Report Bundle Tool with Config Packaging and Schema Validation

report_bundler/README.md

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+# Report Bundler 
+
+Hi! I'm Rachana.
+
+This is my submission for DreamLayer's Open Source Challenge. (Task #5 - Report Bundle)
+
+Why this Option?
+As someone who’s worked on intelligent data pipelines and NLP automation tools, this challenge was a fun way to apply my real world experience into a compact, useful OSS tool.
+
+This task generates a reproducible `report.zip` containing:
+- Metadata (`results.csv`)
+- Generation config (`config.json`)
+- Final grid images
+- Schema validation + README
+
+---
+
+## CSV Columns
+
+| Column        | Description                                               |
+|---------------|-----------------------------------------------------------|
+| image_path    | Relative path to the grid image                           |
+| sampler       | Sampling algorithm used                                   |
+| steps         | Number of inference steps                                 |
+| cfg           | Classifier-Free Guidance scale                            |
+| preset        | Style or visual preset used                               |
+| seed          | Random seed for deterministic generation                  |
+| width         | Grid width in pixels (added for visual clarity)           |
+| height        | Grid height in pixels (added for visual clarity)          |
+| grid_label    | Custom label for the image (used in overlay or UX tags)   |
+| notes         | Any human-readable notes about generation intent          |
+
+
+
+The output is deterministic, simple to trace, and easy to integrate into DreamLayer workflows.
+
+
+## How to Run
+
+```bash
+cd report_bundler
+python bundler.py

report_bundler/bundler.py

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+import csv
+import json
+import zipfile
+from pathlib import Path
+
+# These are the columns we expect in the results.csv
+REQUIRED_COLUMNS = {"image_path", "sampler", "steps", "cfg", "preset", "seed"}
+
+def validate_csv_schema(csv_path):
+    """
+    Opens the CSV file and checks if it has all required columns.
+    Handles missing headers and throws readable errors.
+    """
+    with open(csv_path, newline='') as f:
+        reader = csv.DictReader(f)
+
+        if reader.fieldnames is None:
+            raise ValueError("CSV file is empty or missing a header row.")
+
+        header_fields = set(reader.fieldnames)
+        if not REQUIRED_COLUMNS.issubset(header_fields):
+            missing = REQUIRED_COLUMNS - header_fields
+            raise ValueError(f"Missing required columns: {missing}")
+
+        return list(reader)
+
+def collect_files(csv_rows):
+    """
+    From the rows in the CSV, grab all valid image paths.
+    Filters out rows with missing or empty 'image_path' fields.
+    """
+    files = set()
+    for idx, row in enumerate(csv_rows):
+        if "image_path" not in row:
+            raise ValueError(f"Row {idx} missing 'image_path' key: {row}")
+        image_path = row["image_path"]
+        if image_path and image_path.strip():
+            files.add(image_path)
+        else:
+            print(f"Skipping row {idx} due to empty image_path.")
+    return files
+
+def create_report_zip(output_path="report.zip"):
+    """
+    This function:
+    - Validates the results.csv file
+    - Ensures all images listed exist and are safe
+    - Packages everything into report.zip
+    """
+    base_dir = Path(__file__).parent
+    csv_path = base_dir / "results.csv"
+    config_path = base_dir / "config.json"
+    readme_path = base_dir / "README.md"
+    zip_path = base_dir / output_path
+
+    # Step 1: Validate CSV
+    csv_rows = validate_csv_schema(csv_path)
+
+    # Step 2: Collect all valid image paths
+    image_paths = collect_files(csv_rows)
+
+    # Step 3: Zip all files
+    with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
+        zipf.write(csv_path, arcname="results.csv")
+        zipf.write(config_path, arcname="config.json")
+        zipf.write(readme_path, arcname="README.md")
+
+        for path in image_paths:
+            norm_path = Path(path).resolve()
+            # Prevent path traversal by ensuring image is inside project directory
+            if ".." in path or not str(norm_path).startswith(str(base_dir.resolve())):
+                raise ValueError(f"🚨 Invalid image path: {path}")
+
+            full_path = base_dir / path
+            if not full_path.exists():
+                raise FileNotFoundError(f"Image not found: {full_path}")
+
+            zipf.write(full_path, arcname=path)
+
+    print(f"Done! Report created at: {zip_path}")
+
+if __name__ == "__main__":
+    create_report_zip()

report_bundler/config.json

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+{
+  "model": "SDXL",
+  "vae": "AutoencoderKL",
+  "loras": ["AnimeStyleV2"],
+  "controlnets": ["CannyEdgeDetector"],
+  "prompt": "a serene futuristic cityscape at sunset, ultra realistic, 8k",
+  "negative_prompt": "blurry, low quality, distorted",
+  "seed": 123456,
+  "sampler": "Euler",
+  "steps": 25,
+  "cfg": 7.5,
+  "workflow": "txt2img",
+  "version": "1.0.0"
+}

report_bundler/results.csv

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+image_path,sampler,steps,cfg,preset,seed,width,height,grid_label,notes
+grids/image1.png,Euler,30,8.5,cyber-neural,108234,1024,1024,digital-consciousness,"Futuristic brain core visualization in a neon tech chamber"
+grids/image2.png,DPM++ 2M Karras,28,7.8,ui-dreamscape,786512,1024,768,holographic-devices,"Concept art of interconnected digital devices in a glowy metaverse style"

report_bundler/test_schema.py

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+import csv
+
+def test_csv_schema():
+    """
+    Make sure results.csv contains all required metadata columns.
+    You can add more optional fields in the future if needed.
+    """
+    required = {"image_path", "sampler", "steps", "cfg", "preset", "seed"}
+
+    with open("results.csv", newline='') as f:
+        reader = csv.DictReader(f)
+        assert reader.fieldnames is not None, "Missing header row in CSV"
+        header = set(reader.fieldnames)
+
+        assert required.issubset(header), f"Missing columns: {required - header}"