.classList.remove('active'); + } + + const newActive = listContainer.querySelector(`[data-explanation-id="${explanation.id}"]`); + if (newActive) { + newActive.classList.add('active'); + } + + // Call the callback to notify the main class + if (this.onExplanationSelectCallback) { + this.onExplanationSelectCallback(explanation); + } + } + + setupEventListeners(onLoadMoreCallback) { + // Load more button + document.getElementById('load-more').addEventListener('click', () => { + onLoadMoreCallback(); + }); + } + + setExplanationSelectCallback(callback) { + this.onExplanationSelectCallback = callback; + } +} + +class ExplanationViewerPane { + constructor() { + this.currentExplanation = null; + this.currentMask = null; + } + + async loadMaskData() { + if (!this.currentExplanation) return; + + try { + const response = await fetch(`/api/mask/${this.currentExplanation.id}`); + const responseText = await response.text(); + + let data; + try { + data = JSON.parse(responseText); + } catch (jsonError) { + console.error('JSON parse error:', jsonError); + return; + } + + if (data.error) { + console.error('Error loading mask:', data.error); + return; + } + + this.currentMask = data; + } catch (error) { + console.error('Error loading mask data:', error); + } + } + + async showImage(imageType) { + if (!this.currentExplanation) return; + + const mainImage = document.getElementById('main-image'); + const maskOverlay = document.getElementById('mask-overlay'); + + mainImage.classList.add('loading'); + + try { + // Use new ID-based endpoint + mainImage.src = `/api/image/${this.currentExplanation.id}/${imageType}`; + + await new Promise((resolve, reject) => { + mainImage.onload = () => { + mainImage.classList.remove('loading'); + + // Update canvas size to match image + maskOverlay.width = mainImage.naturalWidth; + maskOverlay.height = mainImage.naturalHeight; + maskOverlay.style.width = mainImage.clientWidth + 'px'; + maskOverlay.style.height = mainImage.clientHeight + 'px'; + + // Update mask overlay with current opacity + const opacity = parseInt(document.getElementById('mask-opacity').value); + this.updateMaskOpacity(opacity); + + resolve(); + }; + mainImage.onerror = reject; + }); + + } catch (error) { + console.error('Error loading image:', error); + mainImage.classList.remove('loading'); + } + } + + updateMaskOpacity(opacity) { + const maskOverlay = document.getElementById('mask-overlay'); + + if (!this.currentMask) { + maskOverlay.style.display = 'none'; + } else { + maskOverlay.style.display = 'block'; + // Always draw the spotlight effect first + this.drawMaskOverlay(opacity); + // Then draw the contour on top (independent of opacity) + this.drawMaskContour(); + } + } + + drawMaskOverlay(opacity = 30) { + if (!this.currentMask) { + console.log('No current mask data'); + return; + } + + const canvas = document.getElementById('mask-overlay'); + const ctx = canvas.getContext('2d'); + const maskData = this.currentMask.mask; + + if (!maskData || !Array.isArray(maskData) || !maskData.length) { + console.error('Invalid mask data format'); + return; + } + + // Clear canvas + ctx.clearRect(0, 0, canvas.width, canvas.height); + + const height = maskData.length; + const width = maskData[0]?.length; + + if (!width) { + console.error('Invalid mask dimensions'); + return; + } + + // Create ImageData for the mask (server returns RGB) + const imageData = ctx.createImageData(width, height); + const data = imageData.data; + + // Convert opacity percentage to alpha value (0-255) + const alpha = Math.round((opacity / 100) * 255); + + // First pass: create base overlay + for (let y = 0; y < height; y++) { + for (let x = 0; x < width; x++) { + const index = (y * width + x) * 4; + const pixel = maskData[y][x]; + + // Check if any channel has non-zero value (works for any color mask) + const hasMask = pixel[0] > 0 || pixel[1] > 0 || pixel[2] > 0; + + data[index] = 0; // Black overlay + data[index + 1] = 0; // Black overlay + data[index + 2] = 0; // Black overlay + data[index + 3] = hasMask ? 