diff --git a/.github/workflows/sync-to-hf.yml b/.github/workflows/sync-to-hf.yml new file mode 100644 index 0000000000000000000000000000000000000000..d886dc8ac5597eb11814533125ee95a93af7b3eb --- /dev/null +++ b/.github/workflows/sync-to-hf.yml @@ -0,0 +1,55 @@ +name: Sync to HuggingFace Dataset + +on: + push: + branches: [main] + paths: + - 'data/**/*.json' + workflow_dispatch: # Allow manual trigger + +jobs: + sync-to-huggingface: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 2 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install dependencies + run: | + pip install datasets huggingface_hub pandas pyarrow + + - name: Convert Changed JSONs to Parquet (Optimized) + env: + HF_DATASET_REPO: deepmage121/eee_test + HF_TOKEN: ${{ secrets.HF_TOKEN }} + run: | + echo "Detecting changed leaderboards..." + python scripts/convert_to_parquet.py + + - name: Upload Changed Parquets to HuggingFace + env: + HF_DATASET_REPO: deepmage121/eee_test + HF_TOKEN: ${{ secrets.HF_TOKEN }} + run: | + echo "Uploading changed parquets..." + python scripts/upload_to_hf.py + + - name: Report status + if: success() + run: | + echo "Successfully synced to HuggingFace dataset" + echo "View at: https://huggingface.co/datasets/deepmage121/eee_test" + if [ -f parquet_output/changed_leaderboards.json ]; then + echo "" + echo "Changes processed:" + cat parquet_output/changed_leaderboards.json + fi + diff --git a/.gitignore b/.gitignore index e43b0f988953ae3a84b00331d0ccf5f7d51cb3cf..f66c0318c86fa7c5971c863094b4f0a45f2c4d01 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,9 @@ .DS_Store +.secrets +.actrc +__pycache__/ +*.pyc +parquet_output/ +*.venv* +*.md +*.ipynb_checkpoints diff --git a/.python-version b/.python-version new file mode 100644 index 0000000000000000000000000000000000000000..2c0733315e415bfb5e5b353f9996ecd964d395b2 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/app.py b/app.py index 8c64d41b1c2e8f7e742563979659bc7e7bc69215..4132024d38460461651d2af4f7f8bf3035e413bf 100644 --- a/app.py +++ b/app.py @@ -1,479 +1,106 @@ +""" +Evaluation Leaderboard - Gradio Interface +Displays model evaluation results from HuggingFace datasets. +""" import gradio as gr import pandas as pd -import json from pathlib import Path -DATA_DIR = Path("leaderboard_data") -LEADERBOARD_CACHE = {} - -def parse_eval_json(file_path): - """Parses a single JSON file to extract model, provider, and results.""" - try: - with open(file_path, 'r') as f: - data = json.load(f) - - leaderboard_name = data.get("evaluation_source", {}).get("evaluation_source_name", "Unknown Leaderboard") - provider_name = data.get("source_metadata", {}).get("source_organization_name", "Unknown Provider") - model_id = data.get("model_info", {}).get("id", "Unknown Model") - developer_name = data.get("model_info", {}).get("developer", "Unknown Developer") - - params = data.get("model_info", {}).get("params_billions", None) - architecture = data.get("model_info", {}).get("architecture", "Unknown") - precision = data.get("additional_details", {}).get("precision", "Unknown") - if precision == "Unknown": - precision = data.get("model_info", {}).get("precision", "Unknown") - - results = {} - if "evaluation_results" in data: - for res in data["evaluation_results"]: - eval_name = res.get("evaluation_name", "Unknown Metric") - score = res.get("score_details", {}).get("score", None) - if score is not None: - results[eval_name] = score - - return { - "leaderboard": leaderboard_name, - "provider": provider_name, - "model": model_id, - "developer": developer_name, - "params": params, - "architecture": architecture, - "precision": precision, - "results": results, - "raw_data": data - } - except Exception as e: - print(f"Error parsing {file_path}: {e}") - return None - -def get_available_leaderboards(): - """Scans data directory for leaderboard folders.""" - if not DATA_DIR.exists(): - return [] - return [d.name for d in DATA_DIR.iterdir() if d.is_dir()] - -def normalize_leaderboard_name(name): - """Normalizes leaderboard name to remove spaces.""" - return name.replace(" ", "") - -def sanitize_filename_component(name): - """Sanitizes a name to be safe for use in directory names.""" - return name.replace("/", "_").replace("\\", "_").replace(":", "_").strip() - -def walk_eval_files(leaderboard_name): - """Generator that walks through Leaderboard directory recursively.""" - lb_path = DATA_DIR / leaderboard_name - if not lb_path.exists(): - return - - yield from lb_path.rglob("*.json") +# Import custom modules +from data_loader import ( + load_hf_dataset_on_startup, + get_available_leaderboards, + get_eval_metadata, + build_leaderboard_table, + clear_cache, + DATA_DIR +) +from ui_components import get_theme, get_custom_css, format_leaderboard_header, format_metric_details -def get_eval_metadata(selected_leaderboard): - """Extracts evaluation metadata from the leaderboard data.""" - if not selected_leaderboard: - return {} - - eval_metadata = {"evals": {}, "source_info": {}} - - for json_file in walk_eval_files(selected_leaderboard): - parsed = parse_eval_json(json_file) - if parsed: - if not eval_metadata["source_info"]: - source_meta = parsed["raw_data"].get("source_metadata", {}) - source_data_list = parsed["raw_data"].get("source_data", []) - url = source_data_list[0] if isinstance(source_data_list, list) and source_data_list else "#" - - eval_metadata["source_info"] = { - "organization": source_meta.get("source_organization_name", "Unknown"), - "relationship": source_meta.get("evaluator_relationship", "Unknown"), - "url": url - } - - if "evaluation_results" in parsed["raw_data"]: - for res in parsed["raw_data"]["evaluation_results"]: - eval_name = res.get("evaluation_name", "Unknown Metric") - if eval_name not in eval_metadata["evals"]: - metric_config = res.get("metric_config", {}) - eval_metadata["evals"][eval_name] = { - "description": metric_config.get("evaluation_description", "No description available"), - "score_type": metric_config.get("score_type", "unknown"), - "lower_is_better": metric_config.get("lower_is_better", False), - "min_score": metric_config.get("min_score"), - "max_score": metric_config.get("max_score"), - "level_names": metric_config.get("level_names", []), - "level_metadata": metric_config.get("level_metadata", []), - "has_unknown_level": metric_config.get("has_unknown_level", False) - } - break - - return eval_metadata -def format_eval_info_html(selected_leaderboard): - """Formats evaluation metadata into a responsive HTML grid.""" +def export_leaderboard_to_json(selected_leaderboard): + """Export current leaderboard to JSON files in a zip using parquet_to_folder.""" if not selected_leaderboard: - return """ -
-

👋 Welcome to Eval Leaderboard

-

Select a leaderboard above to visualize results and metadata.

-
- """ - - metadata = get_eval_metadata(selected_leaderboard) - if not metadata or not metadata.get("evals"): - return f"""
No metadata found for {selected_leaderboard}
""" - - source_info = metadata.get("source_info", {}) - evals = metadata.get("evals", {}) - unique_evals_count = len(evals) - - eval_badges = "".join([ - f'{name}' - for name in sorted(evals.keys()) - ]) - - source_url = source_info.get('url', '#') - source_link = f'🔗 {source_info.get("organization", "Unknown")}' - - html = f""" -
-

📊 {selected_leaderboard}

-
-
-
Source Organization
-
{source_link}
-
-
-
Evaluator Relationship
-
{source_info.get('relationship', 'Unknown').replace('_', ' ').title()}
-
-
-
Included Evaluations
-
{eval_badges}
-
-
-
- -

Metric Details

- """ + return None - html += """ -
- """ + import tempfile + import shutil + import zipfile + from json_to_parquet import parquet_to_folder - for eval_name, info in evals.items(): - score_type = info['score_type'].upper() if info['score_type'] else "UNKNOWN" - direction = "Lower is better" if info['lower_is_better'] else "Higher is better" - direction_icon = "↓" if info['lower_is_better'] else "↑" + try: + # Find the parquet file in DATA_DIR + parquet_path = DATA_DIR / selected_leaderboard / f"{selected_leaderboard}.parquet" - details_content = "" - if info['score_type'] == "continuous" and info.get('min_score') is not None: - details_content += f"
Range: [{info['min_score']} - {info['max_score']}]
" - elif info['score_type'] == "levels" and info.get('level_names'): - levels = ", ".join(info['level_names']) - details_content += f"
Levels: {levels}
" + if not parquet_path.exists(): + print(f"Parquet file not found: {parquet_path}") + return None - if info.get('has_unknown_level'): - details_content += "
* -1 indicates Unknown
" - - html += f""" -
- -
- 🏷ī¸ - {eval_name} -
-
- {direction_icon} {direction} -
- + # Create temp directory for export + with tempfile.TemporaryDirectory() as temp_dir: + temp_path = Path(temp_dir) + output_dir = temp_path / "json_export" + output_dir.mkdir() -
-

