#!/usr/bin/env python import gradio as gr import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler 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, LLM_BENCHMARKS_DETAILS, 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, 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.utils import get_dataset_summary_table def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout): try: print(local_dir) snapshot_download(repo_id=repo_id, local_dir=local_dir, repo_type=repo_type, tqdm_class=tqdm_class, etag_timeout=etag_timeout) except Exception as e: restart_space() def restart_space(): API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) def init_space(): dataset_df = get_dataset_summary_table(file_path='blog/Hallucination-Leaderboard-Summary.csv') import socket if socket.gethostname() not in {'neuromancer'}: ui_snapshot_download(repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30) ui_snapshot_download(repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30) raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) return dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space() leaderboard_df = original_df.copy() # Searching and filtering def update_table(hidden_df: pd.DataFrame, columns: list, type_query: list, precision_query: list, size_query: list, query: str): filtered_df = filter_models(hidden_df, type_query, size_query, precision_query) filtered_df = filter_queries(query, filtered_df) df = select_columns(filtered_df, columns) return df 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 = [AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] dummy_col = [AutoEvalColumn.dummy.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] + [AutoEvalColumn.dummy.name] 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): 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) subset = [AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name] filtered_df = filtered_df.drop_duplicates(subset=subset) return filtered_df def filter_models(df: pd.DataFrame, type_query: list, size_query: list, precision_query: list) -> pd.DataFrame: # Show all models filtered_df = df 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 # triggered only once at startup => read query parameter if it exists def load_query(request: gr.Request): query = request.query_params.get("query") or "" return query 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], ) 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] return subset_df INTRODUCTION_TEXT = """ This is a copied space from LLM Trustworthy Leaderboard. Instead of displaying the results as table this space was modified to simply provides a gradio API interface. Using the following python script below, users can access the full leaderboard data easily. Python on how to access the data: ```python # Import dependencies from gradio_client import Client # Initialize the Gradio client with the API URL client = Client("https://rodrigomasini-data-only-hallucination-leaderboard.hf.space/") try: # Perform the API call response = client.predict("","", api_name='/predict') # Check if response it's directly accessible if len(response) > 0: print("Response received!") headers = response.get('headers', []) data = response.get('data', []) print(headers) # Remove commenst if you want to download the dataset and save in csv format # Specify the path to your CSV file #csv_file_path = 'llm-trustworthy-benchmark.csv' # Open the CSV file for writing #with open(csv_file_path, mode='w', newline='', encoding='utf-8') as file: # writer = csv.writer(file) # Write the headers # writer.writerow(headers) # Write the data # for row in data: # writer.writerow(row) #print(f"Results saved to {csv_file_path}") # If the above line prints a string that looks like JSON, you can parse it with json.loads(response) # Otherwise, you might need to adjust based on the actual structure of `response` except Exception as e: print(f"An error occurred: {e}") ``` """ interface = gr.Interface( fn=display, inputs=[gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text"), dummy1], outputs=[hidden_leaderboard_table_for_search] ) scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=1800) scheduler.start() interface.launch()