0 : alpha; // Alpha only where mask is zero + } + } + + ctx.putImageData(imageData, 0, 0); + + // Draw contour separately if enabled + this.drawMaskContour(); + } + + drawMaskContour() { + if (!this.currentMask) { + return; + } + + const showContour = document.getElementById('mask-contour').checked; + if (!showContour) { + return; + } + + const canvas = document.getElementById('mask-overlay'); + const ctx = canvas.getContext('2d'); + const maskData = this.currentMask.mask; + + const height = maskData.length; + const width = maskData[0]?.length; + + // Get current canvas data to modify it + const currentImageData = ctx.getImageData(0, 0, width, height); + const data = currentImageData.data; + + for (let y = 2; y < height - 2; y++) { + for (let x = 2; x < width - 2; x++) { + const pixel = maskData[y][x]; + const hasMask = pixel[0] > 0 || pixel[1] > 0 || pixel[2] > 0; + + if (!hasMask) { + // Check larger neighborhood for thicker contour (2-pixel radius) + let hasNeighborMask = false; + for (let dy = -2; dy <= 2; dy++) { + for (let dx = -2; dx <= 2; dx++) { + const neighbor = maskData[y + dy][x + dx]; + if (neighbor[0] > 0 || neighbor[1] > 0 || neighbor[2] > 0) { + hasNeighborMask = true; + break; + } + } + if (hasNeighborMask) break; + } + + if (hasNeighborMask) { + // Directly set contour pixels in the existing canvas data + const index = (y * width + x) * 4; + data[index] = 255; // Red + data[index + 1] = 0; // Green + data[index + 2] = 255; // Blue (magenta) + data[index + 3] = 200; // Alpha - fixed high opacity + } + } + } + } + + // Put the modified data back + ctx.putImageData(currentImageData, 0, 0); + } + + showViewer() { + const imageViewer = document.getElementById('image-viewer'); + const noExplanation = document.getElementById('no-explanation'); + + // Completely hide the no-explanation div + noExplanation.style.display = 'none'; + noExplanation.style.position = 'absolute'; + noExplanation.style.top = '-9999px'; + + // Show the image viewer + imageViewer.style.display = 'flex'; + imageViewer.style.visibility = 'visible'; + imageViewer.style.position = 'static'; + } + + closeViewer() { + const imageViewer = document.getElementById('image-viewer'); + const noExplanation = document.getElementById('no-explanation'); + + // Hide the image viewer + imageViewer.style.display = 'none'; + imageViewer.style.visibility = 'hidden'; + imageViewer.style.position = 'absolute'; + imageViewer.style.top = '-9999px'; + + // Show the no-explanation div + noExplanation.style.display = 'flex'; + noExplanation.style.visibility = 'visible'; + noExplanation.style.position = 'static'; + + // Clear current explanation and annotation + this.currentExplanation = null; + this.currentMask = null; + + // Clear annotation textarea + const annotationTextarea = document.getElementById('explanation-annotation'); + if (annotationTextarea) { + annotationTextarea.value = ''; + } + } + + updateExplanationMetadata(explanation) { + document.getElementById('exp-score').textContent = explanation.score.toFixed(4); + + // Hide class information if blinding is enabled + const reportInfo = window.app?.reportInfo; // Access through global app instance + if (reportInfo?.blinding_enabled) { + document.getElementById('exp-source-class').textContent = 'Hidden'; + document.getElementById('exp-target-class').textContent = 'Hidden'; + } else { + document.getElementById('exp-source-class').textContent = explanation.source_class; + document.getElementById('exp-target-class').textContent = explanation.target_class; + } + + document.getElementById('exp-method').textContent = explanation.method || 'N/A'; + } + + setCurrentExplanation(explanation) { + this.currentExplanation = explanation; + this.currentMask = null; + this.loadAnnotation(); + } + + loadAnnotation() { + const