diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..c29583c6b01185dad97f39d3e18437ba63dfaa4d 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +examples/duck.jpg filter=lfs diff=lfs merge=lfs -text diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000000000000000000000000000000000000..dc9ce287bec66a1bb02b5e7f501d77aa77877734 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "GenAI-Arena-hf-logs"] + path = GenAI-Arena-hf-logs + url = https://github.com/jdf-prog/GenAI-Arena-hf-logs.git diff --git a/README.md b/README.md index 11c702ab2c32049c240cc1c69a0daf4fa9453658..7e54b0647406152afddbe69e00ec12f53b3e755f 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,13 @@ --- -title: GenAI-Arena -emoji: πŸš€ -colorFrom: indigo -colorTo: yellow -sdk: static +title: GenAI Arena +emoji: πŸ“ˆ +colorFrom: purple +colorTo: pink +sdk: gradio +sdk_version: 4.21.0 +app_file: app.py pinned: false -header: mini +license: mit --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file +Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..d685fb9ebf15ffbb83e693547c61defad38eb294 --- /dev/null +++ b/app.py @@ -0,0 +1,86 @@ +import gradio as gr +import os +from serve.gradio_web import * +from serve.gradio_web_image_editing import * +from serve.leaderboard import build_leaderboard_tab +from model.model_manager import ModelManager +from pathlib import Path +from serve.constants import SERVER_PORT, ROOT_PATH, ELO_RESULTS_DIR + +def build_combine_demo(models, elo_results_file, leaderboard_table_file): + + with gr.Blocks( + title="Play with Open Vision Models", + theme=gr.themes.Default(), + css=block_css, + ) as demo: + with gr.Tabs() as tabs_combine: + with gr.Tab("Image Generation", id=0): + with gr.Tabs() as tabs_ig: + with gr.Tab("Generation Arena (battle)", id=0): + build_side_by_side_ui_anony(models) + + with gr.Tab("Generation Arena (side-by-side)", id=1): + build_side_by_side_ui_named(models) + + with gr.Tab("Generation Direct Chat", id=2): + build_single_model_ui(models, add_promotion_links=True) + if elo_results_file: + with gr.Tab("Generation Leaderboard", id=3): + build_leaderboard_tab(elo_results_file['t2i_generation'], leaderboard_table_file['t2i_generation']) + with gr.Tab("About Us", id=4): + build_about() + + with gr.Tab("Image Edition", id=5): + with gr.Tabs() as tabs_ie: + with gr.Tab("Edition Arena (battle)", id=5): + build_side_by_side_ui_anony_ie(models) + + with gr.Tab("Edition Arena (side-by-side)", id=6): + build_side_by_side_ui_named_ie(models) + + with gr.Tab("Edition Direct Chat", id=7): + build_single_model_ui_ie(models, add_promotion_links=True) + if elo_results_file: + with gr.Tab("Edition Leaderboard", id=8): + build_leaderboard_tab(elo_results_file['image_editing'], leaderboard_table_file['image_editing']) + with gr.Tab("About Us", id=9): + build_about() + + return demo + + +def load_elo_results(elo_results_dir): + from collections import defaultdict + elo_results_file = defaultdict(lambda: None) + leaderboard_table_file = defaultdict(lambda: None) + if elo_results_dir is not None: + elo_results_dir = Path(elo_results_dir) + elo_results_file = {} + leaderboard_table_file = {} + for file in elo_results_dir.glob('elo_results_*.pkl'): + if 't2i_generation' in file.name: + elo_results_file['t2i_generation'] = file + elif 'image_editing' in file.name: + elo_results_file['image_editing'] = file + else: + raise ValueError(f"Unknown file name: {file.name}") + for file in elo_results_dir.glob('*_leaderboard.csv'): + if 't2i_generation' in file.name: + leaderboard_table_file['t2i_generation'] = file + elif 'image_editing' in file.name: + leaderboard_table_file['image_editing'] = file + else: + raise ValueError(f"Unknown file name: {file.name}") + + return elo_results_file, leaderboard_table_file + +if __name__ == "__main__": + server_port = SERVER_PORT + root_path = ROOT_PATH + elo_results_dir = ELO_RESULTS_DIR + models = ModelManager() + + elo_results_file, leaderboard_table_file = load_elo_results(elo_results_dir) + demo = build_combine_demo(models, elo_results_file, leaderboard_table_file) + demo.queue(max_size=20).launch(server_port=server_port, root_path=ROOT_PATH) \ No newline at end of file diff --git a/arena_elo/LICENSE b/arena_elo/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..b66e214125a484c217def34e6cd30914d086336c --- /dev/null +++ b/arena_elo/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 WildVision-Bench + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/arena_elo/README.md b/arena_elo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9056ec3417001832dfb118bd13a47a5783c24a59 --- /dev/null +++ b/arena_elo/README.md @@ -0,0 +1,46 @@ +## Computing the Elo Ratings + + +```bash +apt-get -y install pkg-config +pip install -r requirements.txt +``` + + +### to update the leaderboard + +```bash +export LOGDIR="/path/to/your/logdir" +bash update_elo_rating.sh +``` + +### to inspect the leaderboard status +```bash +python -m elo_rating.inspect_elo_rating_pkl +``` + +### to inspect the collected data status and cost +```bash +export LOGDIR="/path/to/your/logdir" +python -m elo_rating.inspect_cost +``` + +### to upload the battle data to hugging faceπŸ€— +```bash +export HUGGINGFACE_TOKEN="your_huggingface_token" +bash get_latest_data.sh +python -m elo_rating.upload_battle_data --repo_id "WildVision/wildvision-bench" --log_dir "./vision-arena-logs/" +``` + +### to upload the chat data to hugging faceπŸ€— +```bash +export HUGGINGFACE_TOKEN="your_huggingface_token" +bash get_latest_data.sh +python -m elo_rating.upload_chat_data --repo_id "WildVision/wildvision-bench" --log_dir "./vision-arena-logs/" +``` + + +### to get the collected data +```bash +python -m + diff --git a/arena_elo/edition_model_info.json b/arena_elo/edition_model_info.json new file mode 100644 index 0000000000000000000000000000000000000000..8d698281eeead890d862beeaa9528951eb01b37f --- /dev/null +++ b/arena_elo/edition_model_info.json @@ -0,0 +1,37 @@ +{ + "CycleDiffusion": { + "Link": "https://github.com/ChenWu98/cycle-diffusion", + "License": "X11", + "Organization": "Carnegie Mellon University" + }, + "PNP": { + "Link": "https://github.com/MichalGeyer/plug-and-play", + "License": "-", + "Organization": "Weizmann Institute of Science" + }, + "InstructPix2Pix": { + "Link": "https://www.timothybrooks.com/instruct-pix2pix", + "License": "Copyright 2023 Timothy Brooks, Aleksander Holynski, Alexei A. Efros", + "Organization": "University of California, Berkeley" + }, + "Pix2PixZero": { + "Link": "https://pix2pixzero.github.io", + "License": "MIT License", + "Organization": "Carnegie Mellon University, Adobe Research" + }, + "MagicBrush": { + "Link": "https://osu-nlp-group.github.io/MagicBrush", + "License": "CC-BY-4.0", + "Organization": "The Ohio State University, University of Waterloo" + }, + "Prompt2prompt": { + "Link": "https://prompt-to-prompt.github.io", + "License": "Apache-2.0", + "Organization": "Google, Tel Aviv University" + }, + "SDEdit": { + "Link": "https://sde-image-editing.github.io", + "License": "MIT License", + "Organization": "Stanford University" + } +} \ No newline at end of file diff --git a/arena_elo/elo_rating/__init__.py b/arena_elo/elo_rating/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/arena_elo/elo_rating/basic_stats.py b/arena_elo/elo_rating/basic_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..41527492b747a4d20dbf594f4e418286ed1876d5 --- /dev/null +++ b/arena_elo/elo_rating/basic_stats.py @@ -0,0 +1,227 @@ +import argparse +import code +import datetime +import json +import os +from pytz import timezone +import time + +import pandas as pd # pandas>=2.0.3 +import plotly.express as px +import plotly.graph_objects as go +from tqdm import tqdm + +NUM_SERVERS = 1 +LOG_ROOT_DIR = os.getenv("LOGDIR", None) +if LOG_ROOT_DIR is None: + raise ValueError("LOGDIR environment variable not set, please set it by `export LOGDIR=...`") + +def get_log_files(max_num_files=None): + log_root = os.path.expanduser(LOG_ROOT_DIR) + filenames = [] + if NUM_SERVERS == 1: + for filename in os.listdir(log_root): + if filename.endswith("-conv.json"): + filepath = f"{log_root}/{filename}" + name_tstamp_tuple = (filepath, os.path.getmtime(filepath)) + filenames.append(name_tstamp_tuple) + else: + for i in range(NUM_SERVERS): + for filename in os.listdir(f"{log_root}/server{i}"): + if filename.endswith("-conv.json"): + filepath = f"{log_root}/server{i}/{filename}" + name_tstamp_tuple = (filepath, os.path.getmtime(filepath)) + filenames.append(name_tstamp_tuple) + # sort by tstamp + filenames = sorted(filenames, key=lambda x: x[1]) + filenames = [x[0] for x in filenames] + + max_num_files = max_num_files or len(filenames) + filenames = filenames[-max_num_files:] + return filenames + + +def load_log_files(filename): + data = [] + for retry in range(5): + try: + lines = open(filename).readlines() + break + except FileNotFoundError: + time.sleep(2) + + for l in lines: + row = json.loads(l) + data.append( + dict( + type=row["type"], + tstamp=row["tstamp"], + model=row.get("model", ""), + models=row.get("models", ["", ""]), + ) + ) + return data + + +def load_log_files_parallel(log_files, num_threads=16): + data_all = [] + from multiprocessing import Pool + + with Pool(num_threads) as p: + ret_all = list(tqdm(p.imap(load_log_files, log_files), total=len(log_files))) + for ret in ret_all: + data_all.extend(ret) + return data_all + + +def get_anony_vote_df(df): + anony_vote_df = df[ + df["type"].isin(["leftvote", "rightvote", "tievote", "bothbad_vote"]) + ] + anony_vote_df = anony_vote_df[anony_vote_df["models"].apply(lambda x: x[0] == "")] + return anony_vote_df + + +def merge_counts(series, on, names): + ret = pd.merge(series[0], series[1], on=on) + for i in range(2, len(series)): + ret = pd.merge(ret, series[i], on=on) + ret = ret.reset_index() + old_names = list(ret.columns)[-len(series) :] + rename = {old_name: new_name for old_name, new_name in zip(old_names, names)} + ret = ret.rename(columns=rename) + return ret + + +def report_basic_stats(log_files): + df_all = load_log_files_parallel(log_files) + df_all = pd.DataFrame(df_all) + now_t = df_all["tstamp"].max() + df_1_hour = df_all[df_all["tstamp"] > (now_t - 3600)] + df_1_day = df_all[df_all["tstamp"] > (now_t - 3600 * 24)] + anony_vote_df_all = get_anony_vote_df(df_all) + + # Chat trends + chat_dates = [ + datetime.datetime.fromtimestamp(x, tz=timezone("US/Pacific")).strftime( + "%Y-%m-%d" + ) + for x in df_all[df_all["type"] == "chat"]["tstamp"] + ] + chat_dates_counts = pd.value_counts(chat_dates) + vote_dates = [ + datetime.datetime.fromtimestamp(x, tz=timezone("US/Pacific")).strftime( + "%Y-%m-%d" + ) + for x in anony_vote_df_all["tstamp"] + ] + vote_dates_counts = pd.value_counts(vote_dates) + chat_dates_bar = go.Figure( + data=[ + go.Bar( + name="Anony. Vote", + x=vote_dates_counts.index, + y=vote_dates_counts, + text=[f"{val:.0f}" for val in vote_dates_counts], + textposition="auto", + ), + go.Bar( + name="Chat", + x=chat_dates_counts.index, + y=chat_dates_counts, + text=[f"{val:.0f}" for val in chat_dates_counts], + textposition="auto", + ), + ] + ) + chat_dates_bar.update_layout( + barmode="stack", + xaxis_title="Dates", + yaxis_title="Count", + height=300, + width=1200, + ) + + # Model call counts + model_hist_all = df_all[df_all["type"] == "chat"]["model"].value_counts() + model_hist_1_day = df_1_day[df_1_day["type"] == "chat"]["model"].value_counts() + model_hist_1_hour = df_1_hour[df_1_hour["type"] == "chat"]["model"].value_counts() + model_hist = merge_counts( + [model_hist_all, model_hist_1_day, model_hist_1_hour], + on="model", + names=["All", "Last Day", "Last Hour"], + ) + model_hist_md = model_hist.to_markdown(index=False, tablefmt="github") + + # Action counts + action_hist_all = df_all["type"].value_counts() + action_hist_1_day = df_1_day["type"].value_counts() + action_hist_1_hour = df_1_hour["type"].value_counts() + action_hist = merge_counts( + [action_hist_all, action_hist_1_day, action_hist_1_hour], + on="type", + names=["All", "Last Day", "Last Hour"], + ) + action_hist_md = action_hist.to_markdown(index=False, tablefmt="github") + + # Anony vote counts + anony_vote_hist_all = anony_vote_df_all["type"].value_counts() + anony_vote_df_1_day = get_anony_vote_df(df_1_day) + anony_vote_hist_1_day = anony_vote_df_1_day["type"].value_counts() + # anony_vote_df_1_hour = get_anony_vote_df(df_1_hour) + # anony_vote_hist_1_hour = anony_vote_df_1_hour["type"].value_counts() + anony_vote_hist = merge_counts( + [anony_vote_hist_all, anony_vote_hist_1_day], + on="type", + names=["All", "Last Day"], + ) + anony_vote_hist_md = anony_vote_hist.to_markdown(index=False, tablefmt="github") + + # Last 24 hours + chat_1_day = df_1_day[df_1_day["type"] == "chat"] + num_chats_last_24_hours = [] + base = df_1_day["tstamp"].min() + for i in range(24, 0, -1): + left = base + (i - 1) * 3600 + right = base + i * 3600 + num = ((chat_1_day["tstamp"] >= left) & (chat_1_day["tstamp"] < right)).sum() + num_chats_last_24_hours.append(num) + times = [ + datetime.datetime.fromtimestamp( + base + i * 3600, tz=timezone("US/Pacific") + ).strftime("%Y-%m-%d %H:%M:%S %Z") + for i in range(24, 0, -1) + ] + last_24_hours_df = pd.DataFrame({"time": times, "value": num_chats_last_24_hours}) + last_24_hours_md = last_24_hours_df.to_markdown(index=False, tablefmt="github") + + # Last update datetime + last_updated_tstamp = now_t + last_updated_datetime = datetime.datetime.fromtimestamp( + last_updated_tstamp, tz=timezone("US/Pacific") + ).strftime("%Y-%m-%d %H:%M:%S %Z") + + # code.interact(local=locals()) + + return { + "chat_dates_bar": chat_dates_bar, + "model_hist_md": model_hist_md, + "action_hist_md": action_hist_md, + "anony_vote_hist_md": anony_vote_hist_md, + "num_chats_last_24_hours": last_24_hours_md, + "last_updated_datetime": last_updated_datetime, + } + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--max-num-files", type=int) + args = parser.parse_args() + + log_files = get_log_files(args.max_num_files) + basic_stats = report_basic_stats(log_files) + + print(basic_stats["action_hist_md"] + "\n") + print(basic_stats["model_hist_md"] + "\n") + print(basic_stats["anony_vote_hist_md"] + "\n") + print(basic_stats["num_chats_last_24_hours"] + "\n") diff --git a/arena_elo/elo_rating/clean_battle_data.py b/arena_elo/elo_rating/clean_battle_data.py new file mode 100644 index 0000000000000000000000000000000000000000..e829f4f37a5a37b065cd105514713f77babc3ae6 --- /dev/null +++ b/arena_elo/elo_rating/clean_battle_data.py @@ -0,0 +1,381 @@ +""" +Clean chatbot arena battle log. + +Usage: +python3 clean_battle_data.py --mode conv_release +""" +import argparse +import datetime +import json +import os +import sys +from pytz import timezone +import time +import PIL +from PIL import ImageFile +ImageFile.LOAD_TRUNCATED_IMAGES = True + +from tqdm import tqdm + +from .basic_stats import get_log_files, NUM_SERVERS, LOG_ROOT_DIR +from .utils import detect_language, get_time_stamp_from_date + +VOTES = ["tievote", "leftvote", "rightvote", "bothbad_vote"] +IDENTITY_WORDS = [ + "vicuna", + "lmsys", + "koala", + "uc berkeley", + "open assistant", + "laion", + "chatglm", + "chatgpt", + "gpt-4", + "openai", + "anthropic", + "claude", + "bard", + "palm", + "lamda", + "google", + "llama", + "qianwan", + "alibaba", + "mistral", + "zhipu", + "KEG lab", + "01.AI", + "AI2", + "TΓΌlu", + "Tulu", + "NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.", + "$MODERATION$ YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES.", + "API REQUEST ERROR. Please increase the number of max tokens.", + "**API REQUEST ERROR** Reason: The response was blocked.", + "**API REQUEST ERROR**", +] + +for i in range(len(IDENTITY_WORDS)): + IDENTITY_WORDS[i] = IDENTITY_WORDS[i].lower() + + +def remove_html(raw): + if raw.startswith("

"): + return raw[raw.find(": ") + 2 : -len("

