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| import gradio as gr | |
| import json | |
| import os | |
| from datetime import datetime, timezone | |
| import pandas as pd | |
| from huggingface_hub import snapshot_download | |
| from src.display.about import ( | |
| CITATION_BUTTON_LABEL, | |
| CITATION_BUTTON_TEXT, | |
| EVALUATION_QUEUE_TEXT, | |
| INTRODUCTION_TEXT, | |
| LLM_BENCHMARKS_TEXT, | |
| FAQ_TEXT, | |
| TITLE, | |
| ) | |
| from src.display.css_html_js import custom_css | |
| from src.display.utils import ( | |
| BENCHMARK_COLS, | |
| COLS, | |
| EVAL_COLS, | |
| EVAL_TYPES, | |
| NUMERIC_INTERVALS, | |
| TYPES, | |
| AutoEvalColumn, | |
| ModelType, | |
| fields, | |
| WeightType, | |
| Precision | |
| ) | |
| from src.envs import API, EVAL_REQUESTS_PATH, DYNAMIC_INFO_REPO, DYNAMIC_INFO_FILE_PATH, DYNAMIC_INFO_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO | |
| from src.populate import get_evaluation_queue_df, get_leaderboard_df | |
| from src.submission.submit import add_new_eval | |
| from src.tools.collections import update_collections | |
| from src.tools.plots import ( | |
| create_metric_plot_obj, | |
| create_plot_df, | |
| create_scores_df, | |
| ) | |
| def restart_space(): | |
| API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) | |
| def init_space(): | |
| try: | |
| print(EVAL_REQUESTS_PATH) | |
| snapshot_download( | |
| repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 | |
| ) | |
| except Exception: | |
| restart_space() | |
| try: | |
| print(DYNAMIC_INFO_PATH) | |
| snapshot_download( | |
| repo_id=DYNAMIC_INFO_REPO, local_dir=DYNAMIC_INFO_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 | |
| ) | |
| except Exception: | |
| restart_space() | |
| try: | |
| print(EVAL_RESULTS_PATH) | |
| snapshot_download( | |
| repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 | |
| ) | |
| except Exception: | |
| restart_space() | |
| raw_data, original_df = get_leaderboard_df( | |
| results_path=EVAL_RESULTS_PATH, | |
| requests_path=EVAL_REQUESTS_PATH, | |
| dynamic_path=DYNAMIC_INFO_FILE_PATH, | |
| cols=COLS, | |
| benchmark_cols=BENCHMARK_COLS | |
| ) | |
| update_collections(original_df.copy()) | |
| leaderboard_df = original_df.copy() | |
| plot_df = create_plot_df(create_scores_df(raw_data)) | |
| ( | |
| finished_eval_queue_df, | |
| running_eval_queue_df, | |
| pending_eval_queue_df, | |
| ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) | |
| return leaderboard_df, original_df, plot_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df | |
| leaderboard_df, original_df, plot_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space() | |
| # Searching and filtering | |
| def update_table( | |
| hidden_df: pd.DataFrame, | |
| columns: list, | |
| type_query: list, | |
| precision_query: str, | |
| size_query: list, | |
| show_deleted: bool, | |
| show_merges: bool, | |
| show_moe: bool, | |
| show_flagged: bool, | |
| query: str, | |
| ): | |
| filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted, show_merges, show_moe, show_flagged) | |
| filtered_df = filter_queries(query, filtered_df) | |
| df = select_columns(filtered_df, columns) | |
| return df | |
| def load_query(request: gr.Request): # triggered only once at startup => read query parameter if it exists | |
| query = request.query_params.get("query") or "" | |
| return query, query # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed | |
| def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: | |
| return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))] | |
| def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: | |
| always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] | |
| dummy_col = [AutoEvalColumn.dummy.name] | |
| #AutoEvalColumn.model_type_symbol.name, | |
| #AutoEvalColumn.model.name, | |
| # We use COLS to maintain sorting | |
| filtered_df = df[ | |
| always_here_cols + [c for c in COLS if c in df.columns and c in columns] + dummy_col | |
| ] | |
| return filtered_df | |
| def filter_queries(query: str, filtered_df: pd.DataFrame): | |
| """Added by Abishek""" | |
| final_df = [] | |
| if query != "": | |
| queries = [q.strip() for q in query.split(";")] | |
| for _q in queries: | |
| _q = _q.strip() | |
| if _q != "": | |
| temp_filtered_df = search_table(filtered_df, _q) | |