annotationTextarea = document.getElementById('explanation-annotation'); + if (this.currentExplanation && annotationTextarea) { + annotationTextarea.value = this.currentExplanation.annotation || ''; + } + } + + async saveAnnotation() { + if (!this.currentExplanation) return; + + const annotationTextarea = document.getElementById('explanation-annotation'); + const annotation = annotationTextarea.value; + + console.log('Saving annotation for explanation:', this.currentExplanation); + console.log('Explanation ID:', this.currentExplanation.id); + + try { + // Save to backend + const response = await fetch(`/api/explanation/${this.currentExplanation.id}/annotation`, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ annotation: annotation }) + }); + + if (response.ok) { + // Update the in-memory explanation object only if backend save succeeded + this.currentExplanation.annotation = annotation; + + // Show save status + this.showAnnotationSaved(); + + console.log(`Annotation saved for explanation ${this.currentExplanation.id}:`, annotation); + } else { + console.error('Failed to save annotation:', await response.text()); + } + } catch (error) { + console.error('Error saving annotation:', error); + } + } + + showAnnotationSaved() { + const status = document.getElementById('annotation-status'); + if (status) { + status.style.display = 'inline'; + setTimeout(() => { + status.style.display = 'none'; + }, 2000); + } + } + + setupAnnotationAutoSave() { + const annotationTextarea = document.getElementById('explanation-annotation'); + if (!annotationTextarea) return; + + let autoSaveTimeout; + + annotationTextarea.addEventListener('input', () => { + // Clear previous timeout + if (autoSaveTimeout) { + clearTimeout(autoSaveTimeout); + } + + // Set new timeout for auto-save (2 seconds after typing stops) + autoSaveTimeout = setTimeout(() => { + this.saveAnnotation(); + }, 2000); + }); + + // Also save when user leaves the textarea + annotationTextarea.addEventListener('blur', () => { + if (autoSaveTimeout) { + clearTimeout(autoSaveTimeout); + } + this.saveAnnotation(); + }); + } + + setupEventListeners() { + // Image toggle buttons + document.getElementById('show-query').addEventListener('change', () => { + if (this.currentExplanation) { + this.showImage('query'); + } + }); + + document.getElementById('show-counterfactual').addEventListener('change', () => { + if (this.currentExplanation) { + this.showImage('counterfactual'); + } + }); + + // Mask opacity slider + document.getElementById('mask-opacity').addEventListener('input', (e) => { + const opacity = parseInt(e.target.value); + this.updateMaskOpacity(opacity); + }); + + // Mask contour checkbox + document.getElementById('mask-contour').addEventListener('change', (e) => { + const opacity = parseInt(document.getElementById('mask-opacity').value); + this.updateMaskOpacity(opacity); + }); + + // Close viewer button + document.getElementById('close-viewer').addEventListener('click', () => { + this.closeViewer(); + }); + + // Setup annotation auto-save + this.setupAnnotationAutoSave(); + + // Add keyboard shortcut for annotation (A key) + document.addEventListener('keydown', (e) => { + if (e.key.toLowerCase() === 'a' && !e.ctrlKey && !e.metaKey && !e.altKey) { + // Only if not typing in another input + if (document.activeElement.tagName !== 'INPUT' && + document.activeElement.tagName !