- {info['description']} -

-
-
- {details_content} -
- {score_type} -
-
-
- """ - - html += "
" - return html + # Use the round-trip functionality from json_to_parquet + parquet_to_folder(str(parquet_path), str(output_dir)) + + # Create zip file + zip_path = temp_path / f"{selected_leaderboard}_export.zip" + with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf: + for json_file in output_dir.rglob("*.json"): + arcname = json_file.relative_to(output_dir) + zipf.write(json_file, arcname) + + # Copy to a permanent location for download + final_zip = Path(tempfile.gettempdir()) / f"{selected_leaderboard}_export.zip" + shutil.copy(zip_path, final_zip) + + return str(final_zip) + except Exception as e: + print(f"Export error: {e}") + return None + -def update_leaderboard_table(selected_leaderboard, search_query="", group_by_model=False, progress=gr.Progress()): +def update_leaderboard_table(selected_leaderboard, search_query="", progress=gr.Progress()): """Loads and aggregates data for the selected leaderboard.""" if not selected_leaderboard: - return pd.DataFrame(), format_eval_info_html(None) + return pd.DataFrame(), "", format_leaderboard_header(None, {}), format_metric_details(None, {}) - # Check cache - full_df = None - if selected_leaderboard in LEADERBOARD_CACHE: - # Cache stores (df, meta_html) - full_df, meta_html = LEADERBOARD_CACHE[selected_leaderboard] - else: - progress(0, desc=f"Scanning {selected_leaderboard}...") - all_files = list(walk_eval_files(selected_leaderboard)) - total_files = len(all_files) - - rows = [] - for i, json_file in enumerate(all_files): - if i % 100 == 0: - progress((i / total_files), desc=f"Loading {selected_leaderboard}...") - parsed = parse_eval_json(json_file) - if parsed: - row = { - "Model": parsed["model"], - "Developer": parsed["developer"], - "Params (B)": parsed["params"], - "Arch": parsed["architecture"], - "Precision": parsed["precision"] - } - row.update(parsed["results"]) - rows.append(row) - - meta_html = format_eval_info_html(selected_leaderboard) - - if not rows: - full_df = pd.DataFrame(columns=["Model", "Developer", "Params (B)", "Arch", "Precision", "Score"]) - else: - full_df = pd.DataFrame(rows) - numeric_cols = full_df.select_dtypes(include=['float', 'int']).columns - full_df[numeric_cols] = full_df[numeric_cols].round(3) - - LEADERBOARD_CACHE[selected_leaderboard] = (full_df, meta_html) - - # Filter by search query - df = full_df.copy() - if search_query: - df = df[ - df["Model"].str.contains(search_query, case=False, na=False) | - df["Developer"].str.contains(search_query, case=False, na=False) - ] - - # Group by model and average scores if requested - if group_by_model and not df.empty: - # Identify grouping columns (non-numeric usually, or specific base cols) - # We group by the base identifiers. - base_cols_all = ["Model", "Developer", "Params (B)", "Arch", "Precision"] - group_cols = [c for c in base_cols_all if c in df.columns] - - # Identify columns to average (numeric) - numeric_cols = df.select_dtypes(include=['number']).columns - # Exclude group_cols from numeric_cols if they happen to be numeric (like Params) - # But groupby keys can be numeric. - # We want to average the SCORES. - # Any numeric column NOT in group_cols should be averaged. - agg_cols = [c for c in numeric_cols if c not in group_cols] - - if group_cols and agg_cols: - df = df.groupby(group_cols)[agg_cols].mean().reset_index() - df = df.round(3) - - # Drop columns where all values are null - df = df.dropna(axis=1, how='all') - - if df.empty: - return df, meta_html - - # Filter base_cols to only include columns that exist in df (in case some were dropped) - base_cols = [c for c in ["Model", "Developer", "Params (B)", "Arch", "Precision"] if c in df.columns] - eval_cols = [c for c in df.columns if c not in base_cols] - - cols = base_cols + eval_cols - return df[cols], meta_html - -def find_json_files(path): - """Recursively finds all JSON files in a directory or returns the file if it's a JSON file.""" - json_files = [] - path_obj = Path(path) - - if path_obj.is_file() and path_obj.suffix == ".json": - json_files.append(path_obj) - elif path_obj.is_dir(): - json_files.extend(path_obj.rglob("*.json")) - - return json_files - -def check_is_duplicate(save_dir, new_eval_id): - """Checks if a file with the same evaluation_id already exists in the directory.""" - if not new_eval_id or not save_dir.exists(): - return False - - for existing_file in save_dir.glob("*.json"): - try: - with open(existing_file, 'r') as f: - data = json.load(f) - if data.get("evaluation_id") == new_eval_id: - return True - except: - continue - return False - -def handle_file_upload(files, progress=gr.Progress()): - """Processes uploaded files/folders and saves them to the correct structure. + metadata = get_eval_metadata(selected_leaderboard) - Structure: Leaderboard/Provider/Model/.json - Preserves original filename (which already contains the UUID). - """ - if not files: - return gr.update(), "No files uploaded." + def progress_callback(value, desc): + progress(value, desc=desc) - saved_count = 0 - all_json_files = [] - skipped_count = 0 - duplicate_count = 0 + df = build_leaderboard_table(selected_leaderboard, "", progress_callback) + total_count = len(df) - progress(0, desc="Scanning files...") - for file_obj in files: - path = file_obj.name if hasattr(file_obj, "name") else file_obj - json_files = find_json_files(path) - - if Path(path).is_file() and Path(path).suffix != ".json": - skipped_count += 1 - - all_json_files.extend(json_files) + # Apply search filter (searches all columns) + if search_query and not df.empty: + mask = df.astype(str).apply(lambda row: row.str.contains(search_query, case=False, na=False).any(), axis=1) + df = df[mask] - total_files = len(all_json_files) - for i, json_file in enumerate(all_json_files): - progress((i / total_files), desc=f"Processing {json_file.name}...") - try: - parsed = parse_eval_json(json_file) - if not parsed: - continue - - leaderboard = normalize_leaderboard_name(parsed["leaderboard"]) - provider = parsed["provider"] - model_id = parsed["model"] - developer = parsed["developer"] - eval_id = parsed["raw_data"].get("evaluation_id") - - # Sanitize names for directory structure - sanitized_provider = sanitize_filename_component(developer) - sanitized_model = sanitize_filename_component(model_id) - - # Create structure: Leaderboard/Developer/Model - save_dir = DATA_DIR / leaderboard / sanitized_provider / sanitized_model - save_dir.mkdir(parents=True, exist_ok=True) - - # Check for duplicates based on evaluation_id - if check_is_duplicate(save_dir, eval_id): - duplicate_count += 1 - continue - - # Preserve original filename - filename = json_file.name - save_path = save_dir / filename - - # Avoid overwriting by appending counter - counter = 1 - while save_path.exists(): - stem = save_path.stem.rsplit('_', 1)[0] if '_' in save_path.stem else save_path.stem - save_path = save_dir / f"{stem}_{counter}.json" - counter += 1 - - with open(save_path, 'w') as f: - json.dump(parsed["raw_data"], f, indent=2) - - saved_count += 1 - - except Exception as e: - print(f"Failed to save {json_file}: {e}") - - # Clear cache since data changed - LEADERBOARD_CACHE.clear() - - # Refresh leaderboard choices - choices = get_available_leaderboards() + # Build search status message + if search_query: + search_msg = f"Showing {len(df)} of {total_count} results for '{search_query}'" + else: + search_msg = f"Showing {len(df)} results" - msg_parts = [f"Processed {saved_count} files."] - if duplicate_count > 0: - msg_parts.append(f"Skipped {duplicate_count} duplicates.") - if skipped_count > 0: - msg_parts.append(f"Skipped {skipped_count} non-JSON files.") - - return gr.Dropdown(choices=choices), " ".join(msg_parts), None, None + return df, search_msg, format_leaderboard_header(selected_leaderboard, metadata), format_metric_details(selected_leaderboard, metadata) -# Professional, high-contrast theme -theme = gr.themes.Soft( - primary_hue="slate", - neutral_hue="slate", - font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"] -).set( - body_background_fill="var(--neutral-50)", - block_background_fill="white", - block_border_width="1px", - block_title_text_weight="600" -) - -css = """ -/* Clean up the global container */ -.gradio-container { - max-width: 100% !important; - padding: 0 2rem !important; -} - -/* Table Styles */ -.dataframe { - border: 1px solid var(--border-color-primary) !important; - border-radius: 8px; -} -/* Hide file list in uploaders */ -.file-preview { - display: none !important; -} -""" +# Load HF dataset BEFORE building the interface +load_hf_dataset_on_startup() -with gr.Blocks(title="Eval Leaderboard", theme=theme, css=css) as demo: +# Build Gradio interface +with gr.Blocks(title="Eval Leaderboard", theme=get_theme(), css=get_custom_css()) as demo: with gr.Row(variant="compact", elem_classes="header-row"): with gr.Column(scale=1): gr.Markdown("# 🏆 Evaluation Leaderboard") gr.Markdown("Analyze and compare model performance metrics.", elem_classes="subtitle") - with gr.Row(variant="panel", equal_height=True): + with gr.Row(variant="panel"): initial_choices = get_available_leaderboards() initial_value = initial_choices[0] if initial_choices else None @@ -482,56 +109,51 @@ with gr.Blocks(title="Eval Leaderboard", theme=theme, css=css) as demo: choices=initial_choices, value=initial_value, label="Current Leaderboard", - interactive=True, - container=False, - scale=1 - ) - with gr.Column(scale=2): - search_box = gr.Textbox( - label="Search Model/Developer", - placeholder="🔍 Search model or developer...", - show_label=False, - container=False, - scale=1 - ) - with gr.Column(scale=1, min_width=100): - group_by_model = gr.Checkbox( - label="Average by Model", - value=False, - container=False + interactive=True ) - with gr.Column(scale=1, min_width=100): + with gr.Column(scale=3): + search_box = gr.Textbox( + label="Search", + placeholder="Type to search across all columns...", + show_label=False + ) + with gr.Column(scale=1): refresh_btn = gr.Button("🔄 Refresh", variant="secondary", size="sm") - with gr.Accordion("📤 Upload New Data", open=False): - upload_mode = gr.Radio( - choices=["Files", "Folder"], - value="Files", - label="Upload Mode", - info="Choose 'Files' for individual JSONs, or 'Folder' to upload a directory structure." - ) - - with gr.Group(visible=True) as file_upload_group: - file_uploader_files = gr.File( - file_count="multiple", - file_types=[".json"], - label="Select JSON Files" - ) - - with gr.Group(visible=False) as folder_upload_group: - file_uploader_folder = gr.File( - file_count="directory", - label="Select Folder" - ) - - upload_status = gr.Textbox( - label="Upload Status", - interactive=False - ) + with gr.Accordion("ℹī¸ How to Submit Data", open=False): + gr.Markdown(""" +### Submitting Evaluation Data - init_df, init_meta = update_leaderboard_table(initial_value) +**Data submissions happen via GitHub Pull Requests:** + +1. **Fork** [evaleval/every_eval_ever](https://github.com/evaleval/every_eval_ever) +2. **Add your JSON files** to `data////` +3. **Create a Pull Request** +4. **Automated validation** checks your data +5. **After merge**: GitHub Actions automatically syncs to HuggingFace +6. **Refresh this page** to see your data! + +#### File Structure +``` +data/ +└── YourBenchmark/ + └── developer_name/ + └── model_name/ + └── {uuid}.json +``` + +Each JSON file should follow the schema and be named with a unique UUID. + +📖 [**Full Submission Guide**](https://github.com/evaleval/every_eval_ever#contributor-guide) | +📋 [**JSON