\n")] + if raw.startswith("### Model A: ") or raw.startswith("### Model B: "): + return raw[13:] + return raw + + +def to_openai_format(messages): + roles = ["user", "assistant"] + ret = [] + for i, x in enumerate(messages): + ret.append({"role": roles[i % 2], "content": x[1]}) + return ret + + +def replace_model_name(old_name, tstamp): + replace_dict = { + "bard": "palm-2", + "claude-v1": "claude-1", + "claude-instant-v1": "claude-instant-1", + "oasst-sft-1-pythia-12b": "oasst-pythia-12b", + "claude-2": "claude-2.0", + } + if old_name in ["gpt-4", "gpt-3.5-turbo"]: + if tstamp > 1687849200: + return old_name + "-0613" + else: + return old_name + "-0314" + if old_name in replace_dict: + return replace_dict[old_name] + return old_name + + +def read_file(filename): + data = [] + for retry in range(5): + try: + # lines = open(filename).readlines() + for l in open(filename): + row = json.loads(l) + if row["type"] in VOTES: + data.append(row) + break + except FileNotFoundError: + time.sleep(2) + return data + + +def read_file_parallel(log_files, num_threads=16): + data_all = [] + from multiprocessing import Pool + + with Pool(num_threads) as p: + ret_all = list(tqdm(p.imap(read_file, log_files), total=len(log_files))) + for ret in ret_all: + data_all.extend(ret) + return data_all + +def load_image(image_path): + try: + return PIL.Image.open(image_path) + except: + return None + +def clean_battle_data( + log_files, exclude_model_names, ban_ip_list=None, sanitize_ip=False, mode="simple", task_name="image_editing" +): + data = read_file_parallel(log_files, num_threads=16) + + convert_type = { + "leftvote": "model_a", + "rightvote": "model_b", + "tievote": "tie", + "bothbad_vote": "tie (bothbad)", + } + + all_models = set() + all_ips = dict() + ct_anony = 0 + ct_invalid = 0 + ct_leaked_identity = 0 + ct_banned = 0 + battles = [] + for row in tqdm(data, desc="Cleaning"): + if row["models"][0] is None or row["models"][1] is None: + continue + + # Resolve model names + models_public = [remove_html(row["models"][0]), remove_html(row["models"][1])] + if "model_name" in row["states"][0]: + models_hidden = [ + row["states"][0]["model_name"], + row["states"][1]["model_name"], + ] + if models_hidden[0] is None: + models_hidden = models_public + else: + models_hidden = models_public + + if (models_public[0] == "" and models_public[1] != "") or ( + models_public[1] == "" and models_public[0] != "" + ): + ct_invalid += 1 + continue + + if models_public[0] == "" or models_public[0] == "Model A": + anony = True + models = models_hidden + ct_anony += 1 + else: + anony = False + models = models_public + if not models_public == models_hidden: + ct_invalid += 1 + continue + + # # Detect langauge + # state = row["states"][0] + # if state["offset"] >= len(state["messages"]): + # ct_invalid += 1 + # continue + # lang_code = detect_language(state["messages"][state["offset"]][1]) + + # # Drop conversations if the model names are leaked + # leaked_identity = False + # messages = "" + # for i in range(2): + # state = row["states"][i] + # for turn_idx, (role, msg) in enumerate( + # state["messages"][state["offset"] :] + # ): + # if msg: + # messages += msg.lower() + # for word in IDENTITY_WORDS: + # if word in messages: + # leaked_identity = True + # break + + # if leaked_identity: + # ct_leaked_identity += 1 + # continue + + # Replace bard with palm + models = [replace_model_name(m, row["tstamp"]) for m in models] + if task_name == "image_editing": + if not all(x.startswith("imagenhub_") and x.endswith("_edition") for x in models): + # print(f"Invalid model names: {models}") + ct_invalid += 1 + continue + models = [x[len("imagenhub_"):-len("_edition")] for x in models] + elif task_name == "t2i_generation": + if not all("playground" in x.lower() or (x.startswith("imagenhub_") and x.endswith("_generation")) for x in models): + # print(f"Invalid model names: {models}") + ct_invalid += 1 + continue + # models = [x[len("imagenhub_"):-len("_generation")] for x in models] + for i, model_name in enumerate(models): + if model_name.startswith("imagenhub_"): + models[i] = model_name[len("imagenhub_"):-len("_generation")] + + else: + raise ValueError(f"Invalid task_name: {task_name}") + + # Exclude certain models + if exclude_model_names and any(x in exclude_model_names for x in models): + ct_invalid += 1 + continue + + # if models[0] not in model_infos or models[1] not in model_infos: + # continue + + # # Exclude votes before the starting date + # if model_infos and (model_infos[models[0]]["starting_from"] > row["tstamp"] or model_infos[models[1]]["starting_from"] > row["tstamp"]): + # print(f"Invalid vote before the valid starting date for {models[0]} and {models[1]}") + # ct_invalid += 1 + # continue + + + + if mode == "conv_release": + # assert the two images are the same + date = datetime.datetime.fromtimestamp(row["tstamp"], tz=timezone("US/Pacific")).strftime("%Y-%m-%d") # 2024-02-29 + image_path_format = f"{LOG_ROOT_DIR}/{date}-convinput_images/input_image_" + image_path_0 = image_path_format + str(row["states"][0]["conv_id"]) + ".png" + image_path_1 = image_path_format + str(row["states"][1]["conv_id"]) + ".png" + if not os.path.exists(image_path_0) or not os.path.exists(image_path_1): + print(f"Image not found for {image_path_0} or {image_path_1}") + ct_invalid += 1 + continue + + image_0 = load_image(image_path_0) + image_1 = load_image(image_path_1) + if image_0 is None or image_1 is None: + print(f"Image not found for {image_path_0} or {image_path_1}") + ct_invalid += 1 + continue + if image_0.tobytes() != image_1.tobytes(): + print(f"Image not the same for {image_path_0} and {image_path_1}") + ct_invalid += 1 + continue + + + question_id = row["states"][0]["conv_id"] + # conversation_a = to_openai_format( + # row["states"][0]["messages"][row["states"][0]["offset"] :] + # ) + # conversation_b = to_openai_format( + # row["states"][1]["messages"][row["states"][1]["offset"] :] + # ) + + ip = row["ip"] + if ip not in all_ips: + all_ips[ip] = {"ip": ip, "count": 0, "sanitized_id": len(all_ips)} + all_ips[ip]["count"] += 1 + if sanitize_ip: + user_id = f"arena_user_{all_ips[ip]['sanitized_id']}" + else: + user_id = f"{all_ips[ip]['ip']}" + + if ban_ip_list is not None and ip in ban_ip_list: + ct_banned += 1 + continue + + # Save the results + battles.append( + dict( + question_id=question_id, + model_a=models[0], + model_b=models[1], + winner=convert_type[row["type"]], + judge=f"arena_user_{user_id}", + # conversation_a=conversation_a, + # conversation_b=conversation_b, + # turn=len(conversation_a) // 2, + anony=anony, + # language=lang_code, + tstamp=row["tstamp"], + ) + ) + + all_models.update(models_hidden) + battles.sort(key=lambda x: x["tstamp"]) + last_updated_tstamp = battles[-1]["tstamp"] + + last_updated_datetime = datetime.datetime.fromtimestamp( + last_updated_tstamp, tz=timezone("US/Pacific") + ).strftime("%Y-%m-%d %H:%M:%S %Z") + + print( + f"#votes: {len(data)}, #invalid votes: {ct_invalid}, " + f"#leaked_identity: {ct_leaked_identity} " + f"#banned: {ct_banned} " + ) + print(f"#battles: {len(battles)}, #anony: {ct_anony}") + print(f"#models: {len(all_models)}, {all_models}") + print(f"last-updated: {last_updated_datetime}") + + if ban_ip_list is not None: + for ban_ip in ban_ip_list: + if ban_ip in all_ips: + del all_ips[ban_ip] + print("Top 30 IPs:") + print(sorted(all_ips.values(), key=lambda x: x["count"], reverse=True)[:30]) + return battles + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--max-num-files", type=int) + parser.add_argument( + "--mode", type=str, choices=["simple", "conv_release"], default="simple" + ) + parser.add_argument("--task_name", type=str, default="image_editing", choices=["image_editing", "t2i_generation"]) + parser.add_argument("--exclude-model-names", type=str, nargs="+") + parser.add_argument("--ban-ip-file", type=str) + parser.add_argument("--sanitize-ip", action="store_true", default=False) + args = parser.parse_args() + + log_files = get_log_files(args.max_num_files) + ban_ip_list = json.load(open(args.ban_ip_file)) if args.ban_ip_file else None + + battles = clean_battle_data( + log_files, args.exclude_model_names or [], ban_ip_list, args.sanitize_ip, args.mode, args.task_name + ) + last_updated_tstamp = battles[-1]["tstamp"] + cutoff_date = datetime.datetime.fromtimestamp( + last_updated_tstamp, tz=timezone("US/Pacific") + ).strftime("%Y%m%d") + + if args.mode == "simple": + for x in battles: + for key in [ + "conversation_a", + "conversation_b", + "question_id", + ]: + if key in x: + del x[key] + print("Samples:") + for i in range(min(4, len(battles))): + print(battles[i]) + output = f"clean_battle_{args.task_name}_{cutoff_date}.json" + elif args.mode == "conv_release": + # new_battles = [] + # for x in battles: + # if not x["anony"]: + # continue + # for key in []: + # del x[key] + # new_battles.append(x) + # battles = new_battles + output = f"clean_battle_{args.task_name}_conv_{cutoff_date}.json" + + with open(output, "w") as fout: + json.dump(battles, fout, indent=2, ensure_ascii=False) + print(f"Write cleaned data to {output}") + + with open("cut_off_date.txt", "w") as fout: + fout.write(cutoff_date) \ No newline at end of file diff --git a/arena_elo/elo_rating/elo_analysis.py b/arena_elo/elo_rating/elo_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..67a5795c61cf846ac9a1b8419509e02cccd568d1 --- /dev/null +++ b/arena_elo/elo_rating/elo_analysis.py @@ -0,0 +1,378 @@ +import argparse +from collections import defaultdict +import datetime +import json +import math +import pickle +from pytz import timezone + +import numpy as np +import pandas as pd +import plotly.express as px +from tqdm import tqdm + +from .model_registry import get_model_info +from .basic_stats import get_log_files +from .clean_battle_data import clean_battle_data + +pd.options.display.float_format = "{:.2f}".format + + +def compute_elo(battles, K=4, SCALE=400, BASE=10, INIT_RATING=1000): + rating = defaultdict(lambda: INIT_RATING) + + for rd, model_a, model_b, winner in battles[ + ["model_a", "model_b", "winner"] + ].itertuples(): + ra = rating[model_a] + rb = rating[model_b] + ea = 1 / (1 + BASE ** ((rb - ra) / SCALE)) + eb = 1 / (1 + BASE ** ((ra - rb) / SCALE)) + if winner == "model_a": + sa = 1 + elif winner == "model_b": + sa = 0 + elif winner == "tie" or winner == "tie (bothbad)": + sa = 0.5 + else: + raise Exception(f"unexpected vote {winner}") + rating[model_a] += K * (sa - ea) + rating[model_b] += K * (1 - sa - eb) + + return dict(rating) + + +def get_bootstrap_result(battles, func_compute_elo, num_round=1000): + rows = [] + for i in tqdm(range(num_round), desc="bootstrap"): + tmp_battles = battles.sample(frac=1.0, replace=True) + rows.append(func_compute_elo(tmp_battles)) + df = pd.DataFrame(rows) + return df[df.median().sort_values(ascending=False).index] + + +def compute_elo_mle_with_tie(df, SCALE=400, BASE=10, INIT_RATING=1000): + from sklearn.linear_model import LogisticRegression + + models = pd.concat([df["model_a"], df["model_b"]]).unique() + models = pd.Series(np.arange(len(models)), index=models) + + # duplicate battles + df = pd.concat([df, df], ignore_index=True) + p = len(models.index) + n = df.shape[0] + + X = np.zeros([n, p]) + X[np.arange(n), models[df["model_a"]]] = +math.log(BASE) + X[np.arange(n), models[df["model_b"]]] = -math.log(BASE) + + # one A win => two A win + Y = np.zeros(n) + Y[df["winner"] == "model_a"] = 1.0 + + # one tie => one A win + one B win + # find tie + tie (both bad) index + tie_idx = (df["winner"] == "tie") | (df["winner"] == "tie (bothbad)") + tie_idx[len(tie_idx) // 2 :] = False + Y[tie_idx] = 1.0 + + lr = LogisticRegression(fit_intercept=False) + lr.fit(X, Y) + + elo_scores = SCALE * lr.coef_[0] + INIT_RATING + # calibrate llama-13b to 800 if applicable + if "llama-13b" in models.index: + elo_scores += 800 - elo_scores[models["llama-13b"]] + return pd.Series(elo_scores, index=models.index).sort_values(ascending=False) + + +def get_median_elo_from_bootstrap(bootstrap_df): + median = dict(bootstrap_df.quantile(0.5)) + median = {k: int(v + 0.5) for k, v in median.items()} + return median + + +def compute_pairwise_win_fraction(battles, model_order, limit_show_number=None): + # Times each model wins as Model A + a_win_ptbl = pd.pivot_table( + battles[battles["winner"] == "model_a"], + index="model_a", + columns="model_b", + aggfunc="size", + fill_value=0, + ) + + # Table counting times each model wins as Model B + b_win_ptbl = pd.pivot_table( + battles[battles["winner"] == "model_b"], + index="model_a", + columns="model_b", + aggfunc="size", + fill_value=0, + ) + + # Table counting number of A-B pairs + num_battles_ptbl = pd.pivot_table( + battles, index="model_a", columns="model_b", aggfunc="size", fill_value=0 + ) + + # Computing the proportion of wins for each model as A and as B + # against all other models + row_beats_col_freq = (a_win_ptbl + b_win_ptbl.T) / ( + num_battles_ptbl + num_battles_ptbl.T + ) + + if model_order is None: + prop_wins = row_beats_col_freq.mean(axis=1).sort_values(ascending=False) + model_order = list(prop_wins.keys()) + + if limit_show_number is not None: + model_order = model_order[:limit_show_number] + + # Arrange ordering according to proprition of wins + row_beats_col = row_beats_col_freq.loc[model_order, model_order] + return row_beats_col + + +def visualize_leaderboard_table(rating): + models = list(rating.keys()) + models.sort(key=lambda k: -rating[k]) + + emoji_dict = { + 1: "πŸ₯‡", + 2: "πŸ₯ˆ", + 3: "πŸ₯‰", + } + + md = "" + md += "| Rank | Model | Elo Rating | Description |\n" + md += "| --- | --- | --- | --- |\n" + for i, model in enumerate(models): + rank = i + 1 + minfo = get_model_info(model) + emoji = emoji_dict.get(rank, "") + md += f"| {rank} | {emoji} [{model}]({minfo.link}) | {rating[model]:.0f} | {minfo.description} |\n" + + return md + + +def visualize_pairwise_win_fraction(battles, model_order): + row_beats_col = compute_pairwise_win_fraction(battles, model_order) + fig = px.imshow( + row_beats_col, + color_continuous_scale="RdBu", + text_auto=".2f", + height=700, + width=700, + ) + fig.update_layout( + xaxis_title="Model B", + yaxis_title="Model A", + xaxis_side="top", + title_y=0.07, + title_x=0.5, + ) + fig.update_traces( + hovertemplate="Model A: %{y}
Model B: %{x}
Fraction of A Wins: %{z}" + ) + + return fig + + +def visualize_battle_count(battles, model_order): + ptbl = pd.pivot_table( + battles, index="model_a", columns="model_b", aggfunc="size", fill_value=0 + ) + battle_counts = ptbl + ptbl.T + fig = px.imshow( + battle_counts.loc[model_order, model_order], + text_auto=True, + height=700, + width=700, + ) + fig.update_layout( + xaxis_title="Model B", + yaxis_title="Model A", + xaxis_side="top", + title_y=0.07, + title_x=0.5, + ) + fig.update_traces( + hovertemplate="Model A: %{y}
Model B: %{x}
Count: %{z}" + ) + return fig + + +def visualize_average_win_rate(battles, limit_show_number): + row_beats_col_freq = compute_pairwise_win_fraction( + battles, None, limit_show_number=limit_show_number + ) + fig = px.bar( + row_beats_col_freq.mean(axis=1).sort_values(ascending=False), + text_auto=".2f", + height=500, + width=700, + ) + fig.update_layout( + yaxis_title="Average Win Rate", xaxis_title="Model", showlegend=False + ) + return fig + + +def visualize_bootstrap_elo_rating(df, df_final, limit_show_number): + bars = ( + pd.DataFrame( + dict( + lower=df.quantile(0.025), + rating=df_final, + upper=df.quantile(0.975), + ) + ) + .reset_index(names="model") + .sort_values("rating", ascending=False) + ) + bars = bars[:limit_show_number] + bars["error_y"] = bars["upper"] - bars["rating"] + bars["error_y_minus"] = bars["rating"] - bars["lower"] + bars["rating_rounded"] = np.round(bars["rating"], 2) + fig = px.scatter( + bars, + x="model", + y="rating", + error_y="error_y", + error_y_minus="error_y_minus", + text="rating_rounded", + height=500, + width=700, + ) + fig.update_layout(xaxis_title="Model", yaxis_title="Rating") + return fig + + +def report_elo_analysis_results(battles_json, rating_system="bt", num_bootstrap=100, anony_only=True): + battles = pd.DataFrame(battles_json) + battles = battles.sort_values(ascending=True, by=["tstamp"]) + # Only use anonymous votes + if anony_only: + battles = battles[battles["anony"]].reset_index(drop=True) + battles_no_ties = battles[~battles["winner"].str.contains("tie")] + + # Online update + elo_rating_online = compute_elo(battles) + + if rating_system == "bt": + bootstrap_df = get_bootstrap_result( + battles, compute_elo_mle_with_tie, num_round=num_bootstrap + ) + elo_rating_final = compute_elo_mle_with_tie(battles) + elif rating_system == "elo": + bootstrap_df = get_bootstrap_result( + battles, compute_elo, num_round=num_bootstrap + ) + elo_rating_median = get_median_elo_from_bootstrap(bootstrap_df) + elo_rating_final = elo_rating_median + + model_order = list(elo_rating_final.keys()) + model_order.sort(key=lambda k: -elo_rating_final[k]) + + limit_show_number = 25 # limit show number to make plots smaller + model_order = model_order[:limit_show_number] + + # leaderboard_table_df: elo rating, variance, 95% interval, number of battles + leaderboard_table_df = pd.DataFrame( + { + "rating": elo_rating_final, + "variance": bootstrap_df.var(), + "rating_q975": bootstrap_df.quantile(0.975), + "rating_q025": bootstrap_df.quantile(0.025), + "num_battles": battles["model_a"].value_counts() + + battles["model_b"].value_counts(), + } + ) + + # Plots + leaderboard_table = visualize_leaderboard_table(elo_rating_final) + win_fraction_heatmap = visualize_pairwise_win_fraction(battles_no_ties, model_order) + battle_count_heatmap = visualize_battle_count(battles_no_ties, model_order) + average_win_rate_bar = visualize_average_win_rate( + battles_no_ties, limit_show_number + ) + bootstrap_elo_rating = visualize_bootstrap_elo_rating( + bootstrap_df, elo_rating_final, limit_show_number + ) + + last_updated_tstamp = battles["tstamp"].max() + last_updated_datetime = datetime.datetime.fromtimestamp( + last_updated_tstamp, tz=timezone("US/Pacific") + ).strftime("%Y-%m-%d %H:%M:%S %Z") + + return { + "rating_system": rating_system, + "elo_rating_online": elo_rating_online, + "elo_rating_final": elo_rating_final, + "leaderboard_table": leaderboard_table, + "win_fraction_heatmap": win_fraction_heatmap, + "battle_count_heatmap": battle_count_heatmap, + "average_win_rate_bar": average_win_rate_bar, + "bootstrap_elo_rating": bootstrap_elo_rating, + "last_updated_datetime": last_updated_datetime, + "last_updated_tstamp": last_updated_tstamp, + "bootstrap_df": bootstrap_df, + "leaderboard_table_df": leaderboard_table_df, + } + + +def pretty_print_elo_rating(rating): + model_order = list(rating.keys()) + model_order.sort(key=lambda k: -rating[k]) + for i, model in enumerate(model_order): + print(f"{i+1:2d}, {model:25s}, {rating[model]:.0f}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--clean-battle-file", type=str) + parser.add_argument("--max-num-files", type=int) + parser.add_argument("--num-bootstrap", type=int, default=100) + parser.add_argument( + "--rating-system", type=str, choices=["bt", "elo"], default="bt" + ) + parser.add_argument("--exclude-tie", action="store_true", default=False) + args = parser.parse_args() + + np.random.seed(42) + + if args.clean_battle_file: + # Read data from a cleaned battle files + battles = pd.read_json(args.clean_battle_file) + else: + # Read data from all log files + log_files = get_log_files(args.max_num_files) + battles = clean_battle_data(log_files) + + anony_results = report_elo_analysis_results( + battles, rating_system=args.rating_system, num_bootstrap=args.num_bootstrap, anony_only=True + ) + full_results = report_elo_analysis_results( + battles, rating_system=args.rating_system, num_bootstrap=args.num_bootstrap, anony_only=False + ) + + + print("# Online Elo") + pretty_print_elo_rating(anony_results["elo_rating_online"]) + print("# Median") + pretty_print_elo_rating(anony_results["elo_rating_final"]) + print(f"last update : {anony_results['last_updated_datetime']}") + + last_updated_tstamp = anony_results["last_updated_tstamp"] + cutoff_date = datetime.datetime.fromtimestamp( + last_updated_tstamp, tz=timezone("US/Pacific") + ).strftime("%Y%m%d") + + + results = { + "anony": anony_results, + "full": full_results, + } + with open(f"elo_results_{cutoff_date}.pkl", "wb") as fout: + pickle.dump(results, fout) diff --git a/arena_elo/elo_rating/generate_leaderboard.py b/arena_elo/elo_rating/generate_leaderboard.py new file mode 100644 index 0000000000000000000000000000000000000000..6ef235d8f6c04f594d72183779f541f77e23c890 --- /dev/null +++ b/arena_elo/elo_rating/generate_leaderboard.py @@ -0,0 +1,71 @@ +import fire +import json +import pandas as pd +import pickle + + +def main( + model_info_file: str, + elo_rating_pkl: str, + output_csv: str +): + model_info = json.load(open(model_info_file)) + + with open(elo_rating_pkl, "rb") as fin: + elo_rating_results = pickle.load(fin) + + anony_elo_rating_results = elo_rating_results["anony"] + full_elo_rating_results = elo_rating_results["full"] + anony_leaderboard_data = anony_elo_rating_results["leaderboard_table_df"] + full_leaderboard_data = full_elo_rating_results["leaderboard_table_df"] + + # Model,MT-bench (score),Arena Elo rating,MMLU,License,Link + fields = ["key", "Model", "Arena Elo rating (anony)", "Arena Elo rating (full)", "License", "Organization", "Link"] + # set Organization and license to empty for now + all_models = anony_leaderboard_data.index.tolist() + + for model in all_models: + if not model in model_info: + model_info[model] = {} + model_info[model]["License"] = "N/A" + model_info[model]["Organization"] = "N/A" + model_info[model]["Link"] = "N/A" + model_info[model]["Model"] = model + model_info[model]["key"] = model + + if model in anony_leaderboard_data.index: + model_info[model]["Arena Elo rating (anony)"] = anony_leaderboard_data.loc[model, "rating"] + else: + model_info[model]["Arena Elo rating (anony)"] = 0 + + if model in full_elo_rating_results["leaderboard_table_df"].index: + model_info[model]["Arena Elo rating (full)"] = full_leaderboard_data.loc[model, "rating"] + else: + model_info[model]["Arena Elo rating (full)"] = 0 + # if model in anony_leaderboard_data.index: + # model_info[model]["Arena Elo rating"] = anony_leaderboard_data.loc[model, "rating"] + # else: + # model_info[model]["Arena Elo rating"] = 0 + + final_model_info = {} + for model in model_info: + if "Model" in model_info[model]: + final_model_info[model] = model_info[model] + model_info = final_model_info + + exclude_keys = ['starting_from'] + for key in exclude_keys: + for model in model_info: + if key in model_info[model]: + del model_info[model][key] + df = pd.DataFrame(model_info).T + df = df[fields] + # sort by anony rating + df = df.sort_values(by=["Arena Elo rating (anony)"], ascending=False) + df.to_csv(output_csv, index=False) + print("Leaderboard data saved to", output_csv) + print(df) + + +if __name__ == "__main__": + fire.Fire(main) \ No newline at end of file diff --git a/arena_elo/elo_rating/inspect_conv_rating.py b/arena_elo/elo_rating/inspect_conv_rating.py new file mode 100644 index 0000000000000000000000000000000000000000..7a69f0b0d2042112f0cc2323deee89b225663079 --- /dev/null +++ b/arena_elo/elo_rating/inspect_conv_rating.py @@ -0,0 +1,234 @@ +import argparse +import code +import datetime +import json +import os +from pytz import timezone +import time + +import pandas as pd +from tqdm import tqdm +import csv + +import base64 +from icecream import ic +from openai import OpenAI + +# Function to encode the image +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +def get_log_files(max_num_files=None): + dates = [] + for month in [2, 3]: + for day in range(1, 32): + dates.append(f"2024-{month:02d}-{day:02d}") + + num_servers = 1 + filenames = [] + for d in dates: + for i in range(num_servers): + # name = os.path.expanduser(f"~/fastchat_logs/server{i}/{d}-conv.json") + name = os.path.expanduser(f"vision-arena-logs/{d}-conv.json") + if os.path.exists(name): + filenames.append(name) + max_num_files = max_num_files or len(filenames) + filenames = filenames[-max_num_files:] + return filenames + + +def pretty_print_conversation(messages): + for role, msg in messages: + print(f"[[{role}]]: {msg}") + + +def get_gpt4v_response(client, img_bs64=None, text_prompt="", use_vision=False): + if use_vision: + response = client.chat.completions.create( + model="gpt-4-vision-preview", + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": text_prompt}, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{img_bs64}" + } + }, + ], + } + ], + max_tokens=100, + ) + else: + response = client.chat.completions.create( + model="gpt-4-vision-preview", + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": text_prompt}, + ], + } + ], + max_tokens=100, + ) + return response.choices[0].message.content + +task_template_map = { + "image_caption": "Give me the semantic alignment score between the given image and the given caption: \"{generated_sentence}\" on a scale of 0-100. Only reply the score value.", + "vqa": "Rate the answer correctness regarding the question within the context of the given image on a scale of 0-100. Only reply the score value.", + "pair_rate_old": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"\n\n[System]\nGiven the instruction and the image, please compare the correctness of responses A and B. Reply with \"leftvote\" if you find A better, \"rightvote\" if B is better, \"bothbad_vote\" if both responses are wrong, and \"tievote\" if both responses are equally satisfactory. If you are unable to make a decision, please reply with \"NA\".", + "pair_rate_wexplanation": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"[System]\nPlease act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user’s instructions and answers the user’s question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: \"[[A]]\" if assistant A is better, \"[[B]]\" if assistant B is better, and \"[[C]]\" for a tie.", + "pair_rate": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"\n\n[System]\nPlease act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user’s instructions and answers the user’s question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. Reply with \"leftvote\" if you find assistant A better, \"rightvote\" if assistant B is better, \"bothbad_vote\" if both responses are wrong, and \"tievote\" if both assistants provide equally satisfactory answers. If you are unable to make a decision, please reply with \"NA\"." +} + +def inspect_convs(log_files): + ic(log_files) + data = [] + total_vote = 0 + correct_vote = 0 + + client = OpenAI() + with open('all_pairvote_log_wgpt_prtchatbot.csv', 'w', newline='') as csvfile: + # fieldnames = ['tstamp', 'type', 'model_1', 'model_2', 'template_name_1', 'template_name_2', 'system_message_1', 'system_message_2', 'role_1', 'role_2', 'instruction_1', 'instruction_2', 'message_1', 'message_2', 'offset_1', 'offset_2', 'conv_id_1', 'conv_id_2', 'model_name_1', 'model_name_2', 'ip'] + fieldnames = ['tstamp', 'type', 'models', 'states', 'ip', 'gpt_vote'] + writer = csv.DictWriter(csvfile, fieldnames=fieldnames) + + # Write the header + writer.writeheader() + + for filename in tqdm(log_files, desc="read files"): + for retry in range(5): + try: + lines = open(filename).readlines() + break + except FileNotFoundError: + time.sleep(2) + + for l in lines: + row = json.loads(l) + + if "states" not in row: + continue + if row["type"] not in ["leftvote", "rightvote", "bothbad_vote", "tievote"]: + continue + + model_names = row["states"][0]["model_name"], row["states"][1]["model_name"] + + + # Iterate through each state and write the relevant information + if not len(row["states"][0]['messages']): continue + # ic(row["states"][0]['messages'][1][1]) + + if row["states"][0]['messages'][1][1] is None or row["states"][1]['messages'][1][1] is None or "NETWORK ERROR" in row["states"][0]['messages'][1][1] or "NETWORK ERROR" in row["states"][1]['messages'][1][1]: continue + total_vote += 1 + # row = { + # 'tstamp': row['tstamp'], + # 'type': row['type'], + # 'model_1': row['models'][0], + # 'model_2': row['models'][1], + # 'template_name_1': row["states"][0]['template_name'], + # 'system_message_1': row["states"][0]['system_message'], + # 'template_name_2': row["states"][1]['template_name'], + # 'system_message_2': row["states"][1]['system_message'], + # 'role_1': row["states"][0]['roles'], + # 'role_2': row["states"][1]['roles'], + # 'instruction_1': row["states"][0]['messages'][0][1], + # 'instruction_2': row["states"][1]['messages'][0][1], + # 'message_1': row["states"][0]['messages'][1][1], + # 'message_2': row["states"][1]['messages'][1][1], + # 'offset_1': row["states"][0]['offset'], + # 'offset_2': row["states"][1]['offset'], + # 'conv_id_1': row["states"][0]['conv_id'], + # 'conv_id_2': row["states"][1]['conv_id'], + # 'model_name_1': row["states"][0]['model_name'], + # 'model_name_2': row["states"][1]['model_name'], + # 'ip': row['ip'] + # } + # writer.writerow(row) + # Convert complex objects to JSON strings + # TODO: check two image are the same + conv_id = row["states"][0]['conv_id'] + image_path = os.path.join("/local/home/yujielu/project/Arena-Elo/vision-arena-logs", os.path.basename(filename)[:-5]+"input_images", f"input_image_{conv_id}.png") + if not os.path.exists(image_path): + response = "NA" + ic(image_path) + else: + base64_image = encode_image(image_path) + left_response = row["states"][0]['messages'][1][1] + right_response = row["states"][1]['messages'][1][1] + sep = "-" * 20 + instruction = row["states"][0]['messages'][0][1] + generated_sentence = f"[The Start of Assistant A’s Answer]\n{left_response}\n[The End of Assistant A’s Answer]\n\n[The Start of Assistant B’s Answer]\n{right_response}\n[The End of Assistant B’s Answer]" + text_prompt = task_template_map["pair_rate"].format(instruction=instruction, generated_sentence=generated_sentence) + # ic(text_prompt) + try: + response = get_gpt4v_response(client, img_bs64=base64_image, text_prompt=text_prompt, use_vision=True) + except: + ic(">>> skip") + response = "NA" + + # response = get_gpt4v_response(client, img_bs64=base64_image, text_prompt=text_prompt, use_vision=True) + ic(row['type'], response) + if response.strip() not in ["leftvote", "rightvote", "bothbad_vote", "tievote"]: + response = "NA" + # ic(generated_sentence) + + # if row['type'] == "leftvote": + # row['type'] = "A" + # elif row['type'] == "rightvote": + # row['type'] = "B" + # elif row['type'] in ["bothbad_vote", "tievote"]: + # row['type'] = "C" + if row['type'] == response.strip(): + correct_vote += 1 + row['models'] = json.dumps(row['models']) + row['states'] = json.dumps(row['states'], ensure_ascii=False) + row['gpt_vote'] = response + + # Write the modified row to the CSV file + writer.writerow(row) + # if row["type"] == "leftvote": + # winner, loser = model_names[0], model_names[1] + # winner_conv, loser_conv = row["states"][0], row["states"][1] + # elif row["type"] == "rightvote": + # loser, winner = model_names[0], model_names[1] + # loser_conv, winner_conv = row["states"][0], row["states"][1] + + # if loser == "llava-v1.5-13b" and winner == "llava-v1.5-13b": + # print("=" * 20) + # print(f"Winner: {winner}") + # pretty_print_conversation(winner_conv["messages"]) + # print(f"Loser: {loser}") + # pretty_print_conversation(loser_conv["messages"]) + # print("=" * 20) + # input() + # if row['type'] == 'bothbad_vote': + # from icecream import ic + # ic(model_names) + # if row["type"] == "bothbad_vote" and "gpt-4-vision-preview" in model_names: + # print("=" * 20) + # print(f"Model A: {model_names[0]}") + # pretty_print_conversation(row["states"][0]["messages"]) + # print(f"Model B: {model_names[1]}") + # pretty_print_conversation(row["states"][1]["messages"]) + # print("=" * 20) + # input() + # if correct_vote >= 300: break + ic(total_vote, correct_vote) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--max-num-files", type=int) + args = parser.parse_args() + + log_files = get_log_files(args.max_num_files) + + + + inspect_convs(log_files) diff --git a/arena_elo/elo_rating/inspect_cost.py b/arena_elo/elo_rating/inspect_cost.py new file mode 100644 index 0000000000000000000000000000000000000000..d071ca3ac4e9d7eb8e0559bb7e8fa50b1799e6cf --- /dev/null +++ b/arena_elo/elo_rating/inspect_cost.py @@ -0,0 +1,177 @@ +import fire +import time +import json +from collections import defaultdict +from .basic_stats import get_log_files, NUM_SERVERS, LOG_ROOT_DIR +from .utils import detect_language, get_time_stamp_from_date, get_input_image_path, load_image_from_path +from tqdm import tqdm +VOTES = ["tievote", "leftvote", "rightvote", "bothbad_vote", "chat"] + + +def remove_html(raw): + if raw.startswith("

"): + return raw[raw.find(": ") + 2 : -len("