== 'TEXTAREA') { + const annotationTextarea = document.getElementById('explanation-annotation'); + if (annotationTextarea && this.currentExplanation) { + annotationTextarea.focus(); + e.preventDefault(); + } + } + } + }); + } +} + +class QuACVisualizer { + constructor() { + this.reportInfoPane = new ReportInfoPane(); + this.filterPane = new FilterPane(); + this.explanationListPane = new ExplanationListPane(); + this.explanationViewerPane = new ExplanationViewerPane(); + this.reportInfo = null; // Keep for compatibility with other methods + this.charts = {}; + this.sidebarWidth = 300; // Track current sidebar width + this.sidebarCollapsed = false; + + // Make app globally accessible for panes + window.app = this; + + this.init(); + } + + setNoExplanationState() { + const imageViewer = document.getElementById('image-viewer'); + const noExplanation = document.getElementById('no-explanation'); + + // Completely hide image viewer + imageViewer.style.display = 'none'; + imageViewer.style.visibility = 'hidden'; + imageViewer.style.position = 'absolute'; + imageViewer.style.top = '-9999px'; + + // Show centered no-explanation message + noExplanation.style.display = 'flex'; + noExplanation.style.visibility = 'visible'; + noExplanation.style.position = 'static'; + noExplanation.style.top = 'auto'; + } + + async init() { + // Ensure proper initial state - no explanation selected + this.setNoExplanationState(); + + const reportInfo = await this.reportInfoPane.loadReportInfo(); + if (reportInfo) { + this.reportInfo = reportInfo; // Store for other methods to access + this.filterPane.populateFilterOptions(reportInfo); + } + + // Setup ExplanationListPane callbacks + this.explanationListPane.setExplanationSelectCallback((explanation) => { + this.handleExplanationSelection(explanation); + }); + + this.setupEventListeners(); + await this.explanationListPane.loadExplanations(this.reportInfo, true); + await this.reportInfoPane.loadMainChart(); + } + setupEventListeners() { + // Setup filter event listeners through FilterPane + this.filterPane.setupFilterEventListeners( + () => this.applyFilters(), + () => this.resetFilters() + ); + + // Setup dual range slider + this.filterPane.setupDualRangeSlider(); + + // Setup explanation list event listeners + this.explanationListPane.setupEventListeners(() => { + this.explanationListPane.loadMoreExplanations(this.reportInfo); + }); + + // Setup explanation viewer event listeners + this.explanationViewerPane.setupEventListeners(); + + // Collapsed sidebar indicator toggle + document.getElementById('sidebar-collapsed').addEventListener('click', () => { + this.toggleSidebar(); + }); + + // Resizable dividers + this.setupResizableDivider(); + this.setupSidebarResizer(); + + // Download buttons + document.getElementById('download-json').addEventListener('click', () => { + this.downloadFilteredData('json'); + }); + + document.getElementById('download-images').addEventListener('click', () => { + this.downloadFilteredData('images'); + }); + + // Keyboard shortcuts + document.addEventListener('keydown', (e) => { + // Don't trigger shortcuts when typing in input fields or textareas + if (document.activeElement.tagName === 'INPUT' || + document.activeElement.tagName === 'TEXTAREA') { + // Escape key should unfocus from textarea/input instead of closing viewer + if (e.key === 'Escape') { + document.activeElement.blur(); + e.preventDefault(); + } + return; + } + + if (this.explanationViewerPane.currentExplanation) { + switch(e.key) { + case 'q': + case 'Q': + document.getElementById('show-query').checked = true; + this.explanationViewerPane.showImage('query'); + break; + case 'c': + case 'C': + document.getElementById('show-counterfactual').checked = true; + this.explanationViewerPane.showImage('counterfactual'); + break; + case 'm': + case 'M': + const maskSlider = document.getElementById('mask-opacity'); + const currentOpacity = parseInt(maskSlider.value); + const newOpacity = currentOpacity === 0 ? 30 : 0; + maskSlider.value = newOpacity; + document.getElementById('mask-opacity-value').textContent = `${newOpacity}%`; + this.explanationViewerPane.updateMaskOpacity(newOpacity); + break; + case 'Escape': + this.explanationViewerPane.closeViewer(); + break; + } + } + }); + } + + async applyFilters() { + this.showLoading(true); + await this.explanationListPane.loadExplanations(this.reportInfo, true); + await this.updateMainChart(); + this.showLoading(false); + } + + async updateMainChart() { + try { + const formData = new FormData(document.getElementById('filter-form')); + const params = new URLSearchParams(); + + for (let [key, value] of formData.entries()) { + if (value) params.append(key, value); + } + + const response = await fetch(`/api/curve?