Schema**](https://github.com/evaleval/every_eval_ever/blob/main/eval.schema.json) | +👀 [**See Examples**](https://github.com/evaleval/every_eval_ever/tree/main/data) + """) + + init_df, init_search_msg, init_header, init_metrics = update_leaderboard_table(initial_value) + + header_view = gr.HTML(value=init_header) - metadata_view = gr.HTML(value=init_meta) + search_info = gr.Markdown(value=init_search_msg) leaderboard_table = gr.Dataframe( value=init_df, @@ -541,59 +163,33 @@ with gr.Blocks(title="Eval Leaderboard", theme=theme, css=css) as demo: elem_classes="dataframe" ) - def toggle_upload_input(mode): - return { - file_upload_group: gr.Group(visible=(mode == "Files")), - folder_upload_group: gr.Group(visible=(mode == "Folder")) - } + metrics_view = gr.HTML(value=init_metrics) - upload_mode.change( - fn=toggle_upload_input, - inputs=[upload_mode], - outputs=[file_upload_group, folder_upload_group] - ) - - file_uploader_files.upload( - fn=handle_file_upload, - inputs=[file_uploader_files], - outputs=[leaderboard_selector, upload_status, file_uploader_files, file_uploader_folder] - ) - - file_uploader_folder.upload( - fn=handle_file_upload, - inputs=[file_uploader_folder], - outputs=[leaderboard_selector, upload_status, file_uploader_files, file_uploader_folder] - ) + # Event handlers leaderboard_selector.change( fn=update_leaderboard_table, - inputs=[leaderboard_selector, search_box, group_by_model], - outputs=[leaderboard_table, metadata_view] - ) - - search_box.change( - fn=update_leaderboard_table, - inputs=[leaderboard_selector, search_box, group_by_model], - outputs=[leaderboard_table, metadata_view] + inputs=[leaderboard_selector, search_box], + outputs=[leaderboard_table, search_info, header_view, metrics_view] ) - group_by_model.change( - fn=update_leaderboard_table, - inputs=[leaderboard_selector, search_box, group_by_model], - outputs=[leaderboard_table, metadata_view] + search_box.input( + fn=update_leaderboard_table, + inputs=[leaderboard_selector, search_box], + outputs=[leaderboard_table, search_info, header_view, metrics_view] ) refresh_btn.click( - fn=lambda: (gr.Dropdown(choices=get_available_leaderboards()), "Refreshed."), - outputs=[leaderboard_selector, upload_status] + fn=lambda: gr.Dropdown(choices=get_available_leaderboards()), + outputs=[leaderboard_selector] ).then( - fn=lambda: LEADERBOARD_CACHE.clear() + fn=lambda: clear_cache() ).then( fn=update_leaderboard_table, - inputs=[leaderboard_selector, search_box, group_by_model], - outputs=[leaderboard_table, metadata_view] + inputs=[leaderboard_selector, search_box], + outputs=[leaderboard_table, search_info, header_view, metrics_view] ) - + DATA_DIR.mkdir(exist_ok=True) if __name__ == "__main__": diff --git a/data_loader.py b/data_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..510e9e03d657bd0ebe9f27b9cc44bd47cf2e933a --- /dev/null +++ b/data_loader.py @@ -0,0 +1,317 @@ +""" +Data Loader: Load from HuggingFace, parse JSON files, and build tables. +""" +import json +import pandas as pd +from pathlib import Path +from datasets import load_dataset + + +# Global caches +HF_DATASET_CACHE = {} +LEADERBOARD_CACHE = {} +DATA_DIR = Path("leaderboard_data") + + +def load_hf_dataset_on_startup(): + """Load all splits from HuggingFace dataset at startup.""" + print("Loading dataset from HuggingFace...") + try: + dataset = load_dataset("deepmage121/eee_test") + + for split_name, split_data in dataset.items(): + print(f"Loading split: {split_name} ({len(split_data)} rows)") + + df = split_data.to_pandas() + parsed_items = [] + + for _, row in df.iterrows(): + evaluation_results = json.loads(row['evaluation_results']) + + results = {} + for eval_result in evaluation_results: + eval_name = eval_result.get("evaluation_name") + score = eval_result.get("score_details", {}).get("score") + if eval_name and score is not None: + results[eval_name] = score + + additional_details = {} + if pd.notna(row.get('additional_details')): + additional_details = json.loads(row['additional_details']) + + parsed_item = { + "leaderboard": row['_leaderboard'], + "provider": row['source_organization_name'], + "model": row['model_id'], + "developer": row['model_developer'], + "params": additional_details.get('params_billions'), + "architecture": additional_details.get('architecture', 'Unknown'), + "precision": additional_details.get('precision', 'Unknown'), + "results": results, + "raw_data": { + "schema_version": row['schema_version'], + "evaluation_id": row['evaluation_id'], + "retrieved_timestamp": row['retrieved_timestamp'], + "source_data": json.loads(row['source_data']), + "evaluation_source": { + "evaluation_source_name": row['evaluation_source_name'], + "evaluation_source_type": row['evaluation_source_type'] + }, + "source_metadata": { + "source_organization_name": row['source_organization_name'], + "evaluator_relationship": row['evaluator_relationship'], + }, + "model_info": { + "name": row['model_name'], + "id": row['model_id'], + "developer": row['model_developer'], + }, + "evaluation_results": evaluation_results, + "additional_details": additional_details + } + } + + if pd.notna(row.get('source_organization_url')): + parsed_item["raw_data"]["source_metadata"]["source_organization_url"] = row['source_organization_url'] + if pd.notna(row.get('source_organization_logo_url')): + parsed_item["raw_data"]["source_metadata"]["source_organization_logo_url"] = row['source_organization_logo_url'] + if pd.notna(row.get('model_inference_platform')): + parsed_item["raw_data"]["model_info"]["inference_platform"] = row['model_inference_platform'] + + parsed_items.append(parsed_item) + + HF_DATASET_CACHE[split_name] = parsed_items + + print(f"Loaded {len(HF_DATASET_CACHE)} leaderboard(s) from HuggingFace") + return True + except Exception as e: + print(f"Warning: Could not load HuggingFace dataset: {e}") + print("Falling back to local file system...") + return False + + +def parse_eval_json(file_path): + """Parses a single JSON file to extract model, provider, and results.""" + try: + with open(file_path, 'r') as f: + data = json.load(f) + + leaderboard_name = data.get("evaluation_source", {}).get("evaluation_source_name", "Unknown Leaderboard") + provider_name = data.get("source_metadata", {}).get("source_organization_name", "Unknown Provider") + model_id = data.get("model_info", {}).get("id", "Unknown Model") + developer_name = data.get("model_info", {}).get("developer", "Unknown Developer") + + params = data.get("model_info", {}).get("params_billions", None) + architecture = data.get("model_info", {}).get("architecture", "Unknown") + precision = data.get("additional_details", {}).get("precision", "Unknown") + if precision == "Unknown": + precision = data.get("model_info", {}).get("precision", "Unknown") + + results = {} + if "evaluation_results" in data: + for res in data["evaluation_results"]: + eval_name = res.get("evaluation_name", "Unknown Metric") + score = res.get("score_details", {}).get("score", None) + if score is not None: + results[eval_name] = score + + return { + "leaderboard": leaderboard_name, + "provider": provider_name, + "model": model_id, + "developer": developer_name, + "params": params, + "architecture": architecture, + "precision": precision, + "results": results, + "raw_data": data + } + except Exception as e: + print(f"Error parsing {file_path}: {e}") + return None + + +def get_available_leaderboards(): + """Returns available leaderboards from HF cache or local directory.""" + if HF_DATASET_CACHE: + return list(HF_DATASET_CACHE.keys()) + + if not DATA_DIR.exists(): + return [] + return [d.name for d in DATA_DIR.iterdir() if d.is_dir()] + + +def walk_eval_files(leaderboard_name): + """Generator that walks through Leaderboard directory recursively.""" + lb_path = DATA_DIR / leaderboard_name + if not lb_path.exists(): + return + yield from lb_path.rglob("*.json") + + +def get_eval_metadata(selected_leaderboard): + """Extracts evaluation metadata from the leaderboard data.""" + if not selected_leaderboard: + return {} + + eval_metadata = {"evals": {}, "source_info": {}} + + if selected_leaderboard in HF_DATASET_CACHE: + parsed_items = HF_DATASET_CACHE[selected_leaderboard] + if parsed_items: + parsed = parsed_items[0] + + source_meta = parsed["raw_data"].get("source_metadata", {}) + source_data_list = parsed["raw_data"].get("source_data", []) + url = source_data_list[0] if isinstance(source_data_list, list) and source_data_list else "#" + + eval_metadata["source_info"] = { + "organization": source_meta.get("source_organization_name", "Unknown"), + "relationship": source_meta.get("evaluator_relationship", "Unknown"), + "url": url + } + + if "evaluation_results" in parsed["raw_data"]: + for res in parsed["raw_data"]["evaluation_results"]: + eval_name = res.get("evaluation_name", "Unknown Metric") + if eval_name not in eval_metadata["evals"]: + metric_config = res.get("metric_config", {}) + eval_metadata["evals"][eval_name] = { + "description": metric_config.get("evaluation_description", "No description available"), + "score_type": metric_config.get("score_type", "unknown"), + "lower_is_better": metric_config.get("lower_is_better", False), + "min_score": metric_config.get("min_score"), + "max_score": metric_config.get("max_score"), + "level_names": metric_config.get("level_names", []), + "level_metadata": metric_config.get("level_metadata", []), + "has_unknown_level": metric_config.get("has_unknown_level", False) + } + return eval_metadata + + # Fall back to file system + for json_file in walk_eval_files(selected_leaderboard): + parsed = parse_eval_json(json_file) + if parsed: + if not eval_metadata["source_info"]: + source_meta = parsed["raw_data"].get("source_metadata", {}) + source_data_list = parsed["raw_data"].get("source_data", []) + url = source_data_list[0] if isinstance(source_data_list, list) and source_data_list else "#" + + eval_metadata["source_info"] = { + "organization": source_meta.get("source_organization_name", "Unknown"), + "relationship": source_meta.get("evaluator_relationship", "Unknown"), + "url": url + } + + if "evaluation_results" in parsed["raw_data"]: + for res in parsed["raw_data"]["evaluation_results"]: + eval_name = res.get("evaluation_name", "Unknown Metric") + if eval_name not in eval_metadata["evals"]: + metric_config = res.get("metric_config", {}) + eval_metadata["evals"][eval_name] = { + "description": metric_config.get("evaluation_description", "No description available"), + "score_type": metric_config.get("score_type", "unknown"), + "lower_is_better": metric_config.get("lower_is_better", False), + "min_score": metric_config.get("min_score"), + "max_score": metric_config.get("max_score"), + "level_names": metric_config.get("level_names", []), + "level_metadata": metric_config.get("level_metadata", []), + "has_unknown_level": metric_config.get("has_unknown_level", False) + } + break + + return eval_metadata + + +def build_leaderboard_table(selected_leaderboard, search_query="", progress_callback=None): + """Builds the leaderboard DataFrame from cache or files.""" + if not selected_leaderboard: + return pd.DataFrame() + + if selected_leaderboard in LEADERBOARD_CACHE: + df, _ = LEADERBOARD_CACHE[selected_leaderboard] + else: + rows = [] + + if selected_leaderboard in HF_DATASET_CACHE: + if progress_callback: + progress_callback(0, desc=f"Loading {selected_leaderboard} from cache...") + + parsed_items = HF_DATASET_CACHE[selected_leaderboard] + + for i, parsed in enumerate(parsed_items): + if i % 100 == 0 and progress_callback: + progress_callback((i / len(parsed_items)), desc=f"Processing {selected_leaderboard}...") + + row = { + "Model": parsed["model"], + "Developer": parsed["developer"], + "Params (B)": parsed["params"], + "Arch": parsed["architecture"], + "Precision": parsed["precision"] + } + row.update(parsed["results"]) + rows.append(row) + else: + # Fall back to file system + if progress_callback: + progress_callback(0, desc=f"Scanning {selected_leaderboard}...") + + all_files = list(walk_eval_files(selected_leaderboard)) + total_files = len(all_files) + + for i, json_file in enumerate(all_files): + if i % 100 == 0 and progress_callback: + progress_callback((i / total_files), desc=f"Loading {selected_leaderboard}...") + + parsed = parse_eval_json(json_file) + if parsed: + row = { + "Model": parsed["model"], + "Developer": parsed["developer"], + "Params (B)": parsed["params"], + "Arch": parsed["architecture"], + "Precision": parsed["precision"] + } + row.update(parsed["results"]) + rows.append(row) + + if not rows: + df = pd.DataFrame(columns=["Model", "Developer", "Params (B)", "Arch", "Precision"]) + LEADERBOARD_CACHE[selected_leaderboard] = (df, None) + return df + + df = pd.DataFrame(rows) + df = df.dropna(axis=1, how='all') + + if df.empty: + LEADERBOARD_CACHE[selected_leaderboard] = (df, None) + return df + + numeric_cols = df.select_dtypes(include=['float', 'int']).columns + df[numeric_cols] = df[numeric_cols].round(3) + + # Add Average Score + eval_only_cols = [c for c in numeric_cols if c not in ["Params (B)"]] + if len(eval_only_cols) > 0: + df["Average"] = df[eval_only_cols].mean(axis=1).round(3) + + base_cols = ["Model", "Developer", "Params (B)", "Arch", "Precision", "Average"] + eval_cols = [c for c in df.columns if c not in base_cols] + base_cols = [c for c in base_cols if c in df.columns] + + final_cols = base_cols + sorted(eval_cols) + df = df[final_cols] + + if "Average" in df.columns: + df = df.sort_values("Average", ascending=False) + + LEADERBOARD_CACHE[selected_leaderboard] = (df, None) + + return df + + +def clear_cache(): + """Clears all caches.""" + LEADERBOARD_CACHE.clear() + diff --git a/eval.schema.json b/eval.schema.json new file mode 100644 index 0000000000000000000000000000000000000000..4be0e6ff925ed642124d84e36d4e5e467c71060e --- /dev/null +++ b/eval.schema.json @@ -0,0 +1,282 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "version": "0.0.1", + "type": "object", + "description": "Schema for storing and validating LLMs evaluation data, including model configuration, prompts, instances, Output, and evaluation metrics", + "required": [ + "schema_version", + "evaluation_id", + "evaluation_source", + "retrieved_timestamp", + "source_data", + "source_metadata", + "model_info", + "evaluation_results" + ], + "properties": { + "schema_version": { + "type": "string", + "description": "Version of the schema used for this evaluation data" + }, + "evaluation_id": { + "type": "string", + "description": "Unique identifier for this specific evaluation run. Use org_name/eval_name/retrieved_timestamp format" + }, + "retrieved_timestamp": { + "type": "string", + "description": "Timestamp for when this record was created" + }, + "source_data": { + "type": "array", + "description": "URLs for the source of the evaluation data", + "items": { + "type": "string" + } + }, + "evaluation_source": { + "type": "object", + "description": "Details about evaluation origin. There are options that evaluations come from leaderboards (e.g. Live Code Bench Pro) or evaluation platforms (e.g. lm-eval, inspect ai, HELM...).", + "required": [ + "evaluation_source_name", + "evaluation_source_type" + ], + "properties": { + "evaluation_source_name": { + "type": "string", + "description": "Name of the source (e.g. title of the source leaderboard or name of the platform used for the evaluation." + }, + "evaluation_source_type": { + "type": "string", + "enum": [ + "leaderboard", + "evaluation_platform" + ], + "description": "Type of evaluation source, e.g., leaderboard or evaluation platform" + } + } + }, + "source_metadata": { + "type": "object", + "description": "Metadata about the source of the leaderboard data", + "required": [ + "source_organization_name", + "evaluator_relationship" + ], + "properties": { + "source_organization_name": { + "type": "string", + "description": "Name of the organization that provides the data" + }, + "source_organization_url": { + "type": "string", + "description": "URL for the organization that provides the data" + }, + "source_organization_logo_url": { + "type": "string", + "description": "URL for the Logo for the organization that provides the data" + }, + "evaluator_relationship": { + "type": "string", + "description": "Relationship between the evaluator and the model", + "enum": [ + "first_party", + "third_party", + "collaborative", + "other" + ] + } + } + }, + "model_info": { + "type": "object", + "description": "Complete model specification including basic information, technical configuration and inference settings", + "required": [ + "name", + "id" + ], + "properties": { + "name": { + "type": "string", + "description": "Model name provided by evaluation source" + }, + "id": { + "type": "string", + "description": "Model name standarized to HuggingFace format (e.g. meta-llama/Llama-3.1-8B-Instruct)" + }, + "developer": { + "type": "string", + "description": "Name of organization that provides the model (e.g. 'OpenAI')" + }, + "inference_platform": { + "type": "string", + "description": "Description of platform used to run the evaluations (e.g. local machine, Bedrock)" + } + } + }, + "evaluation_results": { + "type": "array", + "description": "Array of evaluation results", + "items": { + "type": "object", + "required": [ + "evaluation_name", + "metric_config", + "score_details" + ], + "properties": { + "evaluation_name": { + "type": "string", + "description": "Name of the evaluation" + }, + "evaluation_timestamp": { + "type": "string", + "description": "Timestamp for when the evaluations were run" + }, + "metric_config": { + "type": "object", + "description": "Details about the metric", + "required": [ + "lower_is_better" + ], + "properties": { + "evaluation_description": { + "type": "string", + "description": "Description of the evaluation" + }, + "lower_is_better": { + "type": "boolean", + "description": "Whether a lower score is better" + }, + "score_type": { + "type": "string", + "description": "Type of score", + "enum": [ + "binary", + "continuous", + "levels" + ] + }, + "level_names": { + "type": "array", + "description": "Names of the score levels", + "items": { + "type": "string" + } + }, + "level_metadata": { + "type": "array", + "description": "Additional Description for each Score Level", + "items": { + "type": "string" + } + }, + "has_unknown_level": { + "type": "boolean", + "description": "Indicates whether there is an Unknown Level - if True, then a score of -1 will be treated as Unknown" + }, + "min_score": { + "type": "number", + "description": "Minimum possible score for continuous metric" + }, + "max_score": { + "type": "number", + "description": "Maximum possible score for continuous metric" + } + }, + "if": { + "properties": { + "score_type": { + "const": "levels" + } + } + }, + "then": { + "required": [ + "level_names", + "has_unknown_level" + ] + }, + "else": { + "if": { + "properties": { + "score_type": { + "const": "continuous" + } + } + }, + "then": { + "required": [ + "min_score", + "max_score" + ] + } + } + }, + "score_details": { + "type": "object", + "description": "The score for the evaluation and related details", + "required": [ + "score" + ], + "properties": { + "score": { + "type": "number", + "description": "The score for the evaluation" + }, + "details": { + "type": "object", + "description": "Any additional details about the score", + "additionalProperties": true + } + } + }, + "detailed_evaluation_results_url": { + "type": "string", + "description": "Link to detailed evaluation data" + }, + "generation_config": { + "type": "object", + "generation_args": { + "type": "object", + "description": "Parameters used to generate results - properties may vary by model type", + "properties": { + "temperature": { + "type": [ + "null", + "number" + ], + "description": "Sampling temperature" + }, + "top_p": { + "type": [ + "null", + "number" + ], + "description": "Nucleus sampling parameter" + }, + "top_k": { + "type": [ + "null", + "number" + ], + "description": "Top-k sampling parameter" + }, + "max_tokens": { + "type": "integer", + "minimum": 1, + "description": "Maximum number of tokens to generate" + } + }, + "additionalProperties": true + }, + "additional_details": { + "type": "string", + "description": "Additional details about how the results for this metric were generated." + } + } + } + } + + } + } +} diff --git a/hf_operations.py b/hf_operations.py new file mode 100644 index 0000000000000000000000000000000000000000..18d0fc4eb6b2e1cc40336d3686ddd027adeaac66 --- /dev/null +++ b/hf_operations.py @@ -0,0 +1,202 @@ +""" +HuggingFace Operations: Upload data, create PRs, validate schemas. +""" +from huggingface_hub import HfApi, login +import pandas as pd +import json +from pathlib import Path +from jsonschema import validate, ValidationError, Draft7Validator + + +# Load schema once at module level +SCHEMA_PATH = Path(__file__).parent / "eval.schema.json" +with open(SCHEMA_PATH, 'r') as f: + EVAL_SCHEMA = json.load(f) + + +def validate_json_against_schema(json_data): + """ + Validate a JSON object against eval.schema.json. + + Args: + json_data: Dict containing the evaluation data + + Returns: + (bool, str): (is_valid, error_message) + """ + try: + validate(instance=json_data, schema=EVAL_SCHEMA) + return True, "Schema validation passed" + except ValidationError as e: + # Extract the most relevant error message + error_path = " → ".join(str(p) for p in e.path) if e.path else "root" + return False, f"❌ Schema validation failed at '{error_path}': {e.message}" + except Exception as e: + return False, f"❌ Validation error: {str(e)}" + + +def upload_to_hf_dataset(parquet_file, split_name, repo_id="deepmage121/eee_test"): + """ + Upload a parquet file as a new split to the HF dataset. + + Args: + parquet_file: Path to parquet file + split_name: Name of the split (leaderboard name) + repo_id: HuggingFace dataset repository ID + """ + # TODO: Implement upload logic + pass + + +def check_hf_authentication(): + """ + Check if user is authenticated with HuggingFace. + + Returns: + (bool, str): (is_authenticated, username or error_message) + """ + try: + api = HfApi() + user_info = api.whoami() + return True, user_info['name'] + except Exception as e: + return False, "Not authenticated. Run: huggingface-cli login" + + +def check_duplicate_pr_exists(leaderboard_name, repo_id="deepmage121/eee_test"): + """ + Check if a PR already exists for this leaderboard. + + Args: + leaderboard_name: Name of the leaderboard + repo_id: HuggingFace dataset repository ID + + Returns: + (bool, str or None): (exists, pr_url if exists) + """ + try: + api = HfApi() + discussions = api.get_repo_discussions(repo_id=repo_id, repo_type="dataset") + + # Check for open PRs with matching title + pr_title_pattern = f"add new leaderboard: {leaderboard_name.lower()}" + for discussion in discussions: + if discussion.is_pull_request and discussion.status == "open": + if pr_title_pattern in discussion.title.lower(): + pr_url = f"https://huggingface.co/datasets/{repo_id}/discussions/{discussion.num}" + return True, pr_url + + return False, None + except Exception as e: + # If we can't check, assume no duplicate (fail open) + print(f"Warning: Could not check for duplicate PRs: {e}") + return False, None + + +def create_pr_for_new_leaderboard(leaderboard_name, parquet_file, repo_id="deepmage121/eee_test"): + """ + Create a pull request to add a new leaderboard split. + + Args: + leaderboard_name: Name of the new leaderboard + parquet_file: Path to parquet file + repo_id: HuggingFace dataset repository ID + + Returns: + (success, pr_url or error_message) + """ + # 1. Check authentication + is_auth, auth_result = check_hf_authentication() + if not is_auth: + return False, f"❌ {auth_result}" + + # 2. Check for duplicate PR + has_duplicate, duplicate_url = check_duplicate_pr_exists(leaderboard_name, repo_id) + if has_duplicate: + return False, f"⚠ī¸ PR already exists: {duplicate_url}" + + # 3. Validate parquet file exists and has data + parquet_path = Path(parquet_file) + if not parquet_path.exists(): + return False, "❌ Parquet file not found" + + df = pd.read_parquet(parquet_file) + if len(df) == 0: + return False, "❌ Parquet file is empty" + + # 4. Create PR + try: + api = HfApi() + + # Upload the parquet file to the branch + commit_message = f"Add new leaderboard: {leaderboard_name}" + + # Upload file and create PR + commit_info = api.upload_file( + path_or_fileobj=parquet_file, + path_in_repo=f"data/{leaderboard_name}.parquet", + repo_id=repo_id, + repo_type="dataset", + commit_message=commit_message, + create_pr=True, + ) + + # Extract PR URL from commit info + pr_url = commit_info.pr_url if hasattr(commit_info, 'pr_url') else f"https://huggingface.co/datasets/{repo_id}/discussions" + + return True, f"PR created ({len(df)} rows): {pr_url}" + + except Exception as e: + return False, f"❌ Failed to create PR: {str(e)}" + + +def validate_schema(parquet_file): + """ + Validate that a parquet file matches the expected schema. + + Args: + parquet_file: Path to parquet file to validate + + Returns: + (bool, str): (is_valid, error_message) + """ + try: + df = pd.read_parquet(parquet_file) + + # Required columns + required_cols = [ + '_leaderboard', '_developer', '_model', '_uuid', + 'schema_version', 'evaluation_id', 'retrieved_timestamp', + 'source_data', 'evaluation_source_name', 'evaluation_source_type', + 'source_organization_name', 'evaluator_relationship', + 'model_name', 'model_id', 'model_developer', + 'evaluation_results' + ] + + missing = [col for col in required_cols if col not in df.columns] + if missing: + return False, f"Missing required columns: {', '.join(missing)}" + + # Check data types (all should be strings) + for col in df.columns: + if df[col].dtype not in ['object', 'string']: + return False, f"Column '{col}' has wrong type: {df[col].dtype} (expected string)" + + return True, "Schema validation passed" + + except Exception as e: + return False, f"Validation error: {str(e)}" + + +def export_to_json(parquet_file, output_dir): + """ + Export parquet data back to JSON files. + Uses the parquet_to_folder function from json_to_parquet.py + + Args: + parquet_file: Path to parquet file + output_dir: Directory to write JSON files to + """ + from json_to_parquet import parquet_to_folder + parquet_to_folder(parquet_file, output_dir) + diff --git a/json_to_parquet.py b/json_to_parquet.py new file mode 100644 index 0000000000000000000000000000000000000000..f1b701fa8a354831c11579e49042235dd8982b94 --- /dev/null +++ b/json_to_parquet.py @@ -0,0 +1,228 @@ + + +import json +from pathlib import Path +import pandas as pd + + +def json_to_row(json_path: Path) -> dict: + """Convert one JSON to a single row (1 JSON = 1 row, evaluations as columns).""" + with open(json_path, 'r') as f: + data = json.load(f) + + required_fields = ["schema_version", "evaluation_id", "evaluation_source", "retrieved_timestamp", + "source_data", "source_metadata", "model_info", "evaluation_results"] + for field in required_fields: + if field not in data: + raise ValueError(f"{json_path}: Missing required field '{field}'") + + if "evaluation_source_name" not in data["evaluation_source"]: + raise ValueError(f"{json_path}: Missing required field 'evaluation_source.evaluation_source_name'") + if "evaluation_source_type" not in data["evaluation_source"]: + raise ValueError(f"{json_path}: Missing required field 'evaluation_source.evaluation_source_type'") + + if "source_organization_name" not in data["source_metadata"]: + raise ValueError(f"{json_path}: Missing required field 'source_metadata.source_organization_name'") + if "evaluator_relationship" not in data["source_metadata"]: + raise ValueError(f"{json_path}: Missing required field 'source_metadata.evaluator_relationship'") + + if "name" not in data["model_info"]: + raise ValueError(f"{json_path}: Missing required field 'model_info.name'") + if "id" not in data["model_info"]: + raise ValueError(f"{json_path}: Missing required field 'model_info.id'") + if "developer" not in data["model_info"]: + raise ValueError(f"{json_path}: Missing required field 'model_info.developer'") + + leaderboard = data["evaluation_source"]["evaluation_source_name"] + model = data["model_info"]["id"] + uuid = json_path.stem + developer = data["model_info"]["developer"] + + # Validate evaluation results + for eval_result in data["evaluation_results"]: + if "evaluation_name" not in eval_result: + raise ValueError(f"{json_path}: Missing required field 'evaluation_results[].evaluation_name'") + if "metric_config" not in eval_result: + raise ValueError(f"{json_path}: Missing required field 'evaluation_results[].metric_config'") + if "score_details" not in eval_result: + raise ValueError(f"{json_path}: Missing required field 'evaluation_results[].score_details'") + + if "lower_is_better" not in eval_result["metric_config"]: + raise ValueError(f"{json_path}: Missing required field 'evaluation_results[].metric_config.lower_is_better'") + if "score" not in eval_result["score_details"]: + raise ValueError(f"{json_path}: Missing required field 'evaluation_results[].score_details.score'") + + row = { + # Folder structure (for reconstruction) + "_leaderboard": leaderboard, + "_developer": developer, + "_model": model, + "_uuid": uuid, + + # Required top-level fields + "schema_version": data["schema_version"], + "evaluation_id": data["evaluation_id"], + "retrieved_timestamp": data["retrieved_timestamp"], + "source_data": json.dumps(data["source_data"]), + + # Required nested fields + "evaluation_source_name": data["evaluation_source"]["evaluation_source_name"], + "evaluation_source_type": data["evaluation_source"]["evaluation_source_type"], + + "source_organization_name": data["source_metadata"]["source_organization_name"], + "source_organization_url": data["source_metadata"].get("source_organization_url"), + "source_organization_logo_url": data["source_metadata"].get("source_organization_logo_url"), + "evaluator_relationship": data["source_metadata"]["evaluator_relationship"], + + "model_name": data["model_info"]["name"], + "model_id": data["model_info"]["id"], + "model_developer": data["model_info"]["developer"], + "model_inference_platform": data["model_info"].get("inference_platform"), + + # Store full evaluation_results and additional_details as JSON + "evaluation_results": json.dumps(data["evaluation_results"]), + "additional_details": json.dumps(data["additional_details"]) if "additional_details" in data else None, + } + + return row + + +def add_to_parquet(json_or_folder: str, parquet_file: str): + """ + Add JSON(s) to Parquet file. + Creates new file if it doesn't exist, appends and deduplicates if it does. + + Args: + json_or_folder: Path to single JSON file or folder containing JSONs + parquet_file: Output Parquet file path + """ + input_path = Path(json_or_folder) + + if input_path.is_file(): + json_files = [input_path] + elif input_path.is_dir(): + json_files = list(input_path.rglob("*.json")) + if not json_files: + raise ValueError(f"No JSON files found in directory: {json_or_folder}") + else: + raise ValueError(f"Invalid input: {json_or_folder}") + + print(f"Processing {len(json_files)} JSON file(s)...") + + parquet_path = Path(parquet_file) + if parquet_path.exists(): + existing_df = pd.read_parquet(parquet_file) + existing_keys = set( + existing_df[["_leaderboard", "_developer", "_model", "_uuid"]] + .apply(tuple, axis=1) + ) + print(f"Found {len(existing_df)} existing rows") + else: + existing_df = None + existing_keys = set() + + all_rows = [] + skipped = 0 + for i, jf in enumerate(json_files, 1): + if i % 100 == 0: + print(f" {i}/{len(json_files)}") + + row = json_to_row(jf) + key = (row["_leaderboard"], row["_developer"], row["_model"], row["_uuid"]) + if key not in existing_keys: + all_rows.append(row) + existing_keys.add(key) + else: + skipped += 1 + + if skipped > 0: + print(f" Skipped {skipped} duplicate file(s)") + + # Handle case where no new rows to add + if not all_rows: + if existing_df is not None: + print(f"No new files to add, keeping existing {len(existing_df)} file(s)") + return + else: + raise ValueError("No valid JSON files to process and no existing parquet file") + + new_df = pd.DataFrame(all_rows) + + if existing_df is not None: + df = pd.concat([existing_df, new_df], ignore_index=True) + print(f"Added {len(new_df)} new file(s) to existing {len(existing_df)} file(s)") + else: + df = new_df + + df.to_parquet(parquet_file, index=False) + print(f"Saved {len(df)} total file(s) to {parquet_file} ({parquet_path.stat().st_size / 1024 / 1024:.1f} MB)") + + +def parquet_to_folder(parquet_file: str, output_dir: str): + """Reconstruct folder structure from Parquet.""" + df = pd.read_parquet(parquet_file) + out = Path(output_dir) + + for _, row in df.iterrows(): + lb = row["_leaderboard"] + dev = row["_developer"] + model = row["_model"] + uuid = row["_uuid"] + + json_data = { + "schema_version": row["schema_version"], + "evaluation_id": row["evaluation_id"], + "retrieved_timestamp": row["retrieved_timestamp"], + "source_data": json.loads(row["source_data"]), + "evaluation_source": { + "evaluation_source_name": row["evaluation_source_name"], + "evaluation_source_type": row["evaluation_source_type"] + }, + "source_metadata": { + "source_organization_name": row["source_organization_name"], + "evaluator_relationship": row["evaluator_relationship"] + }, + "model_info": { + "name": row["model_name"], + "id": row["model_id"], + "developer": row["model_developer"] + }, + "evaluation_results": json.loads(row["evaluation_results"]) + } + + if pd.notna(row["source_organization_url"]): + json_data["source_metadata"]["source_organization_url"] = row["source_organization_url"] + if pd.notna(row["source_organization_logo_url"]): + json_data["source_metadata"]["source_organization_logo_url"] = row["source_organization_logo_url"] + + if pd.notna(row["model_inference_platform"]): + json_data["model_info"]["inference_platform"] = row["model_inference_platform"] + + if pd.notna(row["additional_details"]): + json_data["additional_details"] = json.loads(row["additional_details"]) + + file_path = out / lb / dev / model / f"{uuid}.json" + file_path.parent.mkdir(parents=True, exist_ok=True) + with open(file_path, 'w') as f: + json.dump(json_data, f, indent=2) + + print(f"Reconstructed {len(df)} files to {output_dir}") + + +if __name__ == "__main__": + import sys + + if len(sys.argv) < 2: + print("Usage:") + print(" python json_to_parquet.py add ") + print(" python json_to_parquet.py export ") + sys.exit(1) + + cmd = sys.argv[1] + + if cmd == "add": + add_to_parquet(sys.argv[2], sys.argv[3]) + elif cmd == "export": + parquet_to_folder(sys.argv[2], sys.argv[3]) + else: + print(f"Unknown command: {cmd}") diff --git a/leaderboard_data/HFOpenLLMv2/0-hero/0-hero_Matter-0.2-7B-DPO/40e80d5e-db72-46b7-bd14-b7d005df4be8.json