\n")] + if raw.startswith("### Model A: ") or raw.startswith("### Model B: "): + return raw[13:] + return raw + + +def read_file(filename): + data = [] + for retry in range(5): + try: + # lines = open(filename).readlines() + for l in open(filename): + row = json.loads(l) + if row["type"] in VOTES: + data.append(row) + break + except FileNotFoundError: + time.sleep(2) + return data + + +def read_file_parallel(log_files, num_threads=16): + data_all = [] + from multiprocessing import Pool + + with Pool(num_threads) as p: + ret_all = list(tqdm(p.imap(read_file, log_files), total=len(log_files))) + for ret in ret_all: + data_all.extend(ret) + return data_all + +def num_tokens(s:str): + if s is None: + return 0 + return len(s) / 4 + +def main( +): + log_files = get_log_files() + data = read_file_parallel(log_files) + + all_model_counts = defaultdict(int) + all_model_input_tokens_counts = defaultdict(list) + all_model_output_tokens_counts = defaultdict(list) + all_model_image_sizes = defaultdict(list) + chat_battle_counts = defaultdict(int) + for row in tqdm(data, desc="counting"): + if row['type'] == "chat": + chat_battle_counts["chat"] += 1 + all_model_counts[row['model']] += 1 + tstamp = row["tstamp"] + conv_id = row["state"]["conv_id"] + + image = load_image_from_path(get_input_image_path(tstamp, conv_id)) + if image is None: + image_size = None + else: + image_size = load_image_from_path(get_input_image_path(tstamp, conv_id)).size + all_model_image_sizes[row['model']].append(image_size) + try: + for message in row["state"]["messages"][row["state"]["offset"] :: 2]: + all_model_input_tokens_counts[row['model']].append(num_tokens(message[1])) + for message in row["state"]["messages"][row["state"]["offset"] + 1 :: 2]: + all_model_output_tokens_counts[row['model']].append(num_tokens(message[1])) + except Exception as e: + print(row) + raise e + + else: + chat_battle_counts[row['type']] += 1 + if row["models"][0] is None or row["models"][1] is None: + continue + + # Resolve model names + models_public = [remove_html(row["models"][0]), remove_html(row["models"][1])] + if "model_name" in row["states"][0]: + models_hidden = [ + row["states"][0]["model_name"], + row["states"][1]["model_name"], + ] + if models_hidden[0] is None: + models_hidden = models_public + else: + models_hidden = models_public + + if (models_public[0] == "" and models_public[1] != "") or ( + models_public[1] == "" and models_public[0] != "" + ): + continue + + if models_public[0] == "" or models_public[0] == "Model A": + anony = True + models = models_hidden + else: + anony = False + models = models_public + if not models_public == models_hidden: + continue + + all_model_counts[models[0]] += 1 + all_model_counts[models[1]] += 1 + tstamp = row["tstamp"] + conv_id1 = row["states"][0]["conv_id"] + conv_id2 = row["states"][1]["conv_id"] + + image1 = load_image_from_path(get_input_image_path(tstamp, conv_id1)) + image2 = load_image_from_path(get_input_image_path(tstamp, conv_id2)) + all_model_image_sizes[models[0]].append(None if image1 is None else image1.size) + all_model_image_sizes[models[1]].append(None if image2 is None else image2.size) + + for message in row["states"][0]["messages"][row["states"][0]["offset"] :: 2]: + all_model_input_tokens_counts[models[0]].append(num_tokens(message[1])) + for message in row["states"][0]["messages"][row["states"][0]["offset"] + 1 :: 2]: + all_model_output_tokens_counts[models[0]].append(num_tokens(message[1])) + for message in row["states"][1]["messages"][row["states"][1]["offset"] :: 2]: + all_model_input_tokens_counts[models[1]].append(num_tokens(message[1])) + for message in row["states"][1]["messages"][row["states"][1]["offset"] + 1 :: 2]: + all_model_output_tokens_counts[models[1]].append(num_tokens(message[1])) + + print("### Chat battle counts (requests)") + print(json.dumps(chat_battle_counts, indent=4)) + + print("### Model counts (requests)") + print(json.dumps(all_model_counts, indent=4)) + + print("### Model Avg input tokens counts (tokens)") + average_input_tokens_counts = {} + for model, counts in all_model_input_tokens_counts.items(): + average_input_tokens_counts[model] = sum(counts) / len(counts) + print(json.dumps(average_input_tokens_counts, indent=4)) + + print("### Model AVg output tokens counts (tokens)") + average_output_tokens_counts = {} + for model, counts in all_model_output_tokens_counts.items(): + average_output_tokens_counts[model] = sum(counts) / len(counts) + print(json.dumps(average_output_tokens_counts, indent=4)) + + print("### Model Avg image sizes (height, width)") + average_image_sizes = {} + for model, sizes in all_model_image_sizes.items(): + avg_height = sum([size[0] for size in sizes if size is not None]) / len(sizes) + avg_width = sum([size[1] for size in sizes if size is not None]) / len(sizes) + average_image_sizes[model] = (avg_height, avg_width) + print(json.dumps(average_image_sizes, indent=4)) + + print("### GPT-4V estimated cost (USD)") + gpt_4v_name = "gpt-4-vision-preview" + gpt_4v_cost = {} + gpt_4v_cost['input'] = sum(all_model_input_tokens_counts[gpt_4v_name]) / 1000 * 0.01 + gpt_4v_cost['output'] = sum(all_model_output_tokens_counts[gpt_4v_name]) / 1000 * 0.03 + + all_image_cost = 0 + for size in all_model_image_sizes[gpt_4v_name]: + if size is None: + continue + all_image_tokens = (size[0] // 512 + 1) * (size[1] // 512 + 1) * 170 + 85 + all_image_cost += all_image_tokens / 1000 * 0.01 + gpt_4v_cost['image'] = all_image_cost + print(json.dumps(gpt_4v_cost, indent=4)) + + + + +if __name__ == "__main__": + fire.Fire(main) \ No newline at end of file diff --git a/arena_elo/elo_rating/inspect_elo_rating_pkl.py b/arena_elo/elo_rating/inspect_elo_rating_pkl.py new file mode 100644 index 0000000000000000000000000000000000000000..b6eda95f9a330338dee8108ba7d0a1a459035f6f --- /dev/null +++ b/arena_elo/elo_rating/inspect_elo_rating_pkl.py @@ -0,0 +1,33 @@ +import pickle +import plotly.graph_objects as go + +def output_figure(data, figure_name="battle_count_heatmap", label="annoy"): + fig = data[label][figure_name] + fig.update_layout( + height=700, + width=700, + title={'text': f'{figure_name}', 'x': 0.5, 'y': 0.07}, + xaxis_title="Model B", + yaxis_title="Model A", + # coloraxis_colorscale=[[0.0, '#0d0887'], [1.0, '#f0f921']], + margin={'t': 60} + ) + fig.write_image(f"{figure_name}.png") + +with open("./results/latest/elo_results.pkl",'rb') as f: + data = pickle.load(f) + print() + df = data["anony"]["leaderboard_table_df"] + # sort by rating + print(data["anony"].keys()) + + for figure_name in [ 'win_fraction_heatmap', 'battle_count_heatmap',]: + output_figure(data, figure_name, "anony") + + df = df.sort_values(by=["rating"], ascending=False) + print(df) + df = data["full"]["leaderboard_table_df"] + # sort by rating + df = df.sort_values(by=["rating"], ascending=False) + print(df) + print('done') diff --git a/arena_elo/elo_rating/model_registry.py b/arena_elo/elo_rating/model_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..7635ba884847f95ccebd0f7af688a2ca25c37695 --- /dev/null +++ b/arena_elo/elo_rating/model_registry.py @@ -0,0 +1,578 @@ +"""Additional information of the models.""" +from collections import namedtuple, OrderedDict +from typing import List + + +ModelInfo = namedtuple("ModelInfo", ["simple_name", "link", "description"]) + + +model_info = OrderedDict() + + +def register_model_info( + full_names: List[str], simple_name: str, link: str, description: str +): + info = ModelInfo(simple_name, link, description) + + for full_name in full_names: + model_info[full_name] = info + + +def get_model_info(name: str) -> ModelInfo: + if name in model_info: + return model_info[name] + else: + # To fix this, please use `register_model_info` to register your model + return ModelInfo( + name, "", "Register the description at arena.model/model_registry.py" + ) + + +register_model_info( + [ + "IEITYuan/Yuan2-2B-Janus-hf", + "IEITYuan/Yuan2-2B-hf", + "IEITYuan/Yuan2-51B-hf", + "IEITYuan/Yuan2-102B-hf", + ], + "IEIT-Yuan2", + "https://github.com/IEIT-Yuan/Yuan-2.0", + "Yuan2.0 is a new generation Fundamental Large Language Model developed by IEIT System.", +) + +register_model_info( + ["mixtral-8x7b-instruct-v0.1", "mistral-7b-instruct"], + "Mixtral of experts", + "https://mistral.ai/news/mixtral-of-experts/", + "A Mixture-of-Experts model by Mistral AI", +) + +register_model_info( + ["gemini-pro"], + "Gemini", + "https://blog.google/technology/ai/google-gemini-pro-imagen-duet-ai-update/", + "Gemini by Google", +) + +register_model_info( + ["gemini-pro-vision"], + "Gemini", + "https://blog.google/technology/ai/google-gemini-pro-imagen-duet-ai-update/", + "Gemini by Google", +) + +register_model_info( + ["solar-10.7b-instruct-v1.0"], + "SOLAR-10.7B-Instruct", + "https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0", + "A model trained using depth up-scaling by Upstage AI", +) + +register_model_info( + ["gpt-4-turbo"], + "GPT-4-Turbo", + "https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo", + "GPT-4-Turbo by OpenAI", +) + +register_model_info( + ["gpt-4-vision-preview"], + "gpt-4-vision-preview", + "https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo", + "GPT-4(V) by OpenAI", +) + +register_model_info( + ["gpt-3.5-turbo", "gpt-3.5-turbo-0314", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-1106"], + "GPT-3.5", + "https://platform.openai.com/docs/models/gpt-3-5", + "GPT-3.5-Turbo by OpenAI", +) + +register_model_info( + ["gpt-4", "gpt-4-0314", "gpt-4-0613"], + "GPT-4", + "https://openai.com/research/gpt-4", + "GPT-4 by OpenAI", +) + +register_model_info( + ["claude-2.1", "claude-2.0"], + "Claude", + "https://www.anthropic.com/index/claude-2", + "Claude 2 by Anthropic", +) + +register_model_info( + ["claude-1"], + "Claude", + "https://www.anthropic.com/index/introducing-claude", + "Claude 1 by Anthropic", +) + +register_model_info( + ["claude-instant-1", "claude-instant-1.2"], + "Claude Instant", + "https://www.anthropic.com/index/introducing-claude", + "Claude Instant by Anthropic", +) + +register_model_info( + ["pplx-70b-online", "pplx-7b-online"], + "pplx-online-llms", + "https://blog.perplexity.ai/blog/introducing-pplx-online-llms", + "Online LLM API by Perplexity AI", +) + +register_model_info( + ["openhermes-2.5-mistral-7b"], + "OpenHermes-2.5-Mistral-7B", + "https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B", + "a mistral-based model fine-tuned on 1M GPT-4 outputs", +) + +register_model_info( + ["starling-lm-7b-alpha"], + "Starling-LM-7B-alpha", + "https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha", + "an open model trained using RLAIF by Berkeley", +) + +register_model_info( + ["tulu-2-dpo-70b"], + "Tulu 2", + "https://huggingface.co/allenai/tulu-2-dpo-70b", + "an instruction and RLHF model by UW/AllenAI", +) + +register_model_info( + ["yi-34b-chat", "yi-6b-chat"], + "Yi-Chat", + "https://huggingface.co/01-ai/Yi-34B-Chat", + "A large language model by 01 AI", +) + +register_model_info( + ["llama-2-70b-chat", "llama-2-34b-chat", "llama-2-13b-chat", "llama-2-7b-chat"], + "Llama 2", + "https://ai.meta.com/llama/", + "open foundation and fine-tuned chat models by Meta", +) + +register_model_info( + [ + "vicuna-33b", + "vicuna-33b-v1.3", + "vicuna-13b", + "vicuna-13b-v1.3", + "vicuna-7b", + "vicuna-7b-v1.3", + ], + "Vicuna", + "https://lmsys.org/blog/2023-03-30-vicuna/", + "a chat assistant fine-tuned on user-shared conversations by LMSYS", +) + +register_model_info( + ["chatglm3-6b", "chatglm2-6b", "chatglm-6b"], + "ChatGLM", + "https://chatglm.cn/blog", + "an open bilingual dialogue language model by Tsinghua University", +) + +register_model_info( + ["openchat-3.5"], + "OpenChat 3.5", + "https://github.com/imoneoi/openchat", + "an open model fine-tuned on Mistral-7B using C-RLFT", +) + +register_model_info( + ["tenyxchat-7b-v1"], + "TenyxChat-7B", + "https://huggingface.co/tenyx/TenyxChat-7B-v1", + "an open model DPO trained on top of OpenChat-3.5 using Tenyx fine-tuning", +) + +register_model_info( + ["zephyr-7b-beta", "zephyr-7b-alpha"], + "Zephyr", + "https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha", + "a chatbot fine-tuned from Mistral by Hugging Face", +) + +register_model_info( + ["notus-7b-v1"], + "Notus", + "https://huggingface.co/argilla/notus-7b-v1", + "a chatbot fine-tuned from Zephyr SFT by Argilla", +) + +register_model_info( + ["catppt"], + "CatPPT", + "https://huggingface.co/rishiraj/CatPPT", + "a chatbot fine-tuned from a SLERP merged model by Rishiraj Acharya", +) + +register_model_info( + ["TinyLlama"], + "TinyLlama", + "https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.", +) + +register_model_info( + ["qwen-14b-chat"], + "Qwen", + "https://huggingface.co/Qwen/Qwen-14B-Chat", + "a large language model by Alibaba Cloud", +) + +register_model_info( + ["codellama-34b-instruct", "codellama-13b-instruct", "codellama-7b-instruct"], + "Code Llama", + "https://ai.meta.com/blog/code-llama-large-language-model-coding/", + "open foundation models for code by Meta", +) + +register_model_info( + ["wizardlm-70b", "wizardlm-30b", "wizardlm-13b"], + "WizardLM", + "https://github.com/nlpxucan/WizardLM", + "an instruction-following LLM using evol-instruct by Microsoft", +) + +register_model_info( + ["wizardcoder-15b-v1.0"], + "WizardLM", + "https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder", + "Empowering Code Large Language Models with Evol-Instruct", +) + +register_model_info( + ["mpt-7b-chat", "mpt-30b-chat"], + "MPT-Chat", + "https://www.mosaicml.com/blog/mpt-30b", + "a chatbot fine-tuned from MPT by MosaicML", +) + +register_model_info( + ["guanaco-33b", "guanaco-65b"], + "Guanaco", + "https://github.com/artidoro/qlora", + "a model fine-tuned with QLoRA by UW", +) + +register_model_info( + ["gpt4all-13b-snoozy"], + "GPT4All-Snoozy", + "https://github.com/nomic-ai/gpt4all", + "a finetuned LLaMA model on assistant style data by Nomic AI", +) + +register_model_info( + ["koala-13b"], + "Koala", + "https://bair.berkeley.edu/blog/2023/04/03/koala", + "a dialogue model for academic research by BAIR", +) + +register_model_info( + ["RWKV-4-Raven-14B"], + "RWKV-4-Raven", + "https://huggingface.co/BlinkDL/rwkv-4-raven", + "an RNN with transformer-level LLM performance", +) + +register_model_info( + ["alpaca-13b"], + "Alpaca", + "https://crfm.stanford.edu/2023/03/13/alpaca.html", + "a model fine-tuned from LLaMA on instruction-following demonstrations by Stanford", +) + +register_model_info( + ["oasst-pythia-12b"], + "OpenAssistant (oasst)", + "https://open-assistant.io", + "an Open Assistant for everyone by LAION", +) + +register_model_info( + ["oasst-sft-7-llama-30b"], + "OpenAssistant (oasst)", + "https://open-assistant.io", + "an Open Assistant for everyone by LAION", +) + +register_model_info( + ["palm-2"], + "PaLM 2 Chat", + "https://cloud.google.com/vertex-ai/docs/release-notes#May_10_2023", + "PaLM 2 for Chat (chat-bison@001) by Google", +) + +register_model_info( + ["llama-7b", "llama-13b"], + "LLaMA", + "https://arxiv.org/abs/2302.13971", + "open and efficient foundation language models by Meta", +) + +register_model_info( + ["open-llama-7b-v2-open-instruct", "open-llama-7b-open-instruct"], + "Open LLaMa (Open Instruct)", + "https://medium.com/vmware-data-ml-blog/starter-llm-for-the-enterprise-instruction-tuning-openllama-7b-d05fc3bbaccc", + "Open LLaMa fine-tuned on instruction-following data by VMware", +) + +register_model_info( + ["dolly-v2-12b"], + "Dolly", + "https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm", + "an instruction-tuned open large language model by Databricks", +) + +register_model_info( + ["stablelm-tuned-alpha-7b"], + "StableLM", + "https://github.com/stability-AI/stableLM", + "Stability AI language models", +) + +register_model_info( + ["codet5p-6b"], + "CodeT5p-6b", + "https://huggingface.co/Salesforce/codet5p-6b", + "Code completion model released by Salesforce", +) + +register_model_info( + ["fastchat-t5-3b", "fastchat-t5-3b-v1.0"], + "FastChat-T5", + "https://huggingface.co/lmsys/fastchat-t5-3b-v1.0", + "a chat assistant fine-tuned from FLAN-T5 by LMSYS", +) + +register_model_info( + ["phoenix-inst-chat-7b"], + "Phoenix-7B", + "https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b", + "a multilingual chat assistant fine-tuned from Bloomz to democratize ChatGPT across languages by CUHK(SZ)", +) + +register_model_info( + ["realm-7b-v1"], + "ReaLM", + "https://github.com/FreedomIntelligence/ReaLM", + "A chatbot fine-tuned from LLaMA2 with data generated via iterative calls to UserGPT and ChatGPT by CUHK(SZ) and SRIBD.", +) + +register_model_info( + ["billa-7b-sft"], + "BiLLa-7B-SFT", + "https://huggingface.co/Neutralzz/BiLLa-7B-SFT", + "an instruction-tuned bilingual LLaMA with enhanced reasoning ability by an independent researcher", +) + +register_model_info( + ["h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2"], + "h2oGPT-GM-7b", + "https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2", + "an instruction-tuned OpenLLaMA with enhanced conversational ability by H2O.ai", +) + +register_model_info( + ["baize-v2-7b", "baize-v2-13b"], + "Baize v2", + "https://github.com/project-baize/baize-chatbot#v2", + "A chatbot fine-tuned from LLaMA with ChatGPT self-chat data and Self-Disillation with Feedback (SDF) by UCSD and SYSU.", +) + +register_model_info( + [ + "airoboros-l2-7b-2.1", + "airoboros-l2-13b-2.1", + "airoboros-c34b-2.1", + "airoboros-l2-70b-2.1", + ], + "airoboros", + "https://huggingface.co/jondurbin/airoboros-l2-70b-2.1", + "an instruction-tuned LlaMa model tuned with 100% synthetic instruction-response pairs from GPT4", +) + +register_model_info( + [ + "spicyboros-7b-2.2", + "spicyboros-13b-2.2", + "spicyboros-70b-2.2", + ], + "spicyboros", + "https://huggingface.co/jondurbin/spicyboros-70b-2.2", + "de-aligned versions of the airoboros models", +) + +register_model_info( + ["Robin-7b-v2", "Robin-13b-v2", "Robin-33b-v2"], + "Robin-v2", + "https://huggingface.co/OptimalScale/robin-7b-v2-delta", + "A chatbot fine-tuned from LLaMA-7b, achieving competitive performance on chitchat, commonsense reasoning and instruction-following tasks, by OptimalScale, HKUST.", +) + +register_model_info( + ["manticore-13b-chat"], + "Manticore 13B Chat", + "https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg", + "A chatbot fine-tuned from LlaMa across several CoT and chat datasets.", +) + +register_model_info( + ["redpajama-incite-7b-chat"], + "RedPajama-INCITE-7B-Chat", + "https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Chat", + "A chatbot fine-tuned from RedPajama-INCITE-7B-Base by Together", +) + +register_model_info( + [ + "falcon-7b", + "falcon-7b-instruct", + "falcon-40b", + "falcon-40b-instruct", + "falcon-180b", + "falcon-180b-chat", + ], + "Falcon", + "https://huggingface.co/tiiuae/falcon-180B", + "TII's flagship series of large language models", +) + +register_model_info( + ["tigerbot-7b-sft"], + "Tigerbot", + "https://huggingface.co/TigerResearch/tigerbot-7b-sft", + "TigerBot is a large-scale language model (LLM) with multiple languages and tasks.", +) + +register_model_info( + ["internlm-chat-7b", "internlm-chat-7b-8k"], + "InternLM", + "https://huggingface.co/internlm/internlm-chat-7b", + "InternLM is a multi-language large-scale language model (LLM), developed by SHLAB.", +) + +register_model_info( + ["Qwen-7B-Chat"], + "Qwen", + "https://huggingface.co/Qwen/Qwen-7B-Chat", + "Qwen is a multi-language large-scale language model (LLM), developed by Damo