${params}`); + const data = await response.json(); + + if (data.error) { + console.error('Error loading filtered chart:', data.error); + return; + } + + this.reportInfoPane.renderMainChart(data); + + } catch (error) { + console.error('Error updating main chart:', error); + } + } + + + + resetFilters() { + this.filterPane.resetFilters(); + this.applyFilters(); + } + + + + handleExplanationSelection(explanation) { + // Set the explanation in the viewer pane + this.explanationViewerPane.setCurrentExplanation(explanation); + + // Show the viewer + this.explanationViewerPane.showViewer(); + + // Update metadata + this.explanationViewerPane.updateExplanationMetadata(explanation); + + // Show initial image (query by default) + document.getElementById('show-query').checked = true; + + // Load mask data and show image asynchronously (non-blocking) + this.explanationViewerPane.loadMaskData().then(() => { + this.explanationViewerPane.showImage('query'); + }); + + // Update individual curve + this.updateIndividualCurve(explanation); + } + + updateIndividualCurve(explanation) { + if (!explanation.normalized_mask_sizes || !explanation.score_changes) { + return; + } + + const ctx = document.getElementById('individual-curve').getContext('2d'); + + // Destroy existing chart if it exists + if (this.charts.individual) { + this.charts.individual.destroy(); + } + + // Reverse the arrays so x-axis goes from 0 to 1 instead of 1 to 0 + const xValues = [...explanation.normalized_mask_sizes].reverse(); + const yValues = [...explanation.score_changes].reverse(); + + this.charts.individual = new Chart(ctx, { + type: 'line', + data: { + labels: xValues, + datasets: [{ + label: 'Score Change', + data: yValues, + borderColor: 'rgb(108, 92, 231)', + backgroundColor: 'rgba(108, 92, 231, 0.1)', + borderWidth: 2, + fill: true, + tension: 0.1 + }] + }, + options: { + responsive: true, + maintainAspectRatio: false, + scales: { + x: { + type: 'linear', + display: true, + min: 0, + max: 1, + ticks: { + stepSize: 0.1 + }, + title: { + display: true, + text: 'Normalized Mask Size' + } + }, + y: { + display: true, + title: { + display: true, + text: 'Score Change' + } + } + }, + plugins: { + legend: { + display: false + }, + title: { + display: true, + text: `QuAC Score: ${explanation.score.toFixed(4)}` + } + }, + layout: { + padding: { + top: 10, + right: 20, + bottom: 10, + left: 20 + } + } + } + }); + } + + downloadFilteredData(type) { + console.log('Download button clicked, type:', type); + + const formData = new FormData(document.getElementById('filter-form')); + const params = new URLSearchParams(); + + for (let [key, value] of formData.entries()) { + if (value) params.append(key, value); + } + + const endpoint = type === 'json' ? 'explanations' : 'images'; + const url = `/api/download/${endpoint}?${params}`; + + console.log('Download URL:', url); + + // Try window.open first (works better with SSH tunnels) + try { + window.open(url, '_blank'); + console.log('Used window.open for download'); + } catch (error) { + console.log('window.open failed, trying direct navigation:', error); + // Fallback: direct navigation + window.location.href = url; + } + } + + showLoading(show) { + const overlay = document.getElementById('loading-overlay'); + overlay.style.display = show ? 