b/leaderboard_data/HFOpenLLMv2/0-hero/0-hero_Matter-0.2-7B-DPO/40e80d5e-db72-46b7-bd14-b7d005df4be8.json deleted file mode 100644 index 13d42abffb5b6dec5b881d249e70ecf1598aaeae..0000000000000000000000000000000000000000 --- a/leaderboard_data/HFOpenLLMv2/0-hero/0-hero_Matter-0.2-7B-DPO/40e80d5e-db72-46b7-bd14-b7d005df4be8.json +++ /dev/null @@ -1,107 +0,0 @@ -{ - "schema_version": "0.0.1", - "evaluation_id": "hfopenllm_v2/0-hero_Matter-0.2-7B-DPO/1762652579.4626381", - "retrieved_timestamp": "1762652579.462642", - "source_data": [ - "https://open-llm-leaderboard-open-llm-leaderboard.hf.space/api/leaderboard/formatted" - ], - "evaluation_source": { - "evaluation_source_name": "HF Open LLM v2", - "evaluation_source_type": "leaderboard" - }, - "source_metadata": { - "source_organization_name": "Hugging Face", - "evaluator_relationship": "third_party" - }, - "model_info": { - "name": "0-hero/Matter-0.2-7B-DPO", - "developer": "0-hero", - "inference_platform": "unknown", - "id": "0-hero/Matter-0.2-7B-DPO" - }, - "evaluation_results": [ - { - "evaluation_name": "IFEval", - "metric_config": { - "evaluation_description": "Accuracy on IFEval", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.3302792147058693 - } - }, - { - "evaluation_name": "BBH", - "metric_config": { - "evaluation_description": "Accuracy on BBH", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.3596254301656297 - } - }, - { - "evaluation_name": "MATH Level 5", - "metric_config": { - "evaluation_description": "Exact Match on MATH Level 5", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.014350453172205438 - } - }, - { - "evaluation_name": "GPQA", - "metric_config": { - "evaluation_description": "Accuracy on GPQA", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.25922818791946306 - } - }, - { - "evaluation_name": "MUSR", - "metric_config": { - "evaluation_description": "Accuracy on MUSR", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.381375 - } - }, - { - "evaluation_name": "MMLU-PRO", - "metric_config": { - "evaluation_description": "Accuracy on MMLU-PRO", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.1163563829787234 - } - } - ], - "additional_details": { - "precision": "bfloat16", - "architecture": "MistralForCausalLM", - "params_billions": 7.242 - } -} \ No newline at end of file diff --git a/leaderboard_data/HFOpenLLMv2/01-ai/01-ai_Yi-1.5-34B-32K/0d91a153-1b6b-4891-8722-a5c7e372ba64.json b/leaderboard_data/HFOpenLLMv2/01-ai/01-ai_Yi-1.5-34B-32K/0d91a153-1b6b-4891-8722-a5c7e372ba64.json deleted file mode 100644 index 80547e421e154b508a24aebfce93d4238b937691..0000000000000000000000000000000000000000 --- a/leaderboard_data/HFOpenLLMv2/01-ai/01-ai_Yi-1.5-34B-32K/0d91a153-1b6b-4891-8722-a5c7e372ba64.json +++ /dev/null @@ -1,107 +0,0 @@ -{ - "schema_version": "0.0.1", - "evaluation_id": "hfopenllm_v2/01-ai_Yi-1.5-34B-32K/1762652579.463656", - "retrieved_timestamp": "1762652579.463657", - "source_data": [ - "https://open-llm-leaderboard-open-llm-leaderboard.hf.space/api/leaderboard/formatted" - ], - "evaluation_source": { - "evaluation_source_name": "HF Open LLM v2", - "evaluation_source_type": "leaderboard" - }, - "source_metadata": { - "source_organization_name": "Hugging Face", - "evaluator_relationship": "third_party" - }, - "model_info": { - "name": "01-ai/Yi-1.5-34B-32K", - "developer": "01-ai", - "inference_platform": "unknown", - "id": "01-ai/Yi-1.5-34B-32K" - }, - "evaluation_results": [ - { - "evaluation_name": "IFEval", - "metric_config": { - "evaluation_description": "Accuracy on IFEval", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.3118691737922047 - } - }, - { - "evaluation_name": "BBH", - "metric_config": { - "evaluation_description": "Accuracy on BBH", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.6015685776542417 - } - }, - { - "evaluation_name": "MATH Level 5", - "metric_config": { - "evaluation_description": "Exact Match on MATH Level 5", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.1540785498489426 - } - }, - { - "evaluation_name": "GPQA", - "metric_config": { - "evaluation_description": "Accuracy on GPQA", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.36325503355704697 - } - }, - { - "evaluation_name": "MUSR", - "metric_config": { - "evaluation_description": "Accuracy on MUSR", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.4398229166666667 - } - }, - { - "evaluation_name": "MMLU-PRO", - "metric_config": { - "evaluation_description": "Accuracy on MMLU-PRO", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.4709109042553192 - } - } - ], - "additional_details": { - "precision": "bfloat16", - "architecture": "LlamaForCausalLM", - "params_billions": 34.389 - } -} \ No newline at end of file diff --git a/leaderboard_data/HFOpenLLMv2/01-ai/01-ai_Yi-1.5-34B-Chat-16K/2192007d-1f6e-4f74-b518-7448ef3a896e.json b/leaderboard_data/HFOpenLLMv2/01-ai/01-ai_Yi-1.5-34B-Chat-16K/2192007d-1f6e-4f74-b518-7448ef3a896e.json deleted file mode 100644 index d0dd58d6ee88c61b554b2e20e6cd035cf66c34a4..0000000000000000000000000000000000000000 --- a/leaderboard_data/HFOpenLLMv2/01-ai/01-ai_Yi-1.5-34B-Chat-16K/2192007d-1f6e-4f74-b518-7448ef3a896e.json +++ /dev/null @@ -1,107 +0,0 @@ -{ - "schema_version": "0.0.1", - "evaluation_id": "hfopenllm_v2/01-ai_Yi-1.5-34B-Chat-16K/1762652579.464125", - "retrieved_timestamp": "1762652579.4641259", - "source_data": [ - "https://open-llm-leaderboard-open-llm-leaderboard.hf.space/api/leaderboard/formatted" - ], - "evaluation_source": { - "evaluation_source_name": "HF Open LLM v2", - "evaluation_source_type": "leaderboard" - }, - "source_metadata": { - "source_organization_name": "Hugging Face", - "evaluator_relationship": "third_party" - }, - "model_info": { - "name": "01-ai/Yi-1.5-34B-Chat-16K", - "developer": "01-ai", - "inference_platform": "unknown", - "id": "01-ai/Yi-1.5-34B-Chat-16K" - }, - "evaluation_results": [ - { - "evaluation_name": "IFEval", - "metric_config": { - "evaluation_description": "Accuracy on IFEval", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.456449997118756 - } - }, - { - "evaluation_name": "BBH", - "metric_config": { - "evaluation_description": "Accuracy on BBH", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.6100218256499571 - } - }, - { - "evaluation_name": "MATH Level 5", - "metric_config": { - "evaluation_description": "Exact Match on MATH Level 5", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.21374622356495468 - } - }, - { - "evaluation_name": "GPQA", - "metric_config": { - "evaluation_description": "Accuracy on GPQA", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.33808724832214765 - } - }, - { - "evaluation_name": "MUSR", - "metric_config": { - "evaluation_description": "Accuracy on MUSR", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.43976041666666665 - } - }, - { - "evaluation_name": "MMLU-PRO", - "metric_config": { - "evaluation_description": "Accuracy on MMLU-PRO", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.45445478723404253 - } - } - ], - "additional_details": { - "precision": "bfloat16", - "architecture": "LlamaForCausalLM", - "params_billions": 34.389 - } -} \ No newline at end of file diff --git a/leaderboard_data/HFOpenLLMv2/01-ai/01-ai_Yi-1.5-34B-Chat/e335874b-9b3e-4966-a7e0-22e9d16f8324.json b/leaderboard_data/HFOpenLLMv2/01-ai/01-ai_Yi-1.5-34B-Chat/e335874b-9b3e-4966-a7e0-22e9d16f8324.json deleted file mode 100644 index e872cb56c27fa3d3098d01803b28e9be7dbd8b1c..0000000000000000000000000000000000000000 --- a/leaderboard_data/HFOpenLLMv2/01-ai/01-ai_Yi-1.5-34B-Chat/e335874b-9b3e-4966-a7e0-22e9d16f8324.json +++ /dev/null @@ -1,107 +0,0 @@ -{ - "schema_version": "0.0.1", - "evaluation_id": "hfopenllm_v2/01-ai_Yi-1.5-34B-Chat/1762652579.463886", - "retrieved_timestamp": "1762652579.4638872", - "source_data": [ - "https://open-llm-leaderboard-open-llm-leaderboard.hf.space/api/leaderboard/formatted" - ], - "evaluation_source": { - "evaluation_source_name": "HF Open LLM v2", - "evaluation_source_type": "leaderboard" - }, - "source_metadata": { - "source_organization_name": "Hugging Face", - "evaluator_relationship": "third_party" - }, - "model_info": { - "name": "01-ai/Yi-1.5-34B-Chat", - "developer": "01-ai", - "inference_platform": "unknown", - "id": "01-ai/Yi-1.5-34B-Chat" - }, - "evaluation_results": [ - { - "evaluation_name": "IFEval", - "metric_config": { - "evaluation_description": "Accuracy on IFEval", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.6066758423205982 - } - }, - { - "evaluation_name": "BBH", - "metric_config": { - "evaluation_description": "Accuracy on BBH", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.6083748310271819 - } - }, - { - "evaluation_name": "MATH Level 5", - "metric_config": { - "evaluation_description": "Exact Match on MATH Level 5", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.277190332326284 - } - }, - { - "evaluation_name": "GPQA", - "metric_config": { - "evaluation_description": "Accuracy on GPQA", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.3649328859060403 - } - }, - { - "evaluation_name": "MUSR", - "metric_config": { - "evaluation_description": "Accuracy on MUSR", - "lower_is_better": false, - "score_type": "continuous", - "min_score": 0, - "max_score": 1 - }, - "score_details": { - "score": 0.4281979166666667 - } - }, - { - "evaluation_name": "MMLU-PRO", - 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-0,0 +1,10 @@ +[project] +name = "eee-test" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "gradio>=5.49.1", + "pandas>=2.3.2", +] diff --git a/scripts/convert_to_parquet.py b/scripts/convert_to_parquet.py new file mode 100644 index 0000000000000000000000000000000000000000..6fd401996506a5d00249ec4cd90f1f73b4d28b1f --- /dev/null +++ b/scripts/convert_to_parquet.py @@ -0,0 +1,142 @@ +""" +Incremental parquet conversion with HuggingFace sync. + +Optimized workflow: +1. Detect changed leaderboards via git diff (instant!) +2. Download ONLY changed parquets from HF (fast!) +3. Re-convert ONLY changed leaderboards +4. Ready for upload (handled by upload_to_hf.py) + +This avoids downloading and processing unchanged leaderboards. +""" + +from pathlib import Path +import sys +import subprocess +import os +import json +from datasets import load_dataset + +sys.path.insert(0, str(Path(__file__).parent.resolve().parent)) + +from json_to_parquet import add_to_parquet + +HF_DATASET_REPO = os.environ.get("HF_DATASET_REPO", "deepmage121/eee_test") + +def download_leaderboards(output_dir: Path, leaderboard_names: set[str]) -> set[str]: + """Download existing leaderboard parquets from HuggingFace.""" + try: + dataset_dict = load_dataset(HF_DATASET_REPO) + downloaded: set[str] = set() + + for lb in leaderboard_names: + if lb in dataset_dict: + print(f" Downloading {lb}") + dataset_dict[lb].to_pandas().to_parquet(output_dir / f"{lb}.parquet", index=False) + downloaded.add(lb) + else: + print(f" {lb} (new)") + + print(f"Downloaded {len(downloaded)}/{len(leaderboard_names)} parquet(s)") + return downloaded + + except Exception as e: + print(f"HF download failed: {e}") + sys.exit(1) + + +def detect_modified_leaderboards() -> set[str]: + """Get leaderboards with changed JSONs via git diff (HEAD~1).""" + try: + result = subprocess.run( + ["git", "diff", "--name-only", "HEAD~1", "HEAD", "data/"], + capture_output=True, text=True, check=True + ) + + changed_files = result.stdout.strip().split('\n') + if not changed_files or changed_files == ['']: + print("No changes detected in data/") + return set() + + leaderboards = { + Path(f).parts[1] + for f in changed_files + if f.startswith('data/') and f.endswith('.json') and