Academy.", +) + +register_model_info( + ["Llama2-Chinese-13b-Chat", "LLama2-Chinese-13B"], + "Llama2-Chinese", + "https://huggingface.co/FlagAlpha/Llama2-Chinese-13b-Chat", + "Llama2-Chinese is a multi-language large-scale language model (LLM), developed by FlagAlpha.", +) + +register_model_info( + ["Chinese-Alpaca-2-7B", "Chinese-Alpaca-2-13B"], + "Chinese-Alpaca", + "https://huggingface.co/hfl/chinese-alpaca-2-13b", + "New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.", +) + +register_model_info( + ["Vigogne-2-7B-Instruct", "Vigogne-2-13B-Instruct"], + "Vigogne-Instruct", + "https://huggingface.co/bofenghuang/vigogne-2-7b-instruct", + "Vigogne-Instruct is a French large language model (LLM) optimized for instruction-following, developed by Bofeng Huang", +) + +register_model_info( + ["Vigogne-2-7B-Chat", "Vigogne-2-13B-Chat"], + "Vigogne-Chat", + "https://huggingface.co/bofenghuang/vigogne-2-7b-chat", + "Vigogne-Chat is a French large language model (LLM) optimized for instruction-following and multi-turn dialogues, developed by Bofeng Huang", +) + +register_model_info( + ["stable-vicuna-13B-HF"], + "stable-vicuna", + "https://huggingface.co/TheBloke/stable-vicuna-13B-HF", + "StableVicuna is a Vicuna model fine-tuned using RLHF via PPO on various conversational and instructional datasets.", +) + +register_model_info( + ["deluxe-chat-v1", "deluxe-chat-v1.1", "deluxe-chat-v1.2"], + "DeluxeChat", + "", + "Deluxe Chat", +) + +register_model_info( + [ + "Xwin-LM-7B-V0.1", + "Xwin-LM-13B-V0.1", + "Xwin-LM-70B-V0.1", + "Xwin-LM-7B-V0.2", + "Xwin-LM-13B-V0.2", + ], + "Xwin-LM", + "https://github.com/Xwin-LM/Xwin-LM", + "Chat models developed by Xwin-LM team", +) + +register_model_info( + ["lemur-70b-chat"], + "Lemur-Chat", + "https://huggingface.co/OpenLemur/lemur-70b-chat-v1", + "an openly accessible language model optimized for both natural language and coding capabilities ", +) + +register_model_info( + ["Mistral-7B-OpenOrca"], + "Open-Orca", + "https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca", + "A fine-tune of [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) using [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca)", +) + +register_model_info( + ["dolphin-2.2.1-mistral-7b"], + "dolphin-mistral", + "https://huggingface.co/ehartford/dolphin-2.2.1-mistral-7b", + "An uncensored fine-tuned Mistral 7B", +) + +register_model_info( + [ + "AquilaChat-7B", + "AquilaChat2-7B", + "AquilaChat2-34B", + ], + "Aquila-Chat", + "https://huggingface.co/BAAI/AquilaChat2-34B", + "Chat models developed by BAAI team", +) + +register_model_info( + ["xDAN-L1-Chat-RL-v1"], + "xDAN-L1-Chat", + "https://huggingface.co/xDAN-AI/xDAN-L1-Chat-RL-v1", + "A large language chat model created by xDAN-AI.", +) + +register_model_info( + ["MetaMath-70B-V1.0", "MetaMath-7B-V1.0"], + "MetaMath", + "https://huggingface.co/meta-math", + "MetaMath is a finetune of Llama2 on [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) that specializes in mathematical reasoning.", +) + +register_model_info( + ["Yuan2-2B-hf", "Yuan2-51B-hf", "Yuan2-102B-hf"], + "IEIYuan", + "https://huggingface.co/IEITYuan", + "Yuan2 is a Basemodel developed by IEI.", +) diff --git a/arena_elo/elo_rating/upload_battle_data.py b/arena_elo/elo_rating/upload_battle_data.py new file mode 100644 index 0000000000000000000000000000000000000000..2ca5f242eea33bf5e266b26a8c40a91db1106e02 --- /dev/null +++ b/arena_elo/elo_rating/upload_battle_data.py @@ -0,0 +1,193 @@ +import fire +import json +import os +import datasets +import datetime +from pathlib import Path +from datetime import datetime +from PIL import Image + +datasets.config.DEFAULT_MAX_BATCH_SIZE = 500 +def create_hf_dataset(data_file: str, split="test"): + hf_dataset = datasets.Dataset.from_list( + data_file, + features=datasets.Features( + { + "question_id": datasets.Value("string"), + "model": datasets.Value("string"), + "conversation": [ + { + "role": datasets.Value("string"), + "content": datasets.Value("string"), + } + ], + "language": datasets.Value("string"), + "image": datasets.Image(), + "turn": datasets.Value("int32"), + } + ), + split=split, + ) + return hf_dataset + +def create_hf_battle_dataset(data_file: str, split="test"): + hf_dataset = datasets.Dataset.from_list( + data_file, + features=datasets.Features( + { + "question_id": datasets.Value("string"), + "model_a": datasets.Value("string"), + "model_b": datasets.Value("string"), + "conversation_a": [ + { + "role": datasets.Value("string"), + "content": datasets.Value("string"), + } + ], + "conversation_b": [ + { + "role": datasets.Value("string"), + "content": datasets.Value("string"), + } + ], + "language": datasets.Value("string"), + "image": datasets.Image(), + "turn": datasets.Value("int32"), + "anony": datasets.Value("bool"), + } + ), + split=split, + ) + return hf_dataset + + + + +def load_image(path:str): + try: + return Image.open(path) + except Exception as e: + print(f"Error loading image {path}: {e}") + return None + +def get_date_from_time_stamp(unix_timestamp: int): + # Create a datetime object from the Unix timestamp + dt = datetime.fromtimestamp(unix_timestamp) + + # Convert the datetime object to a string with the desired format + date_str = dt.strftime("%Y-%m-%d") + return date_str + +def load_battle_image(battle, log_dir): + image_path = Path(log_dir) / f"{get_date_from_time_stamp(battle['tstamp'])}-convinput_images" / f"input_image_{battle['question_id']}.png" + return load_image(image_path) + + +def main( + data_file: str = "./results/latest/clean_battle_conv.json", + repo_id: str = "DongfuTingle/wildvision-bench", + log_dir: str = os.getenv("LOGDIR", "./vision-arena-logs/"), + mode="battle", + token = os.environ.get("HUGGINGFACE_TOKEN", None) +): + with open(data_file, "r") as f: + data = json.load(f) + + + + has_image_stats = { + "has_image": 0, + "no_image": 0, + } + if mode == "keep_bad_only": + # anony only + data = [d for d in data if d["anony"]] + + new_data = [] + for battle in data: + image = load_battle_image(battle, log_dir) + if image is None: + has_image_stats["no_image"] += 1 + # we don't keep the data without image + continue + has_image_stats["has_image"] += 1 + + if battle["winner"] in ["model_a", "model_b"]: + if battle["winner"] == "model_a": + worse_model = "model_b" + worse_conv = "conversation_b" + if battle["winner"] == "model_b": + worse_model = "model_a" + worse_conv = "conversation_a" + + new_data.append({ + "question_id": battle["question_id"], + "model": battle[worse_model], + "conversation": battle[worse_conv], + "language": battle["language"], + "image": image, + "turn": battle["turn"], + }) + elif battle["winner"] == "tie (bothbad)": + + new_data.append({ + "question_id": battle["question_id"], + "model": battle["model_a"], + "conversation": battle["conversation_a"], + "language": battle["language"], + "image": image, + "turn": battle["turn"], + }) + + new_data.append({ + "question_id": battle["question_id"], + "model": battle["model_b"], + "conversation": battle["conversation_b"], + "language": battle["language"], + "image": image, + "turn": battle["turn"], + }) + + split = "test" + hf_dataset = create_hf_dataset(new_data, "test") + + elif mode == "battle": + new_data = [] + for battle in data: + image = load_battle_image(battle, log_dir) + if image is None: + has_image_stats["no_image"] += 1 + continue + has_image_stats["has_image"] += 1 + new_data.append({ + "question_id": battle["question_id"], + "model_a": battle["model_a"], + "model_b": battle["model_b"], + "conversation_a": battle["conversation_a"], + "conversation_b": battle["conversation_b"], + "language": battle["language"], + "image": image, + "turn": battle["turn"], + "anony": battle["anony"], + }) + split = "test" + hf_dataset = create_hf_battle_dataset(new_data, "test") + else: + raise ValueError(f"Invalid mode: {mode}") + + print(f"Stats: {has_image_stats}") + print(hf_dataset) + print(f"Uploading to part {repo_id}:{split}...") + hf_dataset.push_to_hub( + repo_id=repo_id, + config_name=mode, + split=split, + token=token, + commit_message=f"Add vision-arena {split} dataset", + ) + + print("Done!") + + +if __name__ == "__main__": + fire.Fire(main) \ No newline at end of file diff --git a/arena_elo/elo_rating/utils.py b/arena_elo/elo_rating/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..250a1d97bcc5f7ba175e4e57a4b7becafb06ad4b --- /dev/null +++ b/arena_elo/elo_rating/utils.py @@ -0,0 +1,83 @@ +from datetime import datetime +import pytz +import PIL +import os + +def detect_language(text: str) -> str: + """Detect the langauge of a string.""" + import polyglot # pip3 install polyglot pyicu pycld2 + from polyglot.detect import Detector + from polyglot.detect.base import logger as polyglot_logger + import pycld2 + + polyglot_logger.setLevel("ERROR") + + try: + lang_code = Detector(text).language.name + except (pycld2.error, polyglot.detect.base.UnknownLanguage): + lang_code = "unknown" + return lang_code + + +def get_time_stamp_from_date(date_str:str): + """ + Convert a date string to a Unix timestamp + Args: + date_str (str): The input date string in the format 'YYYY-MM-DD-HH:MM-TZ', e.g. '2024-02-10-14:00-PT' + """ + + # Convert the date string into a format that Python's datetime can understand + # and specify the correct timezone for PT, which is 'US/Pacific' + date_format = "%Y-%m-%d-%H:%M-%Z" + + # Parse the date string into a datetime object + # Note: PT is not directly recognized by pytz, so we manually map it to 'US/Pacific' + timezone_map = { + "PT": "US/Pacific", + } + + # Extract the timezone abbreviation + tz_abbr = date_str.split("-")[-1] + # Map the abbreviation to a pytz timezone + tz_info = pytz.timezone(timezone_map[tz_abbr]) + + # Remove the timezone abbreviation for parsing + date_str_parsed = date_str.rsplit("-", 1)[0] + + # Create a datetime object with the corresponding timezone + dt = datetime.strptime(date_str_parsed, "%Y-%m-%d-%H:%M").replace(tzinfo=tz_info) + + # Convert the datetime object to a Unix timestamp + unix_timestamp = dt.timestamp() + return unix_timestamp + +def get_date_from_time_stamp(unix_timestamp: int): + # Create a datetime object from the Unix timestamp + dt = datetime.fromtimestamp(unix_timestamp) + + # Convert the datetime object to a string with the desired format + date_str = dt.strftime("%Y-%m-%d %H:%M:%S %Z") + return date_str + + +def get_input_image_path(tstamp, conv_id): + # from tstamp to date e.g. 2024-02-10 + date_str = datetime.fromtimestamp(tstamp, tz=pytz.timezone("US/Pacific")).strftime("%Y-%m-%d") + LOGDIR = os.getenv("LOGDIR") + return f"{LOGDIR}/{date_str}-convinput_images/input_image_{conv_id}.png" + +def load_image_from_path(image_path): + # Load the image from the specified + # path using the Python Imaging Library (PIL) + try: + image = PIL.Image.open(image_path) + return image + except FileNotFoundError: + print(f"Image not found at path: {image_path}") + return None + except PIL.UnidentifiedImageError: + print(f"Unidentified image format at path: {image_path}") + return None + + + \ No newline at end of file diff --git a/arena_elo/evaluator/convert_to_evaluator_data.py b/arena_elo/evaluator/convert_to_evaluator_data.py new file mode 100644 index 0000000000000000000000000000000000000000..83b04b2ad962b5e63cc682c20378bd1d1ea68e8a --- /dev/null +++ b/arena_elo/evaluator/convert_to_evaluator_data.py @@ -0,0 +1,134 @@ +import argparse +import json +import os +import time +from pytz import timezone +from tqdm import tqdm +import base64 +from icecream import ic +from PIL import Image + + +# Function to encode the image +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +def get_log_files(max_num_files=None): + dates = [] + for month in [2, 3]: + for day in range(1, 32): + dates.append(f"2024-{month:02d}-{day:02d}") + + num_servers = 1 + filenames = [] + for d in dates: + for i in range(num_servers): + # name = os.path.expanduser(f"~/fastchat_logs/server{i}/{d}-conv.json") + name = os.path.expanduser(f"vision-arena-logs/{d}-conv.json") + if os.path.exists(name): + filenames.append(name) + max_num_files = max_num_files or len(filenames) + filenames = filenames[-max_num_files:] + return filenames + + +def pretty_print_conversation(messages): + for role, msg in messages: + print(f"[[{role}]]: {msg}") + +task_template_map = { + "image_caption": "Give me the semantic alignment score between the given image and the given caption: \"{generated_sentence}\" on a scale of 0-100. Only reply the score value.", + "vqa": "Rate the answer correctness regarding the question within the context of the given image on a scale of 0-100. Only reply the score value.", + "pair_rate_old": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"\n\n[System]\nGiven the instruction and the image, please compare the correctness of responses A and B. Reply with \"leftvote\" if you find A better, \"rightvote\" if B is better, \"bothbad_vote\" if both responses are wrong, and \"tievote\" if both responses are equally satisfactory. If you are unable to make a decision, please reply with \"NA\".", + "pair_rate_wexplanation": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"[System]\nPlease act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user’s instructions and answers the user’s question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: \"[[A]]\" if assistant A is better, \"[[B]]\" if assistant B is better, and \"[[C]]\" for a tie.", + "pair_rate": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"\n\n[System]\nPlease act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user’s instructions and answers the user’s question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. Reply with \"leftvote\" if you find assistant A better, \"rightvote\" if assistant B is better, \"bothbad_vote\" if both responses are wrong, and \"tievote\" if both assistants provide equally satisfactory answers. If you are unable to make a decision, please reply with \"NA\"." +} + +def inspect_convs(log_files): + json_data = [] + + ic(log_files) + total_vote = 0 + + for filename in tqdm(log_files, desc="read files"): + for retry in range(5): + try: + lines = open(filename).readlines() + break + except FileNotFoundError: + time.sleep(2) + + for l in lines: + row = json.loads(l) + + if "states" not in row: + continue + if row["type"] not in ["leftvote", "rightvote", "bothbad_vote", "tievote"]: + continue + + model_names = row["states"][0]["model_name"], row["states"][1]["model_name"] + + + # Iterate through each state and write the relevant information + if not len(row["states"][0]['messages']): continue + # ic(row["states"][0]['messages'][1][1]) + + if row["states"][0]['messages'][1][1] is None or row["states"][1]['messages'][1][1] is None or "NETWORK ERROR" in row["states"][0]['messages'][1][1] or "NETWORK ERROR" in row["states"][1]['messages'][1][1]: continue + total_vote += 1 + + conv_id = row["states"][0]['conv_id'] + image_path = os.path.join("/local/home/yujielu/project/Arena-Elo/vision-arena-logs", os.path.basename(filename)[:-5]+"input_images", f"input_image_{conv_id}.png") + if not os.path.exists(image_path) : + continue + try: + image = Image.open(image_path).convert("RGB") + except: + continue + + left_response = row["states"][0]['messages'][1][1] + right_response = row["states"][1]['messages'][1][1] + instruction = row["states"][0]['messages'][0][1] + generated_sentence = f"[The Start of Assistant A’s Answer]\n{left_response}\n[The End of Assistant A’s Answer]\n\n[The Start of Assistant B’s Answer]\n{right_response}\n[The End of Assistant B’s Answer]" + text_prompt = task_template_map["pair_rate"].format(instruction=instruction, generated_sentence=generated_sentence) + + user_input = text_prompt + # Create the conversation structure + conversation = [ + { + "from": "human", + "value": user_input + }, + { + "from": "gpt", + "value": row["type"] + } + ] + + # Create the JSON object for each row + json_obj = { + "id": conv_id, + "image": image_path, + "conversations": conversation + } + + # Append the JSON object to the list + json_data.append(json_obj) + + # Write the JSON data to a file + with open('output_evaluator_data.json', 'w') as json_file: + json.dump(json_data, json_file, indent=2) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--max-num-files", type=int) + args = parser.parse_args() + + log_files = get_log_files(args.max_num_files) + + + + inspect_convs(log_files) + + + diff --git a/arena_elo/evaluator/rating_analysis.ipynb b/arena_elo/evaluator/rating_analysis.