'block' : 'none'; + } + + updateSidebarLayout(width, autoCollapsed = false) { + const sidebar = document.getElementById('sidebar'); + const sidebarCollapsed = document.getElementById('sidebar-collapsed'); + const sidebarDivider = document.getElementById('sidebar-divider'); + + // Auto-collapse if width is too small for content (less than 200px) + if (width > 0 && width < 200 && !autoCollapsed) { + this.sidebarCollapsed = true; + width = 0; + autoCollapsed = true; + } + + if (width === 0) { + // Collapsed state + sidebar.style.width = '0px'; + sidebar.style.padding = '0'; + sidebar.style.overflow = 'hidden'; + sidebarCollapsed.style.display = 'flex'; + sidebarDivider.style.display = 'none'; + } else { + // Expanded state + sidebar.style.width = width + 'px'; + sidebar.style.padding = '1rem'; + sidebar.style.overflow = 'auto'; + sidebarCollapsed.style.display = 'none'; + sidebarDivider.style.display = 'block'; + } + + return autoCollapsed; + } + + toggleSidebar() { + this.sidebarCollapsed = !this.sidebarCollapsed; + + if (this.sidebarCollapsed) { + this.updateSidebarLayout(0); + } else { + this.updateSidebarLayout(this.sidebarWidth); + } + } + + setupResizableDivider() { + const divider = document.getElementById('divider'); + const explanationsPane = document.getElementById('explanations-pane'); + const imageViewerPane = document.getElementById('image-viewer-pane'); + let isResizing = false; + + divider.addEventListener('mousedown', (e) => { + isResizing = true; + document.body.classList.add('resizing'); + e.preventDefault(); + }); + + document.addEventListener('mousemove', (e) => { + if (!isResizing) return; + + const container = divider.parentElement; + const containerRect = container.getBoundingClientRect(); + const containerWidth = containerRect.width; + const mouseX = e.clientX - containerRect.left; + + // Calculate new width as percentage + const newWidth = (mouseX / containerWidth) * 100; + + // Set minimum and maximum widths (20% to 80%) + const minWidth = 20; + const maxWidth = 80; + + if (newWidth >= minWidth && newWidth <= maxWidth) { + explanationsPane.style.width = newWidth + '%'; + // The image viewer pane will flex-grow to fill remaining space + } + }); + + document.addEventListener('mouseup', () => { + if (isResizing) { + isResizing = false; + document.body.classList.remove('resizing'); + } + }); + } + + setupSidebarResizer() { + const sidebarDivider = document.getElementById('sidebar-divider'); + const sidebar = document.getElementById('sidebar'); + let isResizing = false; + + sidebarDivider.addEventListener('mousedown', (e) => { + if (this.sidebarCollapsed) return; + isResizing = true; + document.body.classList.add('resizing'); + // Disable transitions during resize + sidebar.style.transition = 'none'; + e.preventDefault(); + }); + + document.addEventListener('mousemove', (e) => { + if (!isResizing) return; + + const mouseX = e.clientX; + const minWidth = 50; // Allow dragging smaller to trigger auto-collapse + const maxWidth = 600; + + if (mouseX >= minWidth && mouseX <= maxWidth) { + this.sidebarWidth = Math.max(200, mouseX); // Store minimum 200px for when we expand again + const autoCollapsed = this.updateSidebarLayout(mouseX); + if (autoCollapsed) { + this.sidebarCollapsed = true; + } + } + }); + + document.addEventListener('mouseup', () => { + if (isResizing) { + isResizing = false; + document.body.classList.remove('resizing'); + // Re-enable transitions after resize + sidebar.style.transition = 'width 0.3s ease-in-out, padding 0.3s ease-in-out'; + } + }); + } +} + +// Initialize the application when the page loads +document.addEventListener('DOMContentLoaded', () => { + new QuACVisualizer(); +}); \ No newline at end of file diff --git a/web_app/templates/index.html b/web_app/templates/index.html new file mode 100644 index 0000000..6ad94e3 --- /dev/null +++ b/web_app/templates/index.html @@ -0,0 +1,232 @@ + + + + + + QuAC Visualizer + + + + +
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Explanations
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Click on an explanation from the list to view details

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