len(Path(f).parts) >= 2 + } + return leaderboards + + except subprocess.CalledProcessError as e: + print(f"ERROR: Git command failed: {e}") + sys.exit(1) + + +def convert_changed_leaderboards(): + """ + Optimized conversion: detect changes, download only changed, re-convert only changed. + """ + + data_dir = Path("data") + output_dir = Path("parquet_output") + output_dir.mkdir(exist_ok=True) + + if not data_dir.exists(): + print(f"Data directory not found: {data_dir}") + sys.exit(1) + + changed_leaderboards: set[str] = detect_modified_leaderboards() + + if len(changed_leaderboards) == 0: + print("No changes detected, nothing to upload") + manifest = {"changed": [], "converted": []} + (output_dir / "changed_leaderboards.json").write_text(json.dumps(manifest, indent=2)) + sys.exit(0) + + print(f"Detected {len(changed_leaderboards)} changed leaderboard(s):") + for lb in changed_leaderboards: + print(f" {lb}") + + downloaded = download_leaderboards(output_dir, changed_leaderboards) + + converted_count = 0 + error_count = 0 + converted_leaderboards = [] + + for leaderboard_name in changed_leaderboards: + leaderboard_dir = os.path.join(data_dir, leaderboard_name) + + parquet_path = os.path.join(output_dir, f"{leaderboard_name}.parquet") + + print(f"\nConverting: {leaderboard_name}") + + try: + add_to_parquet(json_or_folder=str(leaderboard_dir), parquet_file=str(parquet_path)) + + print(f" Converted to {parquet_path}") + converted_count += 1 + converted_leaderboards.append(leaderboard_name) + + except Exception as e: + print(f" Error: {e}") + error_count += 1 + + manifest = { + "changed": list(changed_leaderboards), + "converted": converted_leaderboards, + "downloaded": list(downloaded), + "errors": error_count + } + manifest_path = os.path.join(output_dir, "changed_leaderboards.json") + with open(manifest_path, 'w') as f: + json.dump(manifest, f, indent=2) + + if error_count > 0: + sys.exit(1) + + if converted_count == 0: + print("Warning: No parquet files successfully converted!") + sys.exit(1) + + +if __name__ == "__main__": + convert_changed_leaderboards() + diff --git a/scripts/json_to_parquet.py b/scripts/json_to_parquet.py new file mode 100644 index 0000000000000000000000000000000000000000..b12fbf8a57500e8e5a2e0824effe2e9f177daba0 --- /dev/null +++ b/scripts/json_to_parquet.py @@ -0,0 +1,222 @@ +""" +Convert evaluation JSONs to Parquet for HF Datasets. +Input: single JSON or folder of JSONs (any structure) +Output: Parquet with all data + reconstructable folder structure +""" + +import json +from pathlib import Path +import pandas as pd + + +def json_to_row(json_path: Path) -> dict: + """Convert one JSON to a single row (1 JSON = 1 row, evaluations as columns).""" + with open(json_path, 'r') as f: + data = json.load(f) + + required_fields = ["schema_version", "evaluation_id", "evaluation_source", "retrieved_timestamp", + "source_data", "source_metadata", "model_info", "evaluation_results"] + for field in required_fields: + if field not in data: + raise ValueError(f"{json_path}: Missing required field '{field}'") + + if "evaluation_source_name" not in data["evaluation_source"]: + raise ValueError(f"{json_path}: Missing required field 'evaluation_source.evaluation_source_name'") + if "evaluation_source_type" not in data["evaluation_source"]: + raise ValueError(f"{json_path}: Missing required field 'evaluation_source.evaluation_source_type'") + + if "source_organization_name" not in data["source_metadata"]: + raise ValueError(f"{json_path}: Missing required field 'source_metadata.source_organization_name'") + if "evaluator_relationship" not in data["source_metadata"]: + raise ValueError(f"{json_path}: Missing required field 'source_metadata.evaluator_relationship'") + + if "name" not in data["model_info"]: + raise ValueError(f"{json_path}: Missing required field 'model_info.name'") + if "id" not in data["model_info"]: + raise ValueError(f"{json_path}: Missing required field 'model_info.id'") + if "developer" not in data["model_info"]: + raise ValueError(f"{json_path}: Missing required field 'model_info.developer'") + + leaderboard = data["evaluation_source"]["evaluation_source_name"] + model = data["model_info"]["id"] + uuid = json_path.stem + developer = data["model_info"]["developer"] + + # Validate evaluation results + for eval_result in data["evaluation_results"]: + if "evaluation_name" not in eval_result: + raise ValueError(f"{json_path}: Missing required field 'evaluation_results[].evaluation_name'") + if "metric_config" not in eval_result: + raise ValueError(f"{json_path}: Missing required field 'evaluation_results[].metric_config'") + if "score_details" not in eval_result: + raise ValueError(f"{json_path}: Missing required field 'evaluation_results[].score_details'") + + if "lower_is_better" not in eval_result["metric_config"]: + raise ValueError(f"{json_path}: Missing required field 'evaluation_results[].metric_config.lower_is_better'") + if "score" not in eval_result["score_details"]: + raise ValueError(f"{json_path}: Missing required field 'evaluation_results[].score_details.score'") + + row = { + # Folder structure (for reconstruction) + "_leaderboard": leaderboard, + "_developer": developer, + "_model": model, + "_uuid": uuid, + + # Required top-level fields + "schema_version": data["schema_version"], + "evaluation_id": data["evaluation_id"], + "retrieved_timestamp": data["retrieved_timestamp"], + "source_data": json.dumps(data["source_data"]), + + # Required nested fields + "evaluation_source_name": data["evaluation_source"]["evaluation_source_name"], + "evaluation_source_type": data["evaluation_source"]["evaluation_source_type"], + + "source_organization_name": data["source_metadata"]["source_organization_name"], + "source_organization_url": data["source_metadata"].get("source_organization_url"), + "source_organization_logo_url": data["source_metadata"].get("source_organization_logo_url"), + "evaluator_relationship": data["source_metadata"]["evaluator_relationship"], + + "model_name": data["model_info"]["name"], + "model_id": data["model_info"]["id"], + "model_developer": data["model_info"]["developer"], + "model_inference_platform": data["model_info"].get("inference_platform"), + + # Store full evaluation_results and additional_details as JSON + "evaluation_results": json.dumps(data["evaluation_results"]), + "additional_details": json.dumps(data["additional_details"]) if "additional_details" in data else None, + } + + return row + + +def add_to_parquet(json_input: str, parquet_file: str): + """ + Add JSON(s) to Parquet file. + Creates new file if it doesn't exist, appends and deduplicates if it does. + + Args: + json_input: Path to single JSON file or folder containing JSONs + parquet_file: Output Parquet file path + """ + input_path = Path(json_input) + + if input_path.is_file(): + json_files = [input_path] + elif input_path.is_dir(): + json_files = list(input_path.rglob("*.json")) + else: + raise ValueError(f"Invalid input: {json_input}") + + print(f"Processing {len(json_files)} JSON file(s)...") + + parquet_path = Path(parquet_file) + if parquet_path.exists(): + existing_df = pd.read_parquet(parquet_file) + existing_keys = set( + existing_df[["_leaderboard", "_developer", "_model", "_uuid"]] + .apply(tuple, axis=1) + ) + print(f"Found {len(existing_df)} existing rows") + else: + existing_df = None + existing_keys = set() + + all_rows = [] + skipped = 0 + for i, jf in enumerate(json_files, 1): + if i % 100 == 0: + print(f" {i}/{len(json_files)}") + + row = json_to_row(jf) + key = (row["_leaderboard"], row["_developer"], row["_model"], row["_uuid"]) + if key not in existing_keys: + all_rows.append(row) + existing_keys.add(key) + else: + skipped += 1 + + if skipped > 0: + print(f" Skipped {skipped} duplicate file(s)") + + new_df = pd.DataFrame(all_rows) + + if existing_df is not None: + df = pd.concat([existing_df, new_df], ignore_index=True) + print(f"Added {len(new_df)} new file(s) to existing {len(existing_df)} file(s)") + else: + df = new_df + + df.to_parquet(parquet_file, index=False) + print(f"Saved {len(df)} total file(s) to {parquet_file} ({parquet_path.stat().st_size / 1024 / 1024:.1f} MB)") + + +def parquet_to_folder(parquet_file: str, output_dir: str): + """Reconstruct folder structure from Parquet.""" + df = pd.read_parquet(parquet_file) + out = Path(output_dir) + + for _, row in df.iterrows(): + lb = row["_leaderboard"] + dev = row["_developer"] + model = row["_model"] + uuid = row["_uuid"] + + json_data = { + "schema_version": row["schema_version"], + "evaluation_id": row["evaluation_id"], + "retrieved_timestamp": row["retrieved_timestamp"], + "source_data": json.loads(row["source_data"]), + "evaluation_source": { + "evaluation_source_name": row["evaluation_source_name"], + "evaluation_source_type": row["evaluation_source_type"] + }, + "source_metadata": { + "source_organization_name": row["source_organization_name"], + "evaluator_relationship": row["evaluator_relationship"] + }, + "model_info": { + "name": row["model_name"], + "id": row["model_id"], + "developer": row["model_developer"] + }, + "evaluation_results": json.loads(row["evaluation_results"]) + } + + if pd.notna(row["source_organization_url"]): + json_data["source_metadata"]["source_organization_url"] = row["source_organization_url"] + if pd.notna(row["source_organization_logo_url"]): + json_data["source_metadata"]["source_organization_logo_url"] = row["source_organization_logo_url"] + + if pd.notna(row["model_inference_platform"]): + json_data["model_info"]["inference_platform"] = row["model_inference_platform"] + + if pd.notna(row["additional_details"]): + json_data["additional_details"] = json.loads(row["additional_details"]) + + file_path = out / lb / dev / model / f"{uuid}.json" + file_path.parent.mkdir(parents=True, exist_ok=True) + with open(file_path, 'w') as f: + json.dump(json_data, f, indent=2) + + print(f"Reconstructed {len(df)} files to {output_dir}") + + +if __name__ == "__main__": + import sys + + if len(sys.argv) < 2: + print("Usage:") + print(" python json_to_parquet.py add ") + print(" python json_to_parquet.py export ") + sys.exit(1) + + cmd = sys.argv[1] + + if cmd == "add": + add_to_parquet(sys.argv[2], sys.argv[3]) + elif cmd == "export": + parquet_to_folder(sys.argv[2], sys.argv[3]) + else: + print(f"Unknown command: {cmd}") diff --git a/ui_components.py b/ui_components.py new file mode 100644 index 0000000000000000000000000000000000000000..62097bd539b6df28c27973ddce2264fc70df5e1b --- /dev/null +++ b/ui_components.py @@ -0,0 +1,211 @@ +""" +UI Components: Themes, CSS, and HTML formatters for the Gradio interface. +""" +import gradio as gr + + +def get_theme(): + """Returns the custom Gradio theme.""" + return gr.themes.Soft( + primary_hue="slate", + neutral_hue="slate", + font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"] + ).set( + body_background_fill="var(--neutral-50)", + block_background_fill="white", + block_border_width="1px", + block_title_text_weight="600" + ) + + +def get_custom_css(): + """Returns custom CSS for the interface.""" + return """ +/* Clean up the global container */ +.gradio-container { + max-width: 100% !important; + padding: 0 2rem !important; +} + +/* Hide file list in uploaders */ +.file-preview { + display: none !important; +} + +/* Ensure details elements work independently */ +details { + position: relative; + isolation: isolate; +} + +details summary { + cursor: pointer; +} +""" + + +def format_leaderboard_header(selected_leaderboard, metadata): + """Formats the leaderboard header info (goes at top).""" + if not selected_leaderboard: + return """ +
+