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e1d574c04ee5e4896929e933ff35b1c02a309d8f --- /dev/null +++ b/arena_elo/evaluator/rating_analysis.ipynb @@ -0,0 +1,321 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1338\n", + "1044\n" + ] + } + ], + "source": [ + "\n", + "import pandas as pd\n", + "import json\n", + "\n", + "# Replace 'your_file_name.csv' with the path to your CSV file\n", + "file_name = 'all_pairvote_log_wgpt.csv'\n", + "\n", + "# Load the CSV file into a DataFrame\n", + "df = pd.read_csv(file_name)\n", + "\n", + "# Define a function to parse JSON data\n", + "def parse_json(data):\n", + " try:\n", + " # Parse the JSON data\n", + " return json.loads(data)\n", + " except ValueError as e:\n", + " # Return None or an empty dictionary if the data cannot be parsed\n", + " return None\n", + "\n", + "# Apply the parse_json function to the 'models' and 'states' columns\n", + "df['models'] = df['models'].apply(parse_json)\n", + "df['states'] = df['states'].apply(parse_json)\n", + "# row[\"states\"][0]['messages'][0][1]\n", + "\n", + "# Now df contains the parsed JSON data in the 'models' and 'states' columns\n", + "# print(df.head())\n", + "print(len(df))\n", + "# filter_vote_df = df[df[\"gpt_vote\"].isin([\"leftvote\", \"rightvote\"])]#, \"tievote\", \"bothbad_vote\"\n", + "# \\#1\n", + "filter_vote_df = df[df[\"gpt_vote\"].isin([\"leftvote\", \"rightvote\", \"tievote\", \"bothbad_vote\"])]\n", + "# \\#2\n", + "# filter_vote_df = df\n", + "filter_vote_df.loc[~filter_vote_df[\"gpt_vote\"].isin([\"leftvote\", \"rightvote\"]), \"gpt_vote\"] = \"tie\"\n", + "filter_vote_df.loc[~filter_vote_df[\"type\"].isin([\"leftvote\", \"rightvote\"]), \"type\"] = \"tie\"\n", + "# \\#3\n", + "#[df[\"gpt_vote\"].isin([\"leftvote\", \"rightvote\"]) & df[\"type\"].isin([\"leftvote\", \"rightvote\"])]\n", + "filtered_df = filter_vote_df[filter_vote_df[\"states\"].apply(lambda x: len(x[0]['messages'][0][1]) > 10)]\n", + "print(len(filtered_df))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix:\n", + "[[300 61 34]\n", + " [102 269 27]\n", + " [ 99 111 41]]\n", + "\n", + "Accuracy: 0.5842911877394636\n" + ] + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "from sklearn.metrics import confusion_matrix, accuracy_score\n", + "import pandas as pd\n", + "\n", + "# Assuming df is your DataFrame\n", + "\n", + "# True labels\n", + "y_true = filtered_df[\"type\"]\n", + "\n", + "# Predictions\n", + "y_pred = filtered_df[\"gpt_vote\"]\n", + "\n", + "# Compute the confusion matrix\n", + "# conf_matrix = confusion_matrix(y_true, y_pred, labels=[\"leftvote\", \"rightvote\", \"tievote\", \"bothbad_vote\"])\n", + "conf_matrix = confusion_matrix(y_true, y_pred, labels=[\"leftvote\", \"rightvote\", \"tie\"])\n", + "\n", + "# Compute the accuracy\n", + "accuracy = accuracy_score(y_true, y_pred)\n", + "\n", + "print(\"Confusion Matrix:\")\n", + "print(conf_matrix)\n", + "\n", + "print(\"\\nAccuracy:\", accuracy)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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S87O6kQoVKqh06dIO0xO/+uor5c6d235Nj3T1WqE///xThQsXVtWqVTVy5Mjbfmi9Fgbi4+NTbJsyZYp+/vlnffnllzc9ft68efr555+1YsUK7du3T3/++afuv//+Wz7n9eLj43Xs2DH749r1aXFxcRoyZIgGDhyYqmubnn32Wbm7u9uvX7l48aIWLFigJ5544rbvkYCAAElXQ3tq/P333ypZsqRDmJD+t7re9X8UkNL+7+6MQYMGyd/fX1WrVlXJkiXVq1cvrV279pbHXKszLCzMod3Ly0vFixdP8ToKFSqUYjpjUFCQ06+jdevW2rVrl7Zt26ZZs2bp2Wefvek0yVOnTqlv377KmzevfH197SFcSv3vDinl77pb8fLy0tSpUxUdHa1z585p2rRpN60PwN3DNUYAUu3XX39VTEyM5syZozlz5qTYHhkZqccff9yh7fq/xkpXRz0kacCAATe8EFpSiqW/bzbn3hgjSTpz5oxq1aqlgIAAjR49WqGhofLx8dHmzZs1aNAg+3Oml5t9gLnVRdM3eg23e12S9OWXX6pDhw5q0qSJBg4cqODgYLm7u2vs2LH2a22c7fNmWrZsqddff10nT55U9uzZ9d1336lVq1YOSw23aNFCDz/8sBYsWKClS5fq7bff1ptvvqn58+ff9BqQwMBA5c+fX3/++WeKbdeuObrVDUFr1qyZIkA6Y/z48Ro1apT9+6JFi+rgwYMaP368EhMT1bJlS/vzHzlyRNLVQHHw4EEVKFDAPmJ3baGJefPmafLkyfr+++917tw5tWnT5rY1lC5dWpK0Y8eONL+OW7mTf/dbvZ+v77dMmTKKiorSokWLtGTJEs2bN08ffvihhg8f7vDzvRN38jqu9+CDDyo0NFQvvviioqOjb3kNVIsWLbRu3ToNHDhQFStWlL+/v5KTk1W/fn2nfnf893fd7fz000+SrgbsvXv3OhWsAGQMghGAVIuMjFRwcPANpzvNnz9fCxYs0Mcff3zLDwjFixeXdHVaUHrdS2XFihWKjY3V/Pnz7YsWSOm7atn1rv01/r+rav33r9/pYe7cuSpevLjmz5/v8AH22uhaemrZsqVGjRqlefPmKW/evIqLi9Ozzz6bYr/8+fOrZ8+e6tmzp44fP67KlSvr9ddfv+XF8Q0bNtRnn32mjRs3qmrVqule+620a9dODz30kP37a+/PQ4cO6fTp0ypbtmyKY9544w298cYb2rJli8MCF23atNGSJUv0448/atasWQoICNBTTz112xpKlSqlsLAwLVy4UO++++4NpxVer2jRotq+fbuSk5MdRo2uTQv9772e7kRQUNANb1r6999/28/Xa7Jly6aWLVuqZcuWSkxMVLNmzfT6669ryJAh8vHxueHrkKSoqCiHvhITExUdHZ1h91OSpFatWmnMmDEqU6ZMikVKrjl9+rSWLVumUaNGOdyjau/evSn2Tc8Rne3bt2v06NHq2LGjtm7dqi5dumjHjh0KDAxMt+cA4Dym0gFIlYSEBM2fP19PPvmknnnmmRSP3r1769y5cymW3P6v4OBgPfLII5oyZYpiYmJSbP/vMtypce2vzNf/VTkxMVEffvih032lRkBAgHLnzu1wrYmkDHm+G722DRs26Lfffkv35ypTpozKlSunr776Sl999ZXy58/vEDSTkpJSTC0KDg5WgQIFdOnSpVv2/fLLL8vPz0+dOnXSv//+m2K7syMCzihevLjD1MgaNWpIkvr06aMFCxY4PKZMmSJJ6tChgxYsWJDir/hNmjSRn5+fPvzwQ/34449q1qzZDQPBjYwaNUqxsbHq0qWLrly5kmL70qVL7cuiN2jQQMeOHXOY2njlyhW9//778vf3V61atdL0s7iR0NBQrV+/XomJifa2RYsW6fDhww77Xb98tXR1Olh4eLiMMTddubFu3bry8vLSe++95/Bv/Pnnn+vs2bNq2LBhur2O/+rSpYtGjBiRYgn8693o/JKkSZMmpdg3W7ZsklL+QcRZly9fVocOHVSgQAG9++67mj59uv7991/169fvjvoFcOcYMQKQKt99953OnTunRo0a3XB7tWrVlCdPHkVGRjpcrH8jkydP1kMPPaRy5cqpa9euKl68uP7991/99ttvOnLkiLZt2+ZUbREREQoKClL79u3Vp08f2Ww2zZw5M0M/bHfp0kXjxo1Tly5dVKVKFa1atUp//fVXuj/Pk08+qfnz56tp06Zq2LChoqOj9fHHHys8PPyG1+zcqZYtW2r48OHy8fFR586dHUYrzp07p0KFCumZZ55RhQoV5O/vr19++UWbNm265YdPSSpZsqRmzZqlVq1aKSwsTG3atFGFChVkjFF0dLRmzZolNzc3FSpUKN1f081Urlw5xRLj16bUlS1bVk2aNElxjL+/v5o0aWK/zig10+iuadmypXbs2KHXX39dW7ZsUatWrVS0aFHFxsZqyZIlWrZsmb3fbt26acqUKerQoYP++OMPhYSEaO7cuVq7dq0mTZqU6kUcUqNLly6aO3eu6tevrxYtWmj//v368ssvUyzn/fjjjytfvnyqUaOG8ubNq927d+uDDz5Qw4YNb1pPnjx5NGTIEI0aNUr169dXo0aNFBUVpQ8//FAPPPDATe8blR6KFi3qcE+xGwkICFDNmjX11ltv6fLlyypYsKCWLl16w9Hma9e1vfrqq3r22Wfl6empp556yh6YUmvMmDHaunWrli1bpuzZs6t8+fIaPny4hg4dqmeeeUYNGjRwqj8A6YdgBCBVIiMj5ePjo8cee+yG293c3NSwYUNFRkam+Mvyf4WHh+v333/XqFGjNH36dMXGxio4OFiVKlVymM6SWrly5dKiRYv00ksvaejQoQoKClLbtm1Vp06dm17HdKeGDx+uEydOaO7cufr666/1xBNP6Mcff1RwcHC6Pk+HDh107NgxTZkyRT/99JPCw8P15Zdf6ptvvtGKFSvS9bmkqx/ehw4dqgsXLqQIuH5+furZs6eWLl2q+fPnKzk5WSVKlNCHH36o559//rZ9N27cWDt27NCECRO0dOlSTZ06VTabTUWLFlXDhg3Vo0cPVahQId1fU3pr06aNZs2apfz58zssTJEaY8aMUe3atfXee+/po48+0qlTpxQUFKRq1app4cKF9j88+Pr6asWKFRo8eLBmzJihuLg4hYWFadq0aTe8GeudqFevniZMmKCJEyfqxRdfVJUqVezn0/W6d++uyMhITZw4UfHx8SpUqJD69OmjoUOH3rL/kSNHKk+ePPrggw/Ur18/5cyZU926ddMbb7xx03tr3U2zZs3SCy+8oMmTJ8sYo8cff1w//vijChQo4LDfAw88oNdee00ff/yxlixZouTkZEVHRzsVjDZv3qw33nhDvXv3tq/sKV29wfDChQvVtWtX7dy5Uzly5EivlwfACTaTkX9SBQAAAIAsgGuMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFjePXmDV99KvV1dAuByvy0c6+oSAJcrXSC7q0sAALiYTyoTDyNGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACwv0wSj/fv3a+jQoWrVqpWOHz8uSfrxxx+1c+dOF1cGAAAA4F6XKYLRypUrVa5cOW3YsEHz589XfHy8JGnbtm0aMWKEi6sDAAAAcK/LFMFo8ODBGjNmjH7++Wd5eXnZ22vXrq3169e7sDIAAAAAVpApgtGOHTvUtGnTFO3BwcE6efKkCyoCAAAAYCWZIhjlyJFDMTExKdq3bNmiggULuqAiAAAAAFaSKYLRs88+q0GDBunYsWOy2WxKTk7W2rVrNWDAALVr187V5QEAAAC4x2WKYPTGG2+odOnSKly4sOLj4xUeHq6aNWsqIiJCQ4cOdXV5AAAAAO5xNmOMcXUR1xw+fFg7duxQfHy8KlWqpJIlS6apH99KvdO5MiDr+W3hWFeXALhc6QLZXV0CAMDFfDxSt1+mGDEaPXq0Lly4oMKFC6tBgwZq0aKFSpYsqYSEBI0ePdrV5QEAAAC4x2WKESN3d3fFxMQoODjYoT02NlbBwcFKSkpyqj9GjABGjACJESMAQBYbMTLGyGazpWjftm2bcubM6YKKAAAAAFhJKvNTxggKCpLNZpPNZlOpUqUcwlFSUpLi4+PVo0cPF1YIAAAAwApcGowmTZokY4w6deqkUaNGKTAw0L7Ny8tLISEhql69ugsrBAAAAGAFLg1G7du3lyQVK1ZMERER8vT0dGU5AAAAACzKpcHomlq1aikpKUnz5s3T7t27JUlly5ZVo0aN5O7u7uLqAAAAANzrMkUw2rdvnxo0aKCjR48qLCxMkjR27FgVLlxYixcvVmhoqIsrBAAAAHAvyxSr0vXp00ehoaE6fPiwNm/erM2bN+vQoUMqVqyY+vTp4+ryAAAAANzjMsWI0cqVK7V+/XqHpblz5cqlcePGqUaNGi6sDAAAAIAVZIpg5O3trXPnzqVoj4+Pl5eXlwsqwn91bf6Quj7zsIoWuBpedx84pjc++VFL1+6SJHl7eWhc/2ZqXu9+eXt56JffdqvvG1/p+Kn//bsWzhekd19pqVpVSik+4ZIiv9+gYe9/p6SkZJe8JiA9nDp5XJGfva+tG9fp0qWLylegkJ4fMEKhYeGSpA2rf9Uvi+bpwN49ij93Vm9+FKmQEmEurhrIeHNmRWrGtM918uQJlQorrcGvDFO58uVdXRZwV3EeZC2ZYirdk08+qW7dumnDhg0yxsgYo/Xr16tHjx5q1KiRq8uDpKP/ntGw9xcqos1bqtHmba3Y+Je+eaebyhTPJ0l6a8DTaljzPrV5+XM93mWS8ucJ1JwJXezHu7nZNP+95+Xl6aFHO0xQ1+Ez1bbRgxr+fENXvSTgjsWfi9PwFzvL3d1DQ954VxM/+1rPde+nbNkD7PtcupigsPsqqnWXF1xYKXB3LfnxB41/a6y69+ylOd8sUFhYaT3fvbNiY2NdXRpw13AeZD2ZIhi99957Cg0NVfXq1eXj4yMfHx/VqFFDJUqU0Lvvvuvq8iDph1V/6qc1u7T/0AntO3RcIyd/r/gLl1S1fDEF+PuoQ5PqGjRxvlZu+ktbdh9WtxFfqnrFUFUtFyJJqlu9jMoUz6dOr87Q9r+OaunaXRr94WJ1b1FTnh6sPIis6buvZihXnrzqOXCESpS+T8H5C6pClWrKV6CQfZ+ajzXUM891VbnKVV1YKXB3zZwxTc2eaaEmTZ9WaIkSGjpilHx8fPTt/HmuLg24azgPsp5MEYxy5MihhQsXKioqSnPnztXcuXMVFRWlBQsWONz0FZmDm5tNzevdr2y+XtqwPVqVyhSRl6eHfl0fZd/nr4P/6lDMKT1Yvpgk6cHyxfTnvn8cptb9vG63ArP7Kjw0/11/DUB6+P23VSpeqowmjh6krs0f06AerbXshwWuLgtwqcuJidq9a6eqVY+wt7m5ualatQht37bFhZUBdw/nQdaUKa4xWrNmjR566CGVLFlSJUuWdOrYS5cu6dKlSw5tJjlJNjdGIdJb2RIFtGLGS/Lx8lB8wiW1fOlT7TlwTBVKFdKlxMs6G5/gsP/x2DjlzXV1SlHeXAE6Hut4HdnxU3FXt+UOkKIEZDnHY47q5+/nqeHTbdS0dUftj9qlaZPHy8PDU7Uef9LV5QEucfrMaSUlJSlXrlwO7bly5VJ09AEXVQXcXZwHWVOmGDGqXbu2ihUrpldeeUW7du1y6tixY8cqMDDQ4XHl3z8yqFJr++vgv3rw2bGq2W68Pv1mjT4d/ZxK//81RoAVJZtkFStZWq0691KxEqVVt2Ez1WnQRD8vYpoEAABZTaYIRv/8849eeuklrVy5Uvfdd58qVqyot99+W0eOHLntsUOGDNHZs2cdHh55778LVVvP5StJOnD4pLbsPqzh73+nHX8dVa9Wj+hYbJy8vTwV6O/rsH9wrgD9G3t1VOjf2DgF58ruuD3n1dGkf0/G3Z0XAKSzoJy5VbBIMYe2gkWK6eTxYy6qCHC9oBxBcnd3T3GBeWxsrHLnzu2iqoC7i/Mga8oUwSh37tzq3bu31q5dq/3796t58+aaMWOGQkJCVLt27Vse6+3trYCAAIcH0+juDjebTd5eHtqy+5ASL1/Row/+bwnikkWDVSR/Tm3YHi1J2rA9WveVKKA8Qf72fepUK62z5xK0+wAfIpE1hZWtoJgjfzu0xRz5W3nyct0crMvTy0tlwstqw/rf7G3JycnasOE3la9QyYWVAXcP50HWlCmuMbpesWLFNHjwYFWoUEHDhg3TypUrXV0SJI1+oZF+WrtTh2NOK3s2H7V8oopqVimpp3p+qLj4i5r+7W9686VmOnX2vM6dv6iJg5pr/bYD2rjjoCTpl992a/eBY/p8THu9+u63ypsrQCN6PakpX69S4uUrrn1xQBo1eLq1hvftpAWzpqp6rce0L2qnlv2wQF1ffNW+T3zcWZ08fkynY09Ikv75/yCVI2cu5cjJXw1xb3qufUcNe2WQypa9T/eVK68vZ85QQkKCmjRt5urSgLuG8yDrsRljjKuLuGbt2rWKjIzU3LlzdfHiRTVu3Fht2rRR/fr1nerHt1LvDKrQuj4a0VqPVg1TvtwBOht/UX/uPaoJ037Rrxv2SPrfDV5b1P//G7yu262+Y7/Sv9ctuFAkf5DefeVZ1by/pM5fvKTI7zdq6HsLucFrBvlt4VhXl2AJf6xfrdmff6BjRw8rT74CevKZNqrToKl9+4qfvtdH40elOO6Z57qqebvud7NUSypdIPvtd0KGmB35pf3GlmGly2jQK0NVvnwFV5cF3FWcB5mDTyqHgjJFMBoyZIjmzJmjf/75R4899pjatGmjxo0by8/PL039EYwAghEgEYwAAKkPRpliKt2qVas0cOBAtWjRggvSAAAAANx1mSIYjR07VhEREfLwcCznypUrWrdunWrWrOmiygAAAABYQaZYle7RRx/VqVOnUrSfPXtWjz76qAsqAgAAAGAlmSIYGWNks9lStMfGxipbtmwuqAgAAACAlbh0Kl2zZleXK7TZbOrQoYO8vb3t25KSkrR9+3ZFRES4qjwAAAAAFuHSYBQYGCjp6ohR9uzZ5evra9/m5eWlatWqqWvXrq4qDwAAAIBFuCwY9e/fXx988IGyZcumgwcP6rPPPpO/v7+rygEAAABgYS67xuj9999XfHy8pKvLdV+4cMFVpQAAAACwOJeNGIWEhOi9997T448/LmOMfvvtNwUFBd1wX5brBgAAAJCRbMYY44on/vbbb9WjRw8dP35cNptNNyvDZrMpKSnJqb59K/VOjxKBLO23hWNdXQLgcqULZHd1CQAAF/NJ5VCQy0aMmjRpoiZNmig+Pl4BAQGKiopScHCwq8oBAAAAYGEuXZVOkvz9/bV8+XIVK1ZMHh4uLwcAAACABWWKG7zWqlVLf//9t4YOHapWrVrp+PHjkqQff/xRO3fudHF1AAAAAO51mSIYrVy5UuXKldOGDRs0f/58+2p127Zt04gRI1xcHQAAAIB7XaYIRoMHD9aYMWP0888/y8vLy95eu3ZtrV+/3oWVAQAAALCCTBGMduzYoaZNm6ZoDw4O1smTJ11QEQAAAAAryRTBKEeOHIqJiUnRvmXLFhUsWNAFFQEAAACwkkwRjJ599lkNGjRIx44dk81mU3JystauXasBAwaoXbt2ri4PAAAAwD0uUwSjN954Q6VLl1bhwoUVHx+v8PBwPfzww4qIiNDQoUNdXR4AAACAe5zNGGNcXcQ1hw8f1o4dOxQfH69KlSqpZMmSaerHt1LvdK4MyHp+WzjW1SUALle6QHZXlwAAcDGfVN4q1WV3VO3fv/8tt1+/Gt3EiRMzuhwAAAAAFuayYLRly5ZU7Wez2TK4EgAAAABW57JgtHz5clc9NQAAAAA4yBSLLwAAAACAKxGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFiezRhjXF1Eevt66z+uLgFwufZD57u6BMDlTn/b29UlAABczMcjdfsxYgQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8ghGAAAAACyPYAQAAADA8jJNMFq9erXatm2r6tWr6+jRo5KkmTNnas2aNS6uDAAAAMC9LlMEo3nz5qlevXry9fXVli1bdOnSJUnS2bNn9cYbb7i4OgAAAAD3ukwRjMaMGaOPP/5Yn376qTw9Pe3tNWrU0ObNm11YGQAAAAAryBTBKCoqSjVr1kzRHhgYqDNnztz9ggAAAABYSqYIRvny5dO+fftStK9Zs0bFixd3QUUAAAAArCRTBKOuXbuqb9++2rBhg2w2m/755x9FRkZqwIABev75511dHgAAAIB7nIerC5CkwYMHKzk5WXXq1NGFCxdUs2ZNeXt7a8CAAXrhhRdcXR4AAACAe5zNGGNcXcQ1iYmJ2rdvn+Lj4xUeHi5/f/809fP11n/SuTIg62k/dL6rSwBc7vS3vV1dAgDAxXxSORSUKabSderUSefOnZOXl5fCw8NVtWpV+fv76/z58+rUqZOrywMAAABwj8sUwWjGjBlKSEhI0Z6QkKAvvvjCBRUBAAAAsBKXXmMUFxcnY4yMMTp37px8fHzs25KSkvTDDz8oODjYhRUCAAAAsAKXBqMcOXLIZrPJZrOpVKlSKbbbbDaNGjXKBZUBAAAAsBKXBqPly5fLGKPatWtr3rx5ypkzp32bl5eXihYtqgIFCriwQgAAAABW4NJgVKtWLUlSdHS0ChcuLDe3THHJEwAAAACLyRT3MSpatKjOnDmjzz//XLt375YklS1bVp06dVJgYKCLqwMAAABwr8sUQzS///67QkND9c477+jUqVM6deqUJk6cqNDQUG3evNnV5QEAAAC4x2WKEaN+/fqpUaNG+vTTT+XhcbWkK1euqEuXLnrxxRe1atUqF1cIAAAA4F6WKYLR77//7hCKJMnDw0Mvv/yyqlSp4sLKAAAAAFhBpphKFxAQoEOHDqVoP3z4sLJnz+6CigAAAABYSaYIRi1btlTnzp311Vdf6fDhwzp8+LDmzJmjLl26qFWrVq4uDwAAAMA9LlNMpRs/frxsNpvatWunK1euSJI8PT31/PPPa9y4cS6uDgAAAMC9zmaMMa4u4poLFy5o//79kqTQ0FD5+fmlqZ+vt/6TnmUBWVL7ofNdXQLgcqe/7e3qEgAALuaTyqGgTDFi9OWXX6pZs2by8/NTuXLlXF0ObuLgrm1a8/1X+if6L507HatWA15T+AMP2bcbY/TrN9P0+7LFung+XkXC7lOjLv2UK38hSdLp48e0Yv4XOvDnFsWfOaXsOXOrwkN1VatZW3l4eLrqZQGpNqD5/WpSvbhKFQpSQuIVbdh9TK9OX6e9R8847Pdg6Xwa+Vw1PRCWV0nJRtsPnNBTw7/TxcQkSVLF0Dwa06G67i95dfu36/Zr0GdrdP7iZRe8KiBjzJkVqRnTPtfJkydUKqy0Br8yTOXKl3d1WcBdxXmQtWSKa4z69eun4OBgtW7dWj/88IOSkpJcXRJuIPHSReUrGqonO/W94fbV383R+h/nq1GXfur++ofy8vHRjDde1uXEREnSyX8OySQbNe7aXy9MmKYn2vXUpl++1y+zP7ubLwNIs4fvK6CPF+9QrQFz9eSwhfLwcNOi1xrJz/t/f2N6sHQ+LRz1lJZtOaSH+3+jh/p9rY8X7VBy8tXB+fw5s2nxmMbaH3NWNV/6Ro1HfKfwIjn1ab86rnpZQLpb8uMPGv/WWHXv2UtzvlmgsLDSer57Z8XGxrq6NOCu4TzIejJFMIqJidGcOXNks9nUokUL5c+fX7169dK6detcXRquU6rSg6r7bGeFV304xTZjjH77Ya5qNXtOZR54SPmKhurpXkN07vRJ7d60RpJUsmJVNes5SCUqPKCceQuoTJUaeujJFtq1cfXdfilAmjQe8b2+XLZHuw+d0o7oWHV75xcVCQ5QpRLB9n3e6vKQPvx+u8bP3azdh05p79EzmrdmnxKvJEuSnnggRJevJOvFj1Zq79Ez+mPvcb0weYWa1iih4vkDXfXSgHQ1c8Y0NXumhZo0fVqhJUpo6IhR8vHx0bfz57m6NOCu4TzIejJFMPLw8NCTTz6pyMhIHT9+XO+8844OHjyoRx99VKGhoa4uD6lw+niM4s+cUmi5++1tPn7+KlSijA7v3XnT4y5eOC9ff5ZkR9YUkM1bknQ6/qIkKU+gr6qWzqcTZxK0/O2ndXBmJy0d21QR4fntx3h7uuvylSRdf3VnQuLVRWeu3w/Iqi4nJmr3rp2qVj3C3ubm5qZq1SK0fdsWF1YG3D2cB1lTpghG1/Pz81O9evX0xBNPqGTJkjp48KCrS0IqxJ85JUnyDwxyaM8WGGTf9l+xx45q/ZIFeqDuUxleH5DebDbp7a4Pa93Of7Tr76vv8WL5AiRJr7auqqk/7VLjEd9p6/4T+uH1JgotcHU0aMX2I8ob5Kd+zSrJ08NNObJ5a0yH6pKkfDmzuebFAOno9JnTSkpKUq5cuRzac+XKpZMnT7qoKuDu4jzImjJNMLpw4YIiIyPVoEEDFSxYUJMmTVLTpk21c+fNRxsk6dKlS4qLi3N4XE68dJeqRlrFnTqhL954WfdVq6UqdZ50dTmA0yY9X0tli+ZUu7d+sre52WySpM+X/KmZv+zWtgMn9fJna/TXkdNq/1i4JGn3oVPq+s4y9WlaUafm9dDBLzvp4LE4HTt9XiY50ywSCgCA5WSKVemeffZZLVq0SH5+fmrRooWGDRum6tWrp+rYsWPHatSoUQ5tz3Tvr+Y9XsqIUnET/jlySpLiz55W9qD//XXk/NnTyhdSwmHfuFMnNXV0fxUuVVaNuvHvhKznnR411eCBENUdPF9HY8/b22NOX/169yHHUdKow6dVOI+//fuvVv6lr1b+peAcvjp/8YqMMerTpKKij529Oy8AyEBBOYLk7u6e4gLz2NhY5c6d20VVAXcX50HWlClGjNzd3fX1118rJiZGH3zwQapDkSQNGTJEZ8+edXg06cR9K+62oOD88s+RUwd2bLa3XbxwXkf27VbhkmXtbXGnTmjq6H4qUKyUmvUcJDe3TPEWBFLtnR411ah6cdV/9Vv9/e85h21//3tO/8TGq1QhxymlJQrm0KHjjvtK0vEzCTp/8bKeqVlSFy8nadnWwxlaO3A3eHp5qUx4WW1Y/5u9LTk5WRs2/KbyFSq5sDLg7uE8yJoyxYhRvXr1VKdOHbm7uzu0JyYmas6cOWrXrt1Nj/X29pa3t7dDm6dXfIbUaXWXLibo1LGj9u/PHI9RzMF98vXPrhy586p6g2e0YsFM5cxfUEHB+bXsq6nKHpRbZf7/Xkdxp07o81H9lCN3XtV/rofOx/3vr+PZ/3/ECcjMJj1fSy1rlVLzMYsVf+Gy8ua4ehPqsxcu2e9R9M68LRrapqp2RJ/UtgMn1bZOaYUVClLrsT/a++nxZDmt331M8QmXVadSYb3RMULDZvyms+cTXfK6gPT2XPuOGvbKIJUte5/uK1deX86coYSEBDVp2szVpQF3DedB1mMzxrh8Uru7u7tiYmIUHBzs0B4bG6vg4GCn72v09dZ/0rM8/L/onVs1dXS/FO2VatVTs56D/3eD118W6eKFeBUJK6enOr+o3AUKS5I2r1iiBR+9ecO+X/tqeYbWbkXth853dQn3nIRFNx6N7vrOL/py2R779wOeqazuDcspKLuPdkSf1KvT1mndrhj79s/611X9KiHy9/VU1JHTmjR/i2Yvj8rw+q3o9LfMIHCV2ZFf2m9sGVa6jAa9MlTly1dwdVnAXcV5kDn4pHIoKE3BaPXq1ZoyZYr279+vuXPnqmDBgpo5c6aKFSumhx56yNnu5Obmpn///Vd58uRxaN+2bZseffRRnTp141XNboZgBBCMAIlgBABIfTByeirdvHnz9Nxzz6lNmzbasmWLLl26ugLc2bNn9cYbb+iHH35IdV+VKlWSzWaTzWZTnTp15OHxv3KSkpIUHR2t+vXrO1siAAAAADjF6WA0ZswYffzxx2rXrp3mzJljb69Ro4bGjBnjVF9NmjSRJG3dulX16tWTv///Vm3y8vJSSEiInn76aWdLBAAAAACnOB2MoqKiVLNmzRTtgYGBOnPmjFN9jRgxQpIUEhKili1bysfHx9lyAAAAAOCOOR2M8uXLp3379ikkJMShfc2aNSpevHiaimjfvr2kq6vQHT9+XMnJyQ7bixQpkqZ+AQAAACA1nA5GXbt2Vd++fTV16lTZbDb9888/+u233zRgwAANGzYsTUXs3btXnTp10rp16xzajTGy2WxOr0oHAAAAAM5wOhgNHjxYycnJqlOnji5cuKCaNWvK29tbAwYM0AsvvJCmIjp06CAPDw8tWrRI+fPnl81mS1M/AAAAAJAWab6PUWJiovbt26f4+HiFh4c7LJzgrGzZsumPP/5Q6dKl09zH9ViuG2C5bkBiuW4AQOqX63ZztuNOnTrp3Llz8vLyUnh4uKpWrSp/f3+dP39enTp1crY7SVJ4eLhOnjyZpmMBAAAA4E45HYxmzJihhISEFO0JCQn64osvUt1PXFyc/fHmm2/q5Zdf1ooVKxQbG+uwLS4uztkSAQAAAMApqb7GKC4uTsYYGWN07tw5h6W1k5KS9MMPPyg4ODjVT5wjRw6Ha4mMMapTp47DPiy+AAAAAOBuSHUwuhZkbDabSpUqlWK7zWbTqFGjUv3Ey5cvT/W+AAAAAJCRUh2Mli9fLmOMateurXnz5ilnzpz2bV5eXipatKgKFCiQ6ieuVauWc5UCAAAAQAZJdTC6FmSio6NVuHBhubk5fXnSTW3fvv2G7TabTT4+PipSpIi8vb3T7fkAAAAA4HpO38eoaNGiOnPmjD7//HPt3r1bklS2bFl16tRJgYGBaSqiYsWKt7x3kaenp1q2bKkpU6Y4XNsEAAAAAOnB6WGf33//XaGhoXrnnXd06tQpnTp1ShMnTlRoaKg2b96cpiIWLFigkiVL6pNPPtHWrVu1detWffLJJwoLC9OsWbP0+eef69dff9XQoUPT1D8AAAAA3IrTN3h9+OGHVaJECX366afy8Lg64HTlyhV16dJFBw4c0KpVq5wuomrVqnrttddUr149h/affvpJw4YN08aNG/Xtt9/qpZde0v79+2/bHzd4BbjBKyBxg1cAQOpv8Or0VLrff//dIRRJkoeHh15++WVVqVLF2e4kSTt27FDRokVTtBctWlQ7duyQdHW6XUxMTJr6BwAAAIBbcXoqXUBAgA4dOpSi/fDhw8qePXuaiihdurTGjRunxMREe9vly5c1btw4lS5dWpJ09OhR5c2bN039AwAAAMCtOD1i1LJlS3Xu3Fnjx49XRESEJGnt2rUaOHCgWrVqlaYiJk+erEaNGqlQoUIqX768pKujSElJSVq0aJEk6cCBA+rZs2ea+gcAAACAW3E6GI0fP142m03t2rXTlStXJF1dNe7555/XuHHj0lRERESEoqOjFRkZqb/++kuS1Lx5c7Vu3do+CvXcc8+lqW8AAAAAuJ1UL75QpUoVdenSRa1bt1ZAQIAuXLhgXwghNDRUfn5+GVqoM1h8AWDxBUBi8QUAQAYsvlChQgW9/PLLeumll/T000+rU6dOeuSRR9JYnvTdd9/piSeekKenp7777rtb7tuoUaM0Pw8AAAAA3I5Ty3VfuHBBX3/9taZPn67Vq1erWLFi6tSpk9q3b6+CBQs69cRubm46duyYgoOD5eZ28zUgbDabkpKSnOqbESOAESNAYsQIAJD6ESOnVqXz8/NThw4dtGLFCv3111969tlnNWXKFIWEhKhhw4aaPz/1H8SSk5MVHBysy5cv65FHHtGePXuUnJyc4uFsKAIAAAAAZzm9XPc1oaGhGjNmjA4ePKjZs2dr/fr1at68udP9eHp6aseOHbccNQIAAACAjHRHaWTFihXq0KGDOnTooKSkJHXt2jVN/bRt21afffbZnZQCAAAAAGnm9HLdR44c0fTp0zV9+nQdOHBADz/8sD788EM1b95cvr6+aSriypUrmjp1qn755Rfdf//9ypYtm8P2iRMnpqlfAAAAAEiNVAejr7/+WlOnTtWyZcsUHBys9u3bq1OnTipRosQdF/Hnn3+qcuXKkmS/j9E1NpvtjvsHAAAAgFtJdTBq27atGjZsqAULFqhBgwbpek3Q8uXL060vAAAAAHBWqoPRkSNHFBwcnJG1AAAAAIBLpHrYh1AEAAAA4F7FGtkAAAAALI9gBAAAAMDyCEYAAAAALM/p+xhdk5iYqOPHjys5OdmhvUiRIndcFAAAAADcTU4Ho71796pTp05at26dQ7sxRjabTUlJSelWHAAAAADcDU4How4dOsjDw0OLFi1S/vz5uQErAAAAgCzP6WC0detW/fHHHypdunRG1AMAAAAAd53Tiy+Eh4fr5MmTGVELAAAAALiE08HozTff1Msvv6wVK1YoNjZWcXFxDg8AAAAAyGqcnkpXt25dSVKdOnUc2ll8AQAAAEBW5XQwWr58eUbUAQAAAAAu43QwqlWrVkbUAQAAAAAuk+YbvF64cEGHDh1SYmKiQ3v58uXvuCgAAAAAuJucDkYnTpxQx44d9eOPP95wO9cYAQAAAMhqnF6V7sUXX9SZM2e0YcMG+fr6asmSJZoxY4ZKliyp7777LiNqBAAAAIAM5fSI0a+//qqFCxeqSpUqcnNzU9GiRfXYY48pICBAY8eOVcOGDTOiTgAAAADIME6PGJ0/f17BwcGSpKCgIJ04cUKSVK5cOW3evDl9qwMAAACAu8DpYBQWFqaoqChJUoUKFTRlyhQdPXpUH3/8sfLnz5/uBQIAAABARnN6Kl3fvn0VExMjSRoxYoTq16+vyMhIeXl5afr06eldHwAAAABkOKeDUdu2be1f33///fr777+1Z88eFSlSRLlz507X4gAAAADgbkjzfYyu8fPzU+XKldOjFgAAAABwCaeDkTFGc+fO1fLly3X8+HElJyc7bJ8/f366FQcAAAAAd4PTwejFF1/UlClT9Oijjypv3ryy2WwZURcAAAAA3DVOB6OZM2dq/vz5atCgQUbUAwAAAAB3ndPLdQcGBqp48eIZUQsAAAAAuITTwWjkyJEaNWqUEhISMqIeAAAAALjrnJ5K16JFC82ePVvBwcEKCQmRp6enw/bNmzenW3EAAAAAcDc4HYzat2+vP/74Q23btmXxBQAAAAD3BKeD0eLFi/XTTz/poYceyoh6AAAAAOCuc/oao8KFCysgICAjagEAAAAAl3A6GE2YMEEvv/yyDh48mAHlAAAAAMDdZzPGGGcOCAoK0oULF3TlyhX5+fmlWHzh1KlT6VpgWly84uoKAAAAAGQGPqm8eMjpa4wmTZrk7CEAAAAAkKk5PWKUFTBiBAAAAEDKwBGjQ4cO3XJ7kSJFnO0SAAAAAFzK6REjNze3W967KCkp6Y6LulOMGAEAAACQMnDEaMuWLQ7fX758WVu2bNHEiRP1+uuvO9sdAAAAALhcul1jtHjxYr399ttasWJFenR3RxgxAgAAACClfsTI6fsY3UxYWJg2bdqUXt0BAAAAwF3j9FS6uLg4h++NMYqJidHIkSNVsmTJdCsMAAAAAO4Wp4NRjhw5Uiy+YIxR4cKFNWfOnHQrDAAAAADuFqeD0fLlyx2+d3NzU548eVSiRAl5eDjdHQAAAAC4HDd4BQAAAHDPSvflur/77rtU7deoUaPUdgkAAAAAmUKqR4zc3BwXsLPZbPrvoTabjRu8AgAAAMg00n257uTkZIeHn5+f9u3b59CWGUIRAAAAADgr3e5jBAAAAABZFcEIAAAAgOURjAAAAABYXpqDkc1mS3GjVwAAAADIilK9Kl1QUJBDEDpz5owCAgJSrFZ36tSp9K0wDViVDgAAAICUAfcxmjRpUhpLAQAAAIDMLdUjRlkJI0YAAAAApAy4jxEAAAAA3KsIRgAAAAAsj2AEAAAAwPIIRgAAAAAsj2AEAAAAwPJSvVz39Y4cOaLvvvtOhw4dUmJiosO2iRMnpkthAAAAAHC3OB2Mli1bpkaNGql48eLas2eP7rvvPh08eFDGGFWuXDkjagQAAACADOX0VLohQ4ZowIAB2rFjh3x8fDRv3jwdPnxYtWrVUvPmzTOiRgAAAADIUE7f4DV79uzaunWrQkNDFRQUpDVr1qhs2bLatm2bGjdurIMHD2ZQqanHDV4BAAAASBl4g9ds2bLZryvKnz+/9u/fb9928uRJZ7sDAAAAAJdz+hqjatWqac2aNSpTpowaNGigl156STt27ND8+fNVrVq1jKgRAAAAADKU01PpDhw4oPj4eJUvX17nz5/XSy+9pHXr1qlkyZKaOHGiihYtmlG1phpT6QAAAABIqZ9K53QwygoIRgAAAACk1AejNN3HSJISExN1/PhxJScnO7QXKVIkrV0CAAAAgEs4HYz++usvde7cWevWrXNoN8bIZrMpKSkp3YoDAAAAgLvB6WDUsWNHeXh4aNGiRcqfP79sNltG1AUAAAAAd43T1xhly5ZNf/zxh0qXLp1RNd0xrjECAAAAIGXgfYzCw8O5XxEAAACAe4rTwejNN9/Uyy+/rBUrVig2NlZxcXEODwAAAADIapyeSufmdjVL/ffaosy0+AJT6QAAAABIGbhc9/Lly509BAAAAAAyNW7wCgAAAOCelWGLL0jS6tWr1bZtW0VEROjo0aOSpJkzZ2rNmjVp6Q4AAAAAXMrpYDRv3jzVq1dPvr6+2rx5sy5duiRJOnv2rN544410LxAAAAAAMprTwWjMmDH6+OOP9emnn8rT09PeXqNGDW3evDldiwMAAACAu8HpYBQVFaWaNWumaA8MDNSZM2fSVMTMmTNVo0YNFShQQH///bckadKkSVq4cGGa+gMAAAAAZzgdjPLly6d9+/alaF+zZo2KFy/udAEfffSR+vfvrwYNGujMmTP25b5z5MihSZMmOd0fAAAAADjL6WDUtWtX9e3bVxs2bJDNZtM///yjyMhIDRgwQM8//7zTBbz//vv69NNP9eqrr8rd3d3eXqVKFe3YscPp/gAAAADAWU7fx2jw4MFKTk5WnTp1dOHCBdWsWVPe3t4aMGCAXnjhBacLiI6OVqVKlVK0e3t76/z58073BwAAAADOcioYJSUlae3aterVq5cGDhyoffv2KT4+XuHh4fL3909TAcWKFdPWrVtVtGhRh/YlS5aoTJkyaeoTAAAAAJzhVDByd3fX448/rt27dytHjhwKDw+/4wL69++vXr166eLFizLGaOPGjZo9e7bGjh2rzz777I77BwAAAIDbcXoq3X333acDBw6oWLFi6VJAly5d5Ovrq6FDh+rChQtq3bq1ChQooHfffVfPPvtsujwHAAAAANyKzRhjnDlgyZIlGjJkiF577TXdf//9ypYtm8P2gICANBdz4cIFxcfHKzg4OM19SNLFK3d0OAAAAIB7hE8qh4KcDkZubv9byM5ms9m/NsbIZrPZl9tOrdq1a2v+/PnKkSOHQ3tcXJyaNGmiX3/91an+JIIRAAAAgKtSG4ycnkq3fPlyZw+5pRUrVigxMTFF+8WLF7V69ep0fS4AAAAAuBGng1GtWrVuuu3PP/9MdT/bt2+3f71r1y4dO3bM/n1SUpKWLFmiggULOlseAAAAADjN6al0/3Xu3DnNnj1bn332mf74449UT6Vzc3OzT8W7UQm+vr56//331alTJ6drYiodAAAAACkDp9Jds2rVKn3++eeaN2+eChQooGbNmmny5MmpPj46OlrGGBUvXlwbN25Unjx57Nu8vLwUHBwsd3f3tJYHAAAAAKnmVDA6duyYpk+frs8//1xxcXFq0aKFLl26pG+//dbpexpdu6FrcnKyU8cBAAAAQHpzu/0uVz311FMKCwvT9u3bNWnSJP3zzz96//3306WI/fv364UXXlDdunVVt25d9enTR/v370+XvgEAAADgdlIdjH788Ud17txZo0aNUsOGDdNtmttPP/2k8PBwbdy4UeXLl1f58uW1YcMGlS1bVj///HO6PAfurjmzIvXEY7X1QKVyavNsc+24bqENwCo4D2B1nAMA50FWk+pgtGbNGp07d07333+/HnzwQX3wwQc6efLkHRcwePBg9evXTxs2bNDEiRM1ceJEbdiwQS+++KIGDRp0x/3j7lry4w8a/9ZYde/ZS3O+WaCwsNJ6vntnxcbGuro04K7hPIDVcQ4AnAdZkdOr0p0/f15fffWVpk6dqo0bNyopKUkTJ05Up06dlD17dqcL8PHx0Y4dO1SyZEmH9r/++kvly5fXxYsXne6TVelcp82zzVX2vnJ6ZehwSVevIXu8Ti21av2cOnft5uLqgLuD8wBWxzkAcB5kJqldlS7VI0bXZMuWTZ06ddKaNWu0Y8cOvfTSSxo3bpyCg4PVqFEjZ7tTnjx5tHXr1hTtW7duVXBwsNP9wXUuJyZq966dqlY9wt7m5uamatUitH3bFhdWBtw9nAewOs4BgPMgq3I6GF0vLCxMb731lo4cOaLZs2enqY+uXbuqW7duevPNN7V69WqtXr1a48aNU/fu3dW1a9fbHn/p0iXFxcU5PC5dupSmWnBnTp85raSkJOXKlcuhPVeuXOky7RLICjgPYHWcAwDnQVaV5vsYXc/d3V1NmjRRkyZNnD522LBhyp49uyZMmKAhQ4ZIkgoUKKCRI0eqT58+tz1+7NixGjVqlEPbq8NGaOjwkU7XAgAAAMCa0iUY3QmbzaZ+/fqpX79+OnfunCQ5da3SkCFD1L9/f4c24+6drjUidYJyBMnd3T3FRYWxsbHKnTu3i6oC7i7OA1gd5wDAeZBV3dFUuvQwZswYRUdHS7oaiJxdwMHb21sBAQEOD29vgpEreHp5qUx4WW1Y/5u9LTk5WRs2/KbyFSq5sDLg7uE8gNVxDgCcB1mVy4PRN998oxIlSigiIkIffvgh8y6zuOfad9T8uV/ru28X6MD+/RozeqQSEhLUpGkzV5cG3DWcB7A6zgGA8yArcnq57oywc+dORUZGas6cOTpy5Igee+wxtWnTRk2aNJGfn5/T/bFct2vNjvxSM6Z9rpMnTyisdBkNemWoypev4OqygLuK8wBWxzkAcB5kFqldrjtTBKPrrV27VrNmzdI333yjixcvKi4uzuk+CEYAAAAApAy8j1FGy5Ytm3x9feXl5aXLly+7uhwAAAAAFpApglF0dLRef/11lS1bVlWqVNGWLVs0atQoHTt2zNWlAQAAALAAly/XXa1aNW3atEnly5dXx44d1apVKxUsWNDVZQEAAACwEJcHozp16mjq1KkKDw93dSkAAAAALCrTLL6QmJio6OhohYaGysPjzvIaiy8AAAAAkLLQ4gsJCQnq3Lmz/Pz8VLZsWR06dEiS9MILL2jcuHEurg4AAACAFbg8GA0ePFjbtm3TihUr5OPjY2+vW7euvvrqKxdWBgAAAMAqXH6N0bfffquvvvpK1apVk81ms7eXLVtW+/fvd2FlAAAAAKzC5SNGJ06cUHBwcIr28+fPOwQlAAAAAMgoLg9GVapU0eLFi+3fXwtDn332mapXr+6qsgAAAABYiMun0r3xxht64okntGvXLl25ckXvvvuudu3apXXr1mnlypWuLg8AAACABbh8xOihhx7S1q1bdeXKFZUrV05Lly5VcHCwfvvtN91///2uLg8AAACABWSa+xilJ+5jBAAAAEBK/X2MXDKVLi4uTgEBAfavb+XafgAAAACQUVwyYuTu7q6YmBgFBwfLzc3thqvPGWNks9mUlJTkdP+MGAEAAACQMvmI0a+//qqcOXNKkqZNm6bChQvL3d3dYZ/k5GQdOnTIFeUBAAAAsBiXX2N0/ejR9WJjYxUcHMyIEQAAAIA0S+2IkctXpbs2Ze6/4uPj5ePj44KKAAAAAFiNy+5j1L9/f0lXb+g6bNgw+fn52bclJSVpw4YNqlixoouqAwAAAGAlLgtGW7ZskXR1xGjHjh3y8vKyb/Py8lKFChU0YMAAV5UHAAAAwEJcfo1Rx44d9e6776brstxcYwQAAABASv01Ri4PRhmBYAQAAABAykKLLwAAAACAqxGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5RGMAAAAAFgewQgAAACA5WW6YHTx4kVXlwAAAADAYjJFMEpOTtZrr72mggULyt/fXwcOHJAkDRs2TJ9//rmLqwMAAABwr8sUwWjMmDGaPn263nrrLXl5ednb77vvPn322WcurAwAAACAFWSKYPTFF1/ok08+UZs2beTu7m5vr1Chgvbs2ePCygAAAABYQaYIRkePHlWJEiVStCcnJ+vy5csuqAgAAACAlWSKYBQeHq7Vq1enaJ87d64qVarkgooAAAAAWImHqwuQpOHDh6t9+/Y6evSokpOTNX/+fEVFRemLL77QokWLXF0eAAAAgHuczRhjXF2EJK1evVqjR4/Wtm3bFB8fr8qVK2v48OF6/PHHne7r4pUMKBAAAABAluOTyqGgTBOM0hPBCAAAAICU+mCUKa4xKl68uGJjY1O0nzlzRsWLF3dBRQAAAACsJFMEo4MHDyopKSlF+6VLl3T06FEXVAQAAADASly6+MJ3331n//qnn35SYGCg/fukpCQtW7ZMISEhLqgMAAAAgJW49BojN7erA1Y2m03/LcPT01MhISGaMGGCnnzySaf65RojAAAAAFLqrzFy6YhRcnKyJKlYsWLatGmTcufO7cpyAAAAAFgUq9IBAAAAuGdlqVXpJGnlypV66qmnVKJECZUoUUKNGjXS6tWrXV0WAAAAAAvIFMHoyy+/VN26deXn56c+ffqoT58+8vX1VZ06dTRr1ixXlwcAAADgHpcpptKVKVNG3bp1U79+/RzaJ06cqE8//VS7d+92qj+m0gEAAACQUj+VLlMEI29vb+3cuVMlSpRwaN+3b5/uu+8+Xbx40an+CEYAAAAApCx2jVHhwoW1bNmyFO2//PKLChcu7IKKAAAAAFiJS5frvuall15Snz59tHXrVkVEREiS1q5dq+nTp+vdd991cXUAAAAA7nWZYiqdJC1YsEATJkywX09UpkwZDRw4UI0bN3a6L6bSAQAAAJCy2DVG6Y1gBAAAAEDKYtcYdenSRStWrHB1GQAAAAAsKlMEoxMnTqh+/foqXLiwBg4cqK1bt7q6JAAAAAAWkmmm0p0+fVrffPONZs2apdWrV6t06dJq06aNWrdurZCQEKf6YiodAAAAACmLX2N05MgRzZ49W1OnTtXevXt15YpzSYdgBAAAAEDKYtcYXe/y5cv6/ffftWHDBh08eFB58+Z1dUkAAAAA7nGZJhgtX75cXbt2Vd68edWhQwcFBARo0aJFOnLkiKtLAwAAAHCPyxRT6QoWLKhTp06pfv36atOmjZ566il5e3unuT+m0gEAAACQstg1Rp9++qmaN2+uHDly3HK/I0eOqECBAnJzu/VAF8EIAAAAgJTFglFqBQQEaOvWrSpevPgt9yMYAQAAAJCy8OILt5KFMhwAAACALCRLBSMAAAAAyAgEIwAAAACWRzACAAAAYHlZKhjZbDZXlwAAAADgHpSlghGLLwAAAADICFlque7Dhw+rQIECcnd3v+V+LNcNAAAAQMoC9zFq1qxZqvedP3++U30TjFxrzqxIzZj2uU6ePKFSYaU1+JVhKle+vKvLAu4qzgNYHecAwHmQWWT6+xgFBgbaHwEBAVq2bJl+//13+/Y//vhDy5YtU2BgoKtKRBos+fEHjX9rrLr37KU53yxQWFhpPd+9s2JjY11dGnDXcB7A6jgHAM6DrChTTKUbNGiQTp06pY8//tg+TS4pKUk9e/ZUQECA3n77baf6Y8TIddo821xl7yunV4YOlyQlJyfr8Tq11Kr1c+rctZuLqwPuDs4DWB3nAMB5kJlk+hGj602dOlUDBgxwuHbI3d1d/fv319SpU11YGZxxOTFRu3ftVLXqEfY2Nzc3VasWoe3btriwMuDu4TyA1XEOAJwHWVWmCEZXrlzRnj17UrTv2bNHycnJtzz20qVLiouLc3hcunQpo0rFLZw+c1pJSUnKlSuXQ3uuXLl08uRJF1UF3F2cB7A6zgGA8yCryhTBqGPHjurcubMmTpyoNWvWaM2aNZowYYK6dOmijh073vLYsWPHOlyvFBgYqLffHHuXKgcAAABwL0jljLuMNX78eOXLl08TJkxQTEyMJCl//vwaOHCgXnrppVseO2TIEPXv39+hzbh7Z1ituLmgHEFyd3dPcVFhbGyscufO7aKqgLuL8wBWxzkAcB5kVZlixMjNzU0vv/yyjh49qjNnzujMmTM6evSoXn755dves8jb21sBAQEOD29vgpEreHp5qUx4WW1Y/5u9LTk5WRs2/KbyFSq5sDLg7uE8gNVxDgCcB1lVphgxul5AQICrS8AdeK59Rw17ZZDKlr1P95Urry9nzlBCQoKaNE39fauArI7zAFbHOQBwHmRFmSYYzZ07V19//bUOHTqkxMREh22bN292UVVwVv0nGuj0qVP68IP3dPLkCYWVLqMPp3ymXAwbw0I4D2B1nAMA50FWlCnuY/Tee+/p1VdfVYcOHfTJJ5+oY8eO2r9/vzZt2qRevXrp9ddfd6o/7mMEAAAAQEr9fYwyRTAqXbq0RowYoVatWil79uzatm2bihcvruHDh+vUqVP64IMPnOqPYAQAAABAymI3eD106JAiIq7eAMvX11fnzp2TJD333HOaPXu2K0sDAAAAYAGZIhjly5dPp06dkiQVKVJE69evlyRFR0crEwxoAQAAALjHZYpgVLt2bX333XeSrt7stV+/fnrsscfUsmVLNW3a1MXVAQAAALjXZYprjJKTk5WcnCwPj6sTAOfMmaN169apZMmS6t69u7y8vJzqj2uMAAAAAEhZbPGF9EYwAgAAACClPhhlmvsYnT59Wp9//rl2794tSQoPD1fHjh2VM2dOF1cGAAAA4F6XKUaMVq1apUaNGikgIEBVqlSRJP3xxx86c+aMvv/+e9WsWdOp/hgxAgAAACBlsal05cqVU/Xq1fXRRx/J3d1dkpSUlKSePXtq3bp12rFjh1P9EYwAAAAASFksGPn6+mrr1q0KCwtzaI+KilLFihWVkJDgVH8EIwAAAABSFrvBa+XKle3XFl1v9+7dqlChggsqAgAAAGAlLlt8Yfv27fav+/Tpo759+2rfvn2qVq2aJGn9+vWaPHmyxo0b56oSAQAAAFiEy6bSubm5yWaz6XZPb7PZlJSU5FTfTKUDAAAAIGWB5bqjo6Nd9dQAAAAA4CBTLL6Q3hgxAgAAACBlgRGj/9q7d6+WL1+u48ePKzk52WHb8OHDXVQVAAAAACvIFCNGn376qZ5//nnlzp1b+fLlk81ms2+z2WzavHmzU/0xYgQAAABAymL3MSpatKh69uypQYMGpUt/BCMAAAAAUha7j9Hp06fVvHlzV5cBAAAAwKIyRTBq3ry5li5d6uoyAAAAAFiUy6bSvffee/avz58/r4kTJ6phw4YqV66cPD09Hfbt06ePU30zlQ4AAACAlAWuMSpWrFiq9rPZbDpw4IBTfROMAAAAAEhZIBhlJIIRAAAAACmLLb4wevRoXbhwIUV7QkKCRo8e7YKKAAAAAFhJphgxcnd3V0xMjIKDgx3aY2NjFRwcrKSkJKf6Y8QIAAAAgJTFRoyMMQ43db1m27ZtypkzpwsqAgAAAGAlqcxPGSMoKEg2m002m02lSpVyCEdJSUmKj49Xjx49XFghAAAAACtw6VS6GTNmyBijTp06adKkSQoMDLRv8/LyUkhIiKpXr+50v0ylAwAAACBlsVXpVq5cqYiIiBT3L0orghEAAAAAKYsFI+nq1Llvv/1Wu3fvliSVLVtWjRo1kru7u9N9EYwAAAAASFksGO3bt08NGjTQ0aNHFRYWJkmKiopS4cKFtXjxYoWGhjrVH8EIAAAAgJTFglGDBg1kjFFkZKR9FbrY2Fi1bdtWbm5uWrx4sVP9EYwAAAAASFksGGXLlk3r169XuXLlHNq3bdumGjVqKD4+3qn+CEYAAAAApCx2HyNvb2+dO3cuRXt8fLy8vLxcUBEAAAAAK8kUwejJJ59Ut27dtGHDBhljZIzR+vXr1aNHDzVq1MjV5QEAAAC4x2WKqXRnzpxR+/bt9f3339uX7L58+bIaN26s6dOnO9zfKDWYSgcAAABAymLXGF2zb98+7dq1S5IUHh6uEiVKpKkfghEAAAAAKfXBKJW7ZbzPP/9c77zzjvbu3StJKlmypF588UV16dLFxZUBAAAAuNdlimA0fPhwTZw4US+88IKqV68uSfrtt9/Ur18/HTp0SKNHj3ZxhQAAAADuZZliKl2ePHn03nvvqVWrVg7ts2fP1gsvvKCTJ0861R9T6QAAAABIWWy57suXL6tKlSop2u+//35duULKAQAAAJCxMkUweu655/TRRx+laP/kk0/Upk0bF1QEAAAAwEpcdo1R//797V/bbDZ99tlnWrp0qapVqyZJ2rBhgw4dOqR27dq5qkQAAAAAFuGya4weffTRVO1ns9n066+/OtU31xgBAAAAkLLofYzSC8EIAAAAgJTFFl8AAAAAAFciGAEAAACwPIIRAAAAAMsjGAEAAACwPIIRAAAAAMsjGAEAAACwPIIRAAAAAMsjGAEAAACwPIIRAAAAAMsjGAEAAACwPIIRAAAAAMsjGAEAAACwPIIRAAAAAMsjGAEAAACwPIIRAAAAAMsjGAEAAACwPIIRAAAAAMuzGWOMq4vAveXSpUsaO3ashgwZIm9vb1eXA7gE5wHAeQBInAdZCcEI6S4uLk6BgYE6e/asAgICXF0O4BKcBwDnASBxHmQlTKUDAAAAYHkEIwAAAACWRzACAAAAYHkEI6Q7b29vjRgxggsMYWmcBwDnASBxHmQlLL4AAAAAwPIYMQIAAABgeQQjAAAAAJZHMAIAAABgeQQjC3rkkUf04osvpnr/b7/9ViVKlJC7u7tTxwGZmc1m07fffpvq/VesWCGbzaYzZ85kWE1AZsH7HZmRs59fUiskJESTJk266faDBw/KZrNp69at6f7cHTp0UJMmTdK9X6QNwQi31b17dz3zzDM6fPiwXnvttQw7iadPn64cOXKke7/AjcTExOiJJ55I1z5HjhypihUrpmufEh9SkfH++4EzIiJCMTExCgwMvGs1ZOSHT0Dic4bk/B8FrcbD1QUgc4uPj9fx48dVr149FShQwNXlAOkiMTFR+fLlc3UZQKbl5eXFOQLAchgxsrhLly5pwIABKliwoLJly6YHH3xQK1askHT1r9TZs2eXJNWuXVs2m02PPPKIZsyYoYULF8pms8lms2nFihWKiIjQoEGDHPo+ceKEPD09tWrVKknS6dOn1a5dOwUFBcnPz09PPPGE9u7da3+ujh076uzZs/Z+R44cedsagdR45JFH1Lt3b7344ovKnTu36tWrl+KvZuvWrVPFihXl4+OjKlWq6Ntvv73hX6//+OMPValSRX5+foqIiFBUVJSkq3+JHDVqlLZt22Z/D0+fPl2tW7dWy5YtHfq4fPmycufOrS+++ELS1fd4nz59FBwcLB8fHz300EPatGmTpKt/RX/00UclSUFBQbLZbOrQoYMkKTk5WWPHjlWxYsXk6+urChUqaO7cuRnwE8S9rEOHDlq5cqXeffddh/fuf0cp16xZo4cffli+vr4qXLiw+vTpo/Pnz0uSXnnlFT344IMp+q5QoYJGjx4t6er7dfTo0SpUqJC8vb1VsWJFLVmyxL5vsWLFJEmVKlWy/39zzWeffaYyZcrIx8dHpUuX1ocffpgBPwlkBVeuXFHv3r0VGBio3Llza9iwYbp255m0fs6QpAsXLqhTp07Knj27ihQpok8++STFc+/Zs0cRERHy8fHRfffdp5UrV9q3JSUlqXPnzvbfx2FhYXr33Xcdjk9KSlL//v2VI0cO5cqVSy+//LJSe9ecTz75RAUKFFBycrJDe+PGjdWpUyf79x999JFCQ0Pl5eWlsLAwzZw5074tJCREktS0aVPZbDb795K0cOFCVa5cWT4+PipevLhGjRqlK1eupKq2e4qB5dSqVcv07dvXGGNMly5dTEREhFm1apXZt2+fefvtt423t7f566+/zKVLl0xUVJSRZObNm2diYmLM2bNnTYsWLUz9+vVNTEyMiYmJMZcuXTIffPCBKVKkiElOTrY/z/vvv+/Q1qhRI1OmTBmzatUqs3XrVlOvXj1TokQJk5iYaC5dumQmTZpkAgIC7P2eO3futjUCqVGrVi3j7+9vBg4caPbs2WP27NljJJkFCxYYY4w5e/asyZkzp2nbtq3ZuXOn+eGHH0ypUqWMJLNlyxZjjDHLly83ksyDDz5oVqxYYXbu3GkefvhhExERYYwx5sKFC+all14yZcuWtb+HL1y4YBYtWmR8fX3t72djjPn++++Nr6+viYuLM8YY06dPH1OgQAHzww8/mJ07d5r27duboKAgExsba65cuWLmzZtnJJmoqCgTExNjzpw5Y4wxZsyYMaZ06dJmyZIlZv/+/WbatGnG29vbrFix4u79cJHlnTlzxlSvXt107drV/t795ZdfjCRz+vRpY4wx+/btM9myZTPvvPOO+euvv8zatWtNpUqVTIcOHYwxxvz5559Gktm3b5+932tte/fuNcYYM3HiRBMQEGBmz55t9uzZY15++WXj6elp/12+ceNGI8n88ssvJiYmxsTGxhpjjPnyyy9N/vz5zbx588yBAwfMvHnzTM6cOc306dPv4k8JmcG13+V9+/Y1e/bsMV9++aXx8/Mzn3zyiTEm7Z8zihYtanLmzGkmT55s9u7da8aOHWvc3NzMnj17jDHGREdHG0mmUKFCZu7cuWbXrl2mS5cuJnv27ObkyZPGGGMSExPN8OHDzaZNm8yBAwfstX311Vf2+t98800TFBRk5s2bZ3bt2mU6d+5ssmfPbho3bnzb137q1Cnj5eVlfvnlF3tbbGysQ9v8+fONp6enmTx5somKijITJkww7u7u5tdffzXGGHP8+HEjyUybNs3ExMSY48ePG2OMWbVqlQkICDDTp083+/fvN0uXLjUhISFm5MiRd/gvlvUQjCzoWjD6+++/jbu7uzl69KjD9jp16pghQ4YYY4w5ffq0kWSWL19u396+ffsUJ/Hx48eNh4eHWbVqlb2tevXqZtCgQcYYY/766y8jyaxdu9a+/eTJk8bX19d8/fXXxhhjpk2bZgIDAx36TU2NwO3UqlXLVKpUyaHt+mD00UcfmVy5cpmEhAT79k8//fSGwej6/5QWL15sJNmPGzFihKlQoYLD81y+fNnkzp3bfPHFF/a2Vq1amZYtWxpjjImPjzeenp4mMjLSvj0xMdEUKFDAvPXWWw7Pfe1DqjHGXLx40fj5+Zl169Y5PF/nzp1Nq1atnPjpAI5/MDMm5Xuuc+fOplu3bg7HrF692ri5udnf/xUqVDCjR4+2bx8yZIh58MEH7d8XKFDAvP766w59PPDAA6Znz57GmP99+Lx2zl0TGhpqZs2a5dD22muvmerVq6fptSLrqlWrlilTpozDH2EHDRpkypQpk+bPGcZcDUZt27a1f5+cnGyCg4PNRx99ZIz533tz3Lhx9n0uX75sChUqZN58882b1turVy/z9NNP27/Pnz+//ff69X2kJhgZY0zjxo1Np06d7N9PmTLFFChQwCQlJRljjImIiDBdu3Z1OKZ58+amQYMG9u+v/7/vmjp16pg33njDoW3mzJkmf/78qarrXsJUOgvbsWOHkpKSVKpUKfn7+9sfK1eu1P79+53qK0+ePHr88ccVGRkpSYqOjtZvv/2mNm3aSJJ2794tDw8Ph6kWuXLlUlhYmHbv3n1XaoS13X///TfdFhUVpfLly8vHx8feVrVq1RvuW758efvX+fPnlyQdP378pn17eHioRYsW9nPj/PnzWrhwof3c2L9/vy5fvqwaNWrYj/H09FTVqlVveW7s27dPFy5c0GOPPeZwbnzxxRecG0h327Zt0/Tp0x3ea/Xq1VNycrKio6MlSW3atNGsWbMkScYYzZ492/4+j4uL0z///OPwPpekGjVq3PJ9fv78ee3fv1+dO3d2eO4xY8bwPreoatWqyWaz2b+vXr269u7dq127dqXpc8Y11/9ut9lsypcvX4rf7dWrV7d/7eHhoSpVqjj0PXnyZN1///3KkyeP/P399cknn+jQoUOSpLNnzyomJsahvmt9pFabNm00b948Xbp0SZIUGRmpZ599Vm5uVz/O79692+lzTLp6fo8ePdrhHOvatatiYmJ04cKFVNd3L2DxBQuLj4+Xu7u7/vjjD7m7uzts8/f3d7q/Nm3aqE+fPnr//fc1a9YslStXTuXKlctUNcK6smXLli79eHp62r++9p/zf+d8/1ebNm1Uq1YtHT9+XD///LN8fX1Vv379O6ojPj5ekrR48WIVLFjQYZu3t/cd9Q38V3x8vLp3764+ffqk2FakSBFJUqtWrTRo0CBt3rxZCQkJOnz4cIrr69LyvJL06aefpriG6b//JwB34vrf7dLV3++3+91+vTlz5mjAgAGaMGGCqlevruzZs+vtt9/Whg0b0q3Gp556SsYYLV68WA888IBWr16td9555477jY+P16hRo9SsWbMU267/g6EVEIwsrFKlSkpKStLx48f18MMPp/o4Ly8vJSUlpWhv3LixunXrpiVLlmjWrFlq166dfVuZMmV05coVbdiwQREREZKk2NhYRUVFKTw8/Kb9prVGwBlhYWH68ssvdenSJXuouLb4gTNudm5ERESocOHC+uqrr/Tjjz+qefPm9v+Er10ku3btWhUtWlTS1cUZNm3aZF8+2cvLS5Ic+g4PD5e3t7cOHTqkWrVqOV0rcL2bvXevqVy5snbt2qUSJUrcdJ9ChQqpVq1aioyMVEJCgh577DEFBwdLkgICAlSgQAGtXbvW4f26du1a++jsjd7nefPmVYECBXTgwAH76BOs7b9BY/369SpZsqTCw8PT9DnDGevXr1fNmjUlXV0E4o8//lDv3r0lXX0vR0REqGfPnvb9rx/VDAwMVP78+bVhw4YUfVSuXDlVz+/j46NmzZopMjJS+/btU1hYmMOxZcqU0dq1a9W+fXt729q1a+2vX7oaAP/7M6hcubKioqJueX5bBcHIwkqVKqU2bdqoXbt2mjBhgipVqqQTJ05o2bJlKl++vBo2bHjD40JCQvTTTz8pKipKuXLlUmBgoDw9PZUtWzY1adJEw4YN0+7du9WqVSv7MSVLllTjxo3VtWtXTZkyRdmzZ9fgwYNVsGBBNW7c2N5vfHy8li1bpgoVKsjPzy/NNQLOaN26tV599VV169ZNgwcP1qFDhzR+/HhJcpiycTshISGKjo7W1q1bVahQIWXPnt0etFq3bq2PP/5Yf/31l5YvX24/Jlu2bHr++ec1cOBA5cyZU0WKFNFbb72lCxcuqHPnzpKkokWLymazadGiRWrQoIF8fX2VPXt2DRgwQP369VNycrIeeughnT17VmvXrlVAQIDDf4zA7YSEhGjDhg06ePCg/P39U/ylfNCgQapWrZp69+6tLl26KFu2bNq1a5d+/vlnffDBB/b92rRpoxEjRigxMTHFX7IHDhyoESNGKDQ0VBUrVtS0adO0detW+zTT4OBg+fr6asmSJSpUqJB8fHwUGBioUaNGqU+fPgoMDFT9+vV16dIl/f777zp9+rT69++f8T8cZCqHDh1S//791b17d23evFnvv/++JkyYkObPGX5+fql+7smTJ6tkyZIqU6aM3nnnHZ0+fdq+IlzJkiX1xRdf6KefflKxYsU0c+ZMbdq0yb7aoiT17dtX48aNU8mSJVW6dGlNnDjR6fvTtWnTRk8++aR27typtm3bOmwbOHCgWrRooUqVKqlu3br6/vvvNX/+fP3yyy/2fUJCQrRs2TLVqFFD3t7eCgoK0vDhw/Xkk0+qSJEieuaZZ+Tm5qZt27bpzz//1JgxY5yqL8tz9UVOuPuuv8j22ioqISEhxtPT0+TPn980bdrUbN++3Rhz48UXjh8/bh577DHj7++fYtsPP/xgJJmaNWumeN5Tp06Z5557zgQGBhpfX19Tr169FCvL9ejRw+TKlctIMiNGjEhVjcDt/PfCcmNSXoC6du1aU758eePl5WXuv/9+M2vWLCPJvirRjRZA2LJli5FkoqOjjTFXF0R4+umnTY4cOewr/1yza9cuI8kULVrU4cJhY4xJSEgwL7zwgsmdO7fx9vY2NWrUMBs3bnTYZ/To0SZfvnzGZrOZ9u3bG2OuXiA8adIkExYWZjw9PU2ePHlMvXr1zMqVK+/o5wXriYqKMtWqVTO+vr729+5/3+8bN260/+7Pli2bKV++fIrFFE6fPm28vb2Nn5+fw0qMxhiTlJRkRo4caQoWLGg8PT1NhQoVzI8//uiwz6effmoKFy5s3NzcTK1ateztkZGRpmLFisbLy8sEBQWZmjVrmvnz56f7zwGZW61atUzPnj1Njx49TEBAgAkKCjKvvPKK/XdqWj9nFC1a1LzzzjsO+1WoUMG+/driC7NmzTJVq1Y1Xl5eJjw83L7amzFXf/936NDBBAYGmhw5cpjnn3/eDB482GFBnsuXL5u+ffuagIAAkyNHDtO/f3/Trl27VC++YMzV8yh//vxGktm/f3+K7R9++KEpXry48fT0NKVKlXJY+McYY7777jtTokQJ4+HhYYoWLWpvX7JkiYmIiDC+vr4mICDAVK1a1b7an5XYjEnlAuoAYCGRkZH2e174+vq6uhwAAJDBmEoHAJK++OILFS9eXAULFtS2bds0aNAgtWjRglAEAIBFEIwAQNKxY8c0fPhwHTt2TPnz51fz5s31+uuvu7osAIAFHDp0yGGRhP/atWuXfQVIZBym0gEAAAAudOXKFR08ePCm20NCQuThwXhGRiMYAQAAALA8N1cXAAAAAACuRjACAAAAYHkEIwAAAACWRzACAAAAYHkEIwAAAACWRzACAKTZsWPH1LdvX5UoUUI+Pj7KmzevatSooY8++kgXLlyw7xcSEiKbzSabzaZs2bKpcuXK+uabb1Jsu9GjQ4cOt6xh7dq18vDwUMWKFe1tTz31lOrXr3/D/VevXi2bzabt27en2FauXDn16NHjhsfNnDlT3t7eOnny5G1+KpLNZtO333572/0AAJkHwQgAkCYHDhxQpUqVtHTpUr3xxhvasmWLfvvtN7388statGiRfvnlF4f9R48erZiYGG3ZskUPPPCAWrZsqXXr1mnTpk2KiYlRTEyM5s2bJ0mKioqyt7377rs3reHMmTNq166d6tSp49DeuXNn/fzzzzpy5EiKY6ZNm6YqVaqofPnyKbZ17txZc+bMUUJCwg2Pa9SokXLnzp2qnw8AIGshGAEA0qRnz57y8PDQ77//rhYtWqhMmTIqXry4GjdurMWLF+upp55y2D979uzKly+fSpUqpcmTJ8vX11fff/+98uTJo3z58ilfvnzKmTOnJCk4ONjeFhgYeNMaevToodatW6t69eoO7U8++aTy5Mmj6dOnO7THx8frm2++UefOnW/YX9u2bZWQkGAPaNdER0drxYoV9uM++ugjhYaGysvLS2FhYZo5c6Z935CQEElS06ZNZbPZ7N9L0sKFC1W5cmX5+PioePHiGjVqlK5cuXLT1wcAuHsIRgAAp8XGxmrp0qXq1auXsmXLdsN9bDbbTY/38PCQp6enEhMT01zDtGnTdODAAY0YMeKG/bdr107Tp0/X9fcx/+abb5SUlKRWrVrdsM/cuXOrcePGmjp1qkP79OnTVahQIT3++ONasGCB+vbtq5deekl//vmnunfvro4dO2r58uWSpE2bNtnri4mJsX+/evVqtWvXTn379tWuXbs0ZcoUTZ8+Xa+//nqafwYAgPRDMAIAOG3fvn0yxigsLMyhPXfu3PL395e/v78GDRp0w2MTExM1duxYnT17VrVr107T8+/du1eDBw/Wl19+KQ8Pjxvu06lTJ+3fv18rV660t02bNk1PP/30LUehOnfurBUrVig6OlqSZIzRjBkz1L59e7m5uWn8+PHq0KGDevbsqVKlSql///5q1qyZxo8fL0nKkyePJClHjhzKly+f/ftRo0Zp8ODBat++vYoXL67HHntMr732mqZMmZKmnwEAIH0RjAAA6Wbjxo3aunWrypYtq0uXLjlsGzRokPz9/eXn56c333xT48aNU8OGDW/b57Wg5e/vrx49eigpKUmtW7fWqFGjVKpUqZseV7p0aUVERNhHf/bt26fVq1ffdBrdNY899pgKFSqkadOmSZKWLVumQ4cOqWPHjpKk3bt3q0aNGg7H1KhRQ7t3775lv9u2bdPo0aMdXk/Xrl0VExPjsFAFAMA1bvxnNgAAbqFEiRKy2WyKiopyaC9evLgkydfXN8UxAwcOVIcOHeTv76+8efPecqrd9bZu3Wr/OiAgQOfOndPvv/+uLVu2qHfv3pKk5ORkGWPk4eGhpUuX2keiOnfurBdeeEGTJ0/WtGnTFBoaqlq1at3y+dzc3NShQwfNmDFDI0eO1LRp0/Too4/aX1taxcfHa9SoUWrWrFmKbT4+PnfUNwDgzjFiBABwWq5cufTYY4/pgw8+0Pnz51N1TO7cuVWiRAnly5cv1aFIuhrCrj2Cg4MVEBCgHTt2aOvWrfZHjx49FBYWpq1bt+rBBx+0H9uiRQu5ublp1qxZ+uKLL9SpU6dUPXfHjh11+PBhzZ8/XwsWLHAYZSpTpozWrl3rsP/atWsVHh5u/97T01NJSUkO+1SuXFlRUVEOr+faw82N/44BwNUYMQIApMmHH36oGjVqqEqVKho5cqTKly8vNzc3bdq0SXv27NH999+fIc/r5uam++67z6EtODhYPj4+Kdr9/f3VsmVLDRkyRHFxcbe9J9I1xYoVU+3atdWtWzd5e3s7jPIMHDhQLVq0UKVKlVS3bl19//33mj9/vsPy5CEhIVq2bJlq1Kghb29vBQUFafjw4XryySdVpEgRPfPMM3Jzc9O2bdv0559/asyYMWn/gQAA0gV/ogIApEloaKi2bNmiunXrasiQIapQoYKqVKmi999/XwMGDNBrr73m6hIlXZ1Od/r0adWrV08FChRw+rjWrVs7THVr0qSJ3n33XY0fP15ly5bVlClTNG3aND3yyCP2fSZMmKCff/5ZhQsXVqVKlSRJ9erV06JFi7R06VI98MADqlatmt555x0VLVo03V4rACDtbOb6dUwBAAAAwIIYMQIAAABgeQQjAAAAAJZHMAIAAABgeQQjAAAAAJZHMAIAAABgeQQjAAAAAJZHMAIAAABgeQQjAAAAAJZHMAIAAABgeQQjAAAAAJZHMAIAAABgef8HWA2Jk61Jt7wAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import