| if len(temp_filtered_df) > 0: | |
| final_df.append(temp_filtered_df) | |
| if len(final_df) > 0: | |
| filtered_df = pd.concat(final_df) | |
| filtered_df = filtered_df.drop_duplicates( | |
| subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name] | |
| ) | |
| return filtered_df | |
| def filter_models( | |
| df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool, show_merges: bool, show_moe:bool, show_flagged: bool | |
| ) -> pd.DataFrame: | |
| # Show all models | |
| if show_deleted: | |
| filtered_df = df | |
| else: # Show only still on the hub models | |
| filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True] | |
| if not show_merges: | |
| filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False] | |
| if not show_moe: | |
| filtered_df = filtered_df[filtered_df[AutoEvalColumn.moe.name] == False] | |
| if not show_flagged: | |
| filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False] | |
| type_emoji = [t[0] for t in type_query] | |
| filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] | |
| filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])] | |
| numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query])) | |
| params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce") | |
| mask = params_column.apply(lambda x: any(numeric_interval.contains(x))) | |
| filtered_df = filtered_df.loc[mask] | |
| return filtered_df | |
| leaderboard_df = filter_models( | |
| df=leaderboard_df, | |
| type_query=[t.to_str(" : ") for t in ModelType], | |
| size_query=list(NUMERIC_INTERVALS.keys()), | |
| precision_query=[i.value.name for i in Precision], | |
| show_deleted=False, | |
| show_merges=False, | |
| show_moe=True, | |
| show_flagged=False | |
| ) | |
| import unicodedata | |
| def is_valid_unicode(char): | |
| try: | |
| unicodedata.name(char) | |
| return True # Valid Unicode character | |
| except ValueError: | |
| return False # Invalid Unicode character | |
| def remove_invalid_unicode(input_string): | |
| if isinstance(input_string, str): | |
| valid_chars = [char for char in input_string if is_valid_unicode(char)] | |
| return ''.join(valid_chars) | |
| else: | |
| return input_string # Return non-string values as is | |
| dummy1 = gr.Textbox(visible=False) | |
| hidden_leaderboard_table_for_search = gr.components.Dataframe( | |
| headers=COLS, | |
| datatype=TYPES, | |
| visible=False, | |
| line_breaks=False, | |
| interactive=False | |
| ) | |
| def display(x, y): | |
| # Assuming df is your DataFrame | |
| for column in leaderboard_df.columns: | |
| if leaderboard_df[column].dtype == 'object': | |
| leaderboard_df[column] = leaderboard_df[column].apply(remove_invalid_unicode) | |
| subset_df = leaderboard_df[COLS] | |
| # Ensure the output directory exists | |
| #output_dir = 'output' | |
| #if not os.path.exists(output_dir): | |
| # os.makedirs(output_dir) | |
| # | |
| ## Save JSON to a file in the output directory | |
| #output_file_path = os.path.join(output_dir, 'output.json') | |
| #with open(output_file_path, 'w') as file: | |
| # file.write(subset_df.to_json(orient='records')) | |
| #first_50_rows = subset_df.head(50) | |
| #print(first_50_rows.to_string()) | |
| #json_data = first_50_rows.to_json(orient='records') | |
| #print(json_data) # Print JSON representation | |
| return subset_df | |
| INTRODUCTION_TEXT = """ | |
| This is a copied space from Open Source LLM leaderboard. Instead of displaying | |
| the results as table the space simply provides a gradio API interface to access | |
| the full leaderboard data easily. | |
| Example python on how to access the data: | |
| ```python | |
| from gradio_client import Client | |
| import json | |
| client = Client("https://felixz-open-llm-leaderboard.hf.space/") | |
| json_data = client.predict("","", api_name='/predict') | |
| with open(json_data, 'r') as file: | |
| file_data = file.read() | |
| # Load the JSON data | |
| data = json.loads(file_data) | |
| # Get the headers and the data | |
| headers = data['headers'] | |
| data = data['data'] | |
| ``` | |
| """ | |
| interface = gr.Interface( | |
| fn=display, | |
| inputs=[gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text"), dummy1], | |
| outputs=[hidden_leaderboard_table_for_search] | |
| ) | |
| interface.launch() | |