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| import json | |
| 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, | |
| TITLE, | |
| TABLE_DESC, | |
| ) | |
| from src.display.css_html_js import custom_css | |
| from src.display.formatting import styled_error, styled_message, styled_warning, model_hyperlink | |
| from src.display.utils import ( | |
| BENCHMARK_COLS, | |
| COLS, | |
| EVAL_COLS, | |
| EVAL_TYPES, | |
| NUMERIC_INTERVALS, | |
| TYPES, | |
| TYPES_LITE, | |
| AutoEvalColumn, | |
| ModelType, | |
| fields, | |
| WeightType, | |
| Precision, | |
| ) | |
| from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, TOKEN, 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 captcha.image import ImageCaptcha | |
| from PIL import Image | |
| import random, string | |
| import matplotlib.pyplot as plt | |
| original_df = None | |
| leaderboard_df = None | |
| def restart_space(): | |
| API.restart_space(repo_id=REPO_ID, token=TOKEN) | |
| def add_model_hyperlinks(row): | |
| if row["Model URL"] is None or row["Model URL"] == "": | |
| return row["Model"] | |
| else: | |
| return model_hyperlink(row["Model URL"], row["Model"]) | |
| def download_data(): | |
| global original_df | |
| global leaderboard_df | |
| try: | |
| print(EVAL_REQUESTS_PATH,QUEUE_REPO) | |
| 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(EVAL_RESULTS_PATH, RESULTS_REPO) | |
| snapshot_download( | |
| repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 | |
| ) | |
| except Exception: | |
| restart_space() | |
| _, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) | |
| leaderboard_df = original_df.copy() | |
| leaderboard_df["Model"] = leaderboard_df.apply(add_model_hyperlinks, axis=1) | |
| leaderboard_df.sort_values(by=["Aggregate Score"], ascending=False, inplace=True) | |
| download_data() | |
| """ | |
| ( | |
| finished_eval_queue_df, | |
| running_eval_queue_df, | |
| pending_eval_queue_df, | |
| ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) | |
| """ | |
| # Searching and filtering | |
| def update_table( | |
| hidden_df: pd.DataFrame, | |
| columns: list, | |
| query: str, | |
| ): | |
| columns += " " # The dataframe does not display the last column - BUG in gradio? | |
| #filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted) | |
| filtered_df = filter_queries(query, hidden_df) | |
| filtered_df["Model"] = filtered_df.apply(add_model_hyperlinks, axis=1) | |
| df = select_columns(filtered_df, columns) | |
| return df | |
| def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: | |
| print(query) | |
| return df[(df[AutoEvalColumn.eval_name.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.eval_name.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] | |
| ] | |
| return filtered_df | |
| def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame: | |
| final_df = [] | |
| if query != "" and query is not None: | |
| 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.eval_name.name] | |
| ) | |
| return filtered_df | |
| def filter_models( | |
| df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: 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] | |
| 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 | |
| def validate_upload(input): | |
| try: | |
| with open(input, mode="r") as f: | |
| data = json.load(f) | |
| #raise gr.Error("Cannot divide by zero!") | |
| except: | |
| raise gr.Error("Cannot parse file") | |
| def generate_captcha(width=300, height=220, length=4): | |
| text = ''.join(random.choices(string.ascii_uppercase + string.digits, k=length)) | |
| captcha_obj = ImageCaptcha(width, height) | |
| data = captcha_obj.generate(text) | |
| image = Image.open(data) | |
| return image, text | |
| def validate_captcha(input, text, img): | |
| img, new_text = generate_captcha() | |
| if input.lower() == text.lower(): | |
| return True, styled_message("Correct! You can procede with your submission."), new_text, img, "" | |
| return False, styled_error("Incorrect! Please retry with the new code."), new_text, img, "" | |
| demo = gr.Blocks(css=custom_css, theme=gr.themes.Default(primary_hue=gr.themes.colors.orange, secondary_hue=gr.themes.colors.orange)) | |
| with demo: | |
| gr.HTML(TITLE) | |
| gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem("🏅 Leaderboard", elem_id="llm-benchmark-tab-table", id=0) as tb_board: | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| search_bar = gr.Textbox( | |
| placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...", | |
| show_label=False, | |
| elem_id="search-bar", | |
| ) | |
| with gr.Row(): | |
| shown_columns = gr.CheckboxGroup( | |
| choices=[ | |
| c.name | |
| for c in fields(AutoEvalColumn) | |
| if not c.hidden and not c.never_hidden # and not c.dummy # Causes errors | |
| ], | |
| value=[ | |
| c.name | |
| for c in fields(AutoEvalColumn) | |
| if c.displayed_by_default and not c.hidden and not c.never_hidden | |
| ], | |
| label="Select columns to show", | |
| elem_id="column-select", | |
| interactive=True, | |
| ) | |
| gr.Markdown(TABLE_DESC, elem_classes="markdown-text") | |
| leaderboard_table = gr.Dataframe( | |
| value=leaderboard_df[ | |
| [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value | |
| ], | |
| headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value, | |
| datatype=TYPES_LITE, | |