👋 Welcome to Eval Leaderboard

+

Select a leaderboard above to visualize results and metadata.

+
+ """ + + if not metadata or not metadata.get("evals"): + return f"""
No metadata found for {selected_leaderboard}
""" + + source_info = metadata.get("source_info", {}) + org = source_info.get("organization", "Unknown") + relationship = source_info.get("relationship", "Unknown").replace("_", " ").title() + url = source_info.get("url", "#") + eval_names = list(metadata["evals"].keys()) + + # Create badges for evaluations + eval_badges = "".join([f""" + {name} + """ for name in eval_names]) + + return f""" +
+
+
+

+ {selected_leaderboard} +

+
+ Source Organization: {org} â€ĸ + Evaluator Relationship: {relationship} +
+
+
+ Included Evaluations: +
+
{eval_badges}
+
+
+ + Source + +
+
+ """ + + +def format_metric_details(selected_leaderboard, metadata): + """Formats metric detail cards (goes below table).""" + if not selected_leaderboard or not metadata or not metadata.get("evals"): + return "" + + evals = metadata.get("evals", {}) + + html = """ +

+ 📏 Metric Details +

+ +
+ """ + + for eval_name, info in evals.items(): + score_type = info['score_type'].upper() if info.get('score_type') else "UNKNOWN" + direction = "Lower is better" if info.get('lower_is_better') else "Higher is better" + direction_icon = "↓" if info.get('lower_is_better') else "↑" + + details_content = "" + if info.get('score_type') == "continuous" and info.get('min_score') is not None: + details_content += f"
Range: [{info['min_score']} - {info['max_score']}]
" + elif info.get('score_type') == "levels" and info.get('level_names'): + levels = ", ".join(str(l) for l in info['level_names']) + details_content += f"
Levels: {levels}
" + + if info.get('has_unknown_level'): + details_content += "
* -1 indicates Unknown
" + + html += f""" +
+ +
+ 🏷ī¸ + {eval_name} +
+
+ {direction_icon} {direction} +
+ + +
+

+ {info['description']} +

+
+
+ {details_content} +
+ {score_type} +
+
+
+ """ + + html += "
" + return html + diff --git a/upload_to_hf.py b/upload_to_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..471d76b5165721cc878dfb7cdc2dbb8549b7b403 --- /dev/null +++ b/upload_to_hf.py @@ -0,0 +1,122 @@ +""" +Upload changed parquet files to HuggingFace dataset. + +This script: +1. Reads the manifest of changed leaderboards +2. Uploads ONLY the changed parquet files +3. Uses HfApi for efficient individual file uploads + +Usage: + # With HF_TOKEN environment variable (GitHub Actions): + python upload_to_hf.py + + # Interactive login (local): + python upload_to_hf.py --login +""" + +from huggingface_hub import login, HfFolder, HfApi +import pandas as pd +from pathlib import Path +import sys +import os +import json + +HF_DATASET_REPO = os.environ.get("HF_DATASET_REPO", "deepmage121/eee_test") +PARQUET_DIR = Path("parquet_output") +MANIFEST_PATH = PARQUET_DIR / "changed_leaderboards.json" + +def upload_changed_parquets(): + """ + Upload only changed parquet files from manifest. + """ + + hf_token = os.environ.get("HF_TOKEN") + if hf_token: + print("Using HF_TOKEN from environment") + HfFolder.save_token(hf_token) + elif "--login" in sys.argv: + print("Logging in to HuggingFace...") + login() + else: + if not HfFolder.get_token(): + print("ERROR: Not logged in. Run with --login flag or set HF_TOKEN environment variable") + sys.exit(1) + print("Using existing HuggingFace token") + + api = HfApi() + + if not MANIFEST_PATH.exists(): + print(f"ERROR: No manifest found at {MANIFEST_PATH}") + print("Run convert_changed_to_parquet.py first to generate the manifest") + sys.exit(1) + + manifest = json.loads(MANIFEST_PATH.read_text()) + converted_leaderboards = manifest.get("converted", []) + + if not converted_leaderboards: + print("\nNo changed leaderboards to upload (per manifest)") + sys.exit(0) + + print(f"\nManifest found: {len(converted_leaderboards)} leaderboard(s) to upload") + + files_to_upload = [ + PARQUET_DIR / f"{lb}.parquet" + for lb in converted_leaderboards + ] + + files_to_upload = [f for f in files_to_upload if f.exists()] + + if not files_to_upload: + print(f"ERROR: No parquet files to upload in {PARQUET_DIR}") + sys.exit(1) + + print(f"\nUploading {len(files_to_upload)} parquet file(s):") + for pf in files_to_upload: + print(f" - {pf.stem}") + + uploaded_count = 0 + error_count = 0 + + for parquet_file in files_to_upload: + leaderboard_name = parquet_file.stem + + path_in_repo = f"data/{leaderboard_name}/data-00000-of-00001.parquet" + + try: + print(f"\nUploading: {leaderboard_name}") + + df = pd.read_parquet(parquet_file) + print(f" {len(df)} rows, {len(df.columns)} columns") + + api.upload_file( + path_or_fileobj=str(parquet_file), + path_in_repo=path_in_repo, + repo_id=HF_DATASET_REPO, + repo_type="dataset", + commit_message=f"Update {leaderboard_name} leaderboard data" + ) + + print(f" SUCCESS: Uploaded → {path_in_repo}") + uploaded_count += 1 + + except Exception as e: + print(f" ERROR: Error uploading {leaderboard_name}: {e}") + error_count += 1 + + print(f"\n{'='*70}") + print(f"Upload Summary:") + print(f"{'='*70}") + print(f" Successfully uploaded: {uploaded_count} file(s)") + print(f" Errors: {error_count} file(s)") + print(f"{'='*70}") + + if error_count > 0: + print(f"\nWARNING: {error_count} file(s) failed to upload") + sys.exit(1) + + print(f"\nSuccessfully uploaded to HuggingFace!") + print(f"View at: https://huggingface.co/datasets/{HF_DATASET_REPO}") + + +if __name__ == "__main__": + upload_changed_parquets() \ No newline at end of file