| elem_id="leaderboard-table", | |
| interactive=False, | |
| visible=True, | |
| wrap=False, | |
| ) | |
| # Dummy leaderboard for handling the case when the user uses backspace key | |
| hidden_leaderboard_table_for_search = gr.Dataframe( | |
| value=original_df[COLS], | |
| headers=COLS, | |
| datatype=TYPES, | |
| visible=False, | |
| ) | |
| search_bar.submit( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| ) | |
| shown_columns.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| queue=True, | |
| ) | |
| model_num = len(original_df) | |
| graph_df = original_df.drop(columns=[" ", "Precision", "Model URL"]).set_index("Model").T | |
| graph_ax = graph_df.plot( | |
| kind="barh", | |
| title="Graphical performance comparison", | |
| xlabel="Accuracy [%]", | |
| ylabel="Model", | |
| width=0.9, | |
| figsize=(15, 7 + 2*model_num), | |
| ) | |
| graph_ax.invert_yaxis() | |
| for container in graph_ax.containers: | |
| graph_ax.bar_label(container, fontsize=8, fmt="%.1f") | |
| graph_ax.legend(loc='center left', bbox_to_anchor=(1.01, 0.95)) | |
| plt.tight_layout(rect=[0, 0, 0.95, 1]) | |
| plot = gr.Plot(graph_ax.get_figure(), label="Graphical performance comparison") | |
| with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): | |
| gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
| with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3): | |
| with gr.Column(): | |
| with gr.Row(): | |
| gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") | |
| """ | |
| with gr.Column(): | |
| with gr.Accordion( | |
| f"✅ Finished Evaluations ({len(finished_eval_queue_df)})", | |
| open=False, | |
| ): | |
| with gr.Row(): | |
| finished_eval_table = gr.components.Dataframe( | |
| value=finished_eval_queue_df, | |
| headers=EVAL_COLS, | |
| datatype=EVAL_TYPES, | |
| row_count=5, | |
| ) | |
| with gr.Accordion( | |
| f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})", | |
| open=False, | |
| ): | |
| with gr.Row(): | |
| running_eval_table = gr.components.Dataframe( | |
| value=running_eval_queue_df, | |
| headers=EVAL_COLS, | |
| datatype=EVAL_TYPES, | |
| row_count=5, | |
| ) | |
| with gr.Accordion( | |
| f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})", | |
| open=False, | |
| ): | |
| with gr.Row(): | |
| pending_eval_table = gr.components.Dataframe( | |
| value=pending_eval_queue_df, | |
| headers=EVAL_COLS, | |
| datatype=EVAL_TYPES, | |
| row_count=5, | |
| ) | |
| """ | |
| with gr.Row(): | |
| gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text") | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| model_name_textbox = gr.Textbox(label="Model name", info="Please avoid using the slash (/) character") | |
| #precision = gr.Radio(["bfloat16", "float16", "4bit"], label="Precision", info="What precision are you using for inference?") | |
| precision = gr.Dropdown( | |
| choices=[i.value.name for i in Precision if i != Precision.Unknown], | |
| label="Precision", | |
| multiselect=False, | |
| value="other", | |
| interactive=True, | |
| info="What weight precision were you using during the evaluation?" | |
| ) | |
| hf_model_id = gr.Textbox(label="Model link (Optional)", info="URL to the model's Hugging Face repository, or it's official website") | |
| contact_email = gr.Textbox(label="Your E-Mail") | |
| file_input = gr.File(file_count="single", interactive=True, label="Upload json file with evaluation results", file_types=['.json', '.jsonl']) | |
| file_input.upload(validate_upload, file_input) | |
| #upload_button = gr.UploadButton("Upload json", file_types=['.json']) | |
| #upload_button.upload(validate_upload, upload_button, file_input) | |
| with gr.Group(): | |
| captcha_correct = gr.State(False) | |
| text = gr.State("") | |
| image, text.value = generate_captcha() | |
| captcha_img = gr.Image( | |
| image, | |
| label="Prove your humanity", | |
| interactive=False, | |
| show_download_button=False, | |
| show_fullscreen_button=False, | |
| show_share_button=False, | |
| ) | |
| captcha_input = gr.Textbox(placeholder="Enter the text in the image above", show_label=False, container=False) | |
| check_button = gr.Button("Validate", interactive=True) | |
| captcha_result = gr.Markdown() | |
| check_button.click( | |
| fn = validate_captcha, | |
| inputs = [captcha_input, text, captcha_img], | |
| outputs = [captcha_correct, captcha_result, text, captcha_img, captcha_input], | |
| ) | |
| submit_button = gr.Button("Submit Eval", interactive=True) | |
| submission_result = gr.Markdown() | |
| submit_button.click( | |
| fn = add_new_eval, | |
| inputs = [ | |
| model_name_textbox, | |
| file_input, | |
| precision, | |
| hf_model_id, | |
| contact_email, | |
| captcha_correct, | |
| ], | |
| outputs = [submission_result, captcha_correct], | |
| ) | |
| with gr.Row(): | |
| with gr.Accordion("📙 Citation", open=False): | |
| citation_button = gr.Textbox( | |
| value=CITATION_BUTTON_TEXT, | |
| label=CITATION_BUTTON_LABEL, | |
| lines=14, | |
| elem_id="citation-button", | |
| show_copy_button=True, | |
| ) | |
| demo.load( | |
| fn=generate_captcha, | |
| outputs=[captcha_img, text] | |
| ) | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(restart_space, "interval", seconds=43200) | |
| scheduler.start() | |
| demo.queue(default_concurrency_limit=40).launch(server_name="0.0.0.0") | |