confusion_matrix\n", + "import pandas as pd\n", + "\n", + "# Assuming df is your DataFrame\n", + "\n", + "# True labels and predictions\n", + "y_true = filtered_df[\"type\"]\n", + "y_pred = filtered_df[\"gpt_vote\"]\n", + "\n", + "# Compute the confusion matrix\n", + "conf_matrix = confusion_matrix(y_true, y_pred, labels=[\"leftvote\", \"rightvote\", \"tievote\", \"bothbad_vote\"])\n", + "\n", + "# Create a pandas DataFrame from the confusion matrix\n", + "conf_matrix_df = pd.DataFrame(conf_matrix, index=[\"leftvote\", \"rightvote\", \"tievote\", \"bothbad_vote\"], columns=[\"leftvote\", \"rightvote\", \"tievote\", \"bothbad_vote\"])\n", + "\n", + "# Plotting the heatmap\n", + "plt.figure(figsize=(10, 7))\n", + "sns.heatmap(conf_matrix_df, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n", + "plt.title(\"Arena Human vs GPT-4V Confusion Matrix\")\n", + "plt.xlabel(\"GPT-4V Vote\")\n", + "plt.ylabel(\"Arena Human Vote\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.5842911877394636\n", + "F1 Score (Macro): 0.514392348541452\n", + "F1 Score (Micro): 0.5842911877394636\n", + "F1 Score (Weighted): 0.5536668839130223\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score, f1_score\n", + "\n", + "# Assuming df is your DataFrame and it contains 'type' as true labels and 'gpt_vote' as predictions\n", + "y_true = filtered_df['type']\n", + "y_pred = filtered_df['gpt_vote']\n", + "\n", + "# Calculate accuracy\n", + "accuracy = accuracy_score(y_true, y_pred)\n", + "print(f'Accuracy: {accuracy}')\n", + "\n", + "# Calculate F1 score, here using 'macro' average to treat all classes equally\n", + "f1 = f1_score(y_true, y_pred, average='macro')\n", + "print(f'F1 Score (Macro): {f1}')\n", + "\n", + "# If you want to calculate F1 score with other averages, for example 'micro' or 'weighted', you can do:\n", + "f1_micro = f1_score(y_true, y_pred, average='micro')\n", + "print(f'F1 Score (Micro): {f1_micro}')\n", + "\n", + "f1_weighted = f1_score(y_true, y_pred, average='weighted')\n", + "print(f'F1 Score (Weighted): {f1_weighted}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cohen's Kappa Score: 0.3442144615665177\n" + ] + } + ], + "source": [ + "from sklearn.metrics import cohen_kappa_score\n", + "\n", + "# Assuming df is your DataFrame and it contains 'type' as true labels and 'gpt_vote' as predictions\n", + "y_true = filtered_df['type']\n", + "y_pred = filtered_df['gpt_vote']\n", + "\n", + "# Calculate Cohen's Kappa score\n", + "kappa = cohen_kappa_score(y_true, y_pred)\n", + "print(f'Cohen\\'s Kappa Score: {kappa}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy Score: 0.5842911877394636\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "accuracy = accuracy_score(y_true, y_pred)\n", + "print(f'Accuracy Score: {accuracy}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson Correlation Coefficient: 0.2880096104357029\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Assuming filtered_df is your DataFrame and it contains 'type' and 'gpt_vote' columns\n", + "# Convert 'type' and 'gpt_vote' to categorical codes\n", + "filtered_df['type_int'] = pd.factorize(filtered_df['type'])[0]\n", + "filtered_df['gpt_vote_int'] = pd.factorize(filtered_df['gpt_vote'])[0]\n", + "\n", + "# Now you can calculate Pearson correlation between these new integer columns\n", + "pearson_correlation = filtered_df['type_int'].corr(filtered_df['gpt_vote_int'])\n", + "print(f'Pearson Correlation Coefficient: {pearson_correlation}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "otask", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/arena_elo/generation_model_info.json b/arena_elo/generation_model_info.json new file mode 100644 index 0000000000000000000000000000000000000000..b5bdac320171e8147f385a4155a2715de3b9a60f --- /dev/null +++ b/arena_elo/generation_model_info.json @@ -0,0 +1,42 @@ +{ + "LCM": { + "Link": "https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7", + "License": "MIT License", + "Organization": "Tsinghua University" + }, + "Playground v2": { + "Link": "https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic", + "License": "Playground v2 Community License", + "Organization": "Playground" + }, + "OpenJourney": { + "Link": "https://huggingface.co/prompthero/openjourney", + "License": "creativeml-openrail-m", + "Organization": "PromptHero" + }, + "SDXLTurbo": { + "Link": "https://huggingface.co/stabilityai/sdxl-turbo", + "License": "sai-nc-community (other)", + "Organization": "Stability AI" + }, + "SDXL": { + "Link": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", + "License": "openrail++", + "Organization": "Stability AI" + }, + "PixArtAlpha": { + "Link": "https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS", + "License": "openrail++", + "Organization": "PixArt-alpha" + }, + "SDXLLightning": { + "Link": "https://huggingface.co/ByteDance/SDXL-Lightning", + "License": "openrail++", + "Organization": "ByteDance" + }, + "StableCascade": { + "Link": "https://huggingface.co/stabilityai/stable-cascade", + "License": "stable-cascade-nc-community (other)", + "Organization": "Stability AI" + } +} \ No newline at end of file diff --git a/arena_elo/get_latest_data.sh b/arena_elo/get_latest_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..e1d9229b5267ac89d5dba442d78a3a04e7829547 --- /dev/null +++ b/arena_elo/get_latest_data.sh @@ -0,0 +1,17 @@ + +# set LOGDIR to default if not set before +if [ -z "$LOGDIR" ]; then + export LOGDIR="./vision-arena-logs" +fi +mkdir -p results + + +# # for battle data +python -m elo_rating.clean_battle_data --model_infos_file "./model_infos.json" --mode conv_release +battle_cutoff_date=`cat cut_off_date.txt` && rm cut_off_date.txt && echo "Battle data last updated on $battle_cutoff_date" + +mkdir -p ./results/latest +mkdir -p ./results/$battle_cutoff_date && mv ./clean_battle_conv_$battle_cutoff_date.json ./results/$battle_cutoff_date/clean_battle_conv.json +cp ./results/$battle_cutoff_date/clean_battle_conv.json ./results/latest/clean_battle_conv.json + +echo "Battle data last updated on $battle_cutoff_date" >> ./results/latest/latest_updated_date.txt diff --git a/arena_elo/pyproject.toml b/arena_elo/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..215c759e4659b366073cf278ee17b67e3960f181 --- /dev/null +++ b/arena_elo/pyproject.toml @@ -0,0 +1,28 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "arena_elo" +version = "0.2.35" +description = "Elo rating system for WildVision Bench Arena" +readme = "README.md" +requires-python = ">=3.9" +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: Apache Software License", +] +dependencies = [ + "numpy", "prompt_toolkit>=3.0.0", "uvicorn","polyglot", "pyicu", "pycld2", "morfessor", "scikit-learn", + "pytz", "tqdm", "pandas", "plotly", "fire", "Pillow" +] + +[project.urls] +"Homepage" = "https://github.com/WildVision-Bench/Arena-Elo" +"Bug Tracker" = "https://github.com/WildVision-Bench/Arena-Elo/issues" + +[tool.setuptools.packages.find] +exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"] + +[tool.wheel] +exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"] \ No newline at end of file diff --git a/arena_elo/requirements.txt b/arena_elo/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..0b55ee1857cb4321f1d15bf82ac4d19d2f02fb9e --- /dev/null +++ b/arena_elo/requirements.txt @@ -0,0 +1,28 @@ +-e git+https://github.com/WildVision-Bench/Arena-Elo.git@9dc2fa8543a2e9eda3d5bc01c2212fdfcdd4bfb5#egg=arena_elo +click==8.1.7 +fire==0.5.0 +h11==0.14.0 +joblib==1.3.2 +Morfessor==2.0.6 +numpy==1.26.4 +packaging==23.2 +pandas==2.2.0 +pillow==10.2.0 +plotly==5.18.0 +polyglot==16.7.4 +prompt-toolkit==3.0.43 +pycld2==0.41 +PyICU==2.12 +python-dateutil==2.8.2 +pytz==2024.1 +scikit-learn==1.4.0 +scipy==1.12.0 +six==1.16.0 +tenacity==8.2.3 +termcolor==2.4.0 +threadpoolctl==3.2.0 +tqdm==4.66.2 +typing_extensions==4.9.0 +tzdata==2024.1 +uvicorn==0.27.1 +wcwidth==0.2.13 diff --git a/arena_elo/results/20240220/elo_results_image_editing.pkl b/arena_elo/results/20240220/elo_results_image_editing.pkl new file mode 100644 index 0000000000000000000000000000000000000000..66e7f59d1d6fa88d9aced1331bbf256fba4d3f98 --- /dev/null +++ b/arena_elo/results/20240220/elo_results_image_editing.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f41023a65a4dc1831a482dfa6098ccd528af9de297a1ea518881d49ce2885f0e +size 57121 diff --git a/arena_elo/results/20240220/elo_results_t2i_generation.pkl b/arena_elo/results/20240220/elo_results_t2i_generation.pkl new file mode 100644 index 0000000000000000000000000000000000000000..8e76b085ed05b4b89d3c4ea5cd1f1ad435a3e670 --- /dev/null +++ b/arena_elo/results/20240220/elo_results_t2i_generation.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f383f3920ef3834f6e6ec213691a955992b4ce49ccaf7658c0b35ff72a2219d3 +size 54505 diff --git a/arena_elo/results/20240220/image_editing_leaderboard.csv b/arena_elo/results/20240220/image_editing_leaderboard.csv new file mode 100644 index 0000000000000000000000000000000000000000..18e79da9ad60f00a8838705d1ad74b20c9b0c1cf --- /dev/null +++ b/arena_elo/results/20240220/image_editing_leaderboard.csv @@ -0,0 +1,8 @@ +key,Model,Arena Elo rating (anony),Arena Elo rating (full),License,Organization,Link +Prompt2prompt,Prompt2prompt,1252.820838097007,1216.6489026518666,Apache-2.0,"Google, Tel Aviv University",https://prompt-to-prompt.github.io +PNP,PNP,1175.6261555831445,1171.3279007979363,-,Weizmann Institute of Science,https://github.com/MichalGeyer/plug-and-play +InstructPix2Pix,InstructPix2Pix,1155.8431458813104,1142.6827834982837,"Copyright 2023 Timothy Brooks, Aleksander Holynski, Alexei A. Efros","University of California, Berkeley",https://www.timothybrooks.com/instruct-pix2pix +MagicBrush,MagicBrush,1051.428411953954,1089.4499296239383,CC-BY-4.0,"The Ohio State University, University of Waterloo",https://osu-nlp-group.github.io/MagicBrush +Pix2PixZero,Pix2PixZero,955.5903260059122,929.2296611307636,MIT License,"Carnegie Mellon University, Adobe Research",https://pix2pixzero.github.io +CycleDiffusion,CycleDiffusion,771.4360186105207,753.4930725653142,X11,Carnegie Mellon University,https://github.com/ChenWu98/cycle-diffusion +SDEdit,SDEdit,637.2551038681513,697.1677497318974,MIT License,Stanford University,https://sde-image-editing.github.io diff --git a/arena_elo/results/20240220/t2i_generation_leaderboard.csv b/arena_elo/results/20240220/t2i_generation_leaderboard.csv new file mode 100644 index 0000000000000000000000000000000000000000..9c2e583f47e80ab4fde1327657420617056e3d1d --- /dev/null +++ b/arena_elo/results/20240220/t2i_generation_leaderboard.csv @@ -0,0 +1,7 @@ +key,Model,Arena Elo rating (anony),Arena Elo rating (full),License,Organization,Link +PlayGroundV2,PlayGroundV2,1151.1834096302248,1150.901721636401,Playground v2 Community License,Playground,https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic +PixArtAlpha,PixArtAlpha,1078.3583466674136,1069.815012597113,openrail++,PixArt-alpha,https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS +SDXL,SDXL,1027.258044463298,1035.47732509915,openrail++,Stability AI,https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 +SDXLTurbo,SDXLTurbo,972.0904914158416,969.4933207298967,sai-nc-community (other),Stability AI,https://huggingface.co/stabilityai/sdxl-turbo +OpenJourney,OpenJourney,921.3424873878607,906.3184453708288,creativeml-openrail-m,PromptHero,https://huggingface.co/prompthero/openjourney +LCM,LCM,849.7672204353615,868.2154196730218,MIT License,Tsinghua University,https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7 diff --git a/arena_elo/results/20240315/clean_battle_image_editing.json b/arena_elo/results/20240315/clean_battle_image_editing.json new file mode 100644 index 0000000000000000000000000000000000000000..f6c320ac1641c7c15524efb33bf551d6f8ee4f7d --- /dev/null +++ b/arena_elo/results/20240315/clean_battle_image_editing.json @@ -0,0 +1,794 @@ +[ + { + "model_a": "CycleDiffusion", + "model_b": "InstructPix2Pix", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707712630.872 + }, + { + "model_a": "CycleDiffusion", + "model_b": "InstructPix2Pix", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": false, + "tstamp": 1707712699.668 + }, + { + "model_a": "Pix2PixZero", + "model_b": "MagicBrush", + "winner": "model_a", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707712896.0427 + }, + { + "model_a": "CycleDiffusion", + "model_b": "InstructPix2Pix", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": false, + "tstamp": 1707712929.7061 + }, + { + "model_a": "CycleDiffusion", + "model_b": "InstructPix2Pix", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707713147.0445 + }, + { + "model_a": "CycleDiffusion", + "model_b": "PNP", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707713198.9284 + }, + { + "model_a": "CycleDiffusion", + "model_b": "Prompt2prompt", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707713210.1306 + }, + { + "model_a": "Prompt2prompt", + "model_b": "SDEdit", + "winner": "model_a", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707713747.5115 + }, + { + "model_a": "PNP", + "model_b": "Pix2PixZero", + "winner": "model_a", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707715613.7226 + }, + { + "model_a": "CycleDiffusion", + "model_b": "MagicBrush", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707765708.2644 + }, + { + "model_a": "PNP", + 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"judge": "arena_user_::1", + "anony": true, + "tstamp": 1707784983.9581 + }, + { + "model_a": "MagicBrush", + "model_b": "SDEdit", + "winner": "model_a", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707785277.16 + }, + { + "model_a": "MagicBrush", + "model_b": "SDEdit", + "winner": "model_a", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707795299.0619 + }, + { + "model_a": "MagicBrush", + "model_b": "SDEdit", + "winner": "tie (bothbad)", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707795798.752 + }, + { + "model_a": "SDEdit", + "model_b": "Prompt2prompt", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": false, + "tstamp": 1707796435.7996 + }, + { + "model_a": "SDEdit", + "model_b": "CycleDiffusion", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": false, + "tstamp": 1707797278.7369 + }, + { + "model_a": "SDEdit", + "model_b": "CycleDiffusion", + "winner": "model_a", + "judge": "arena_user_::1", + "anony": false, + "tstamp": 1707797279.6004 + }, + { + "model_a": "SDEdit", + "model_b": "Prompt2prompt", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707805086.9739 + }, + { + "model_a": "PNP", + "model_b": "SDEdit", + "winner": "model_a", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707805220.3253 + }, + { + "model_a": "InstructPix2Pix", + "model_b": "CycleDiffusion", + "winner": "tie (bothbad)", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707805332.6322 + }, + { + "model_a": "InstructPix2Pix", + "model_b": "Prompt2prompt", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707805476.0509 + }, + { + "model_a": "InstructPix2Pix", + "model_b": "Prompt2prompt", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707818374.3438 + }, + { + "model_a": "PNP", + "model_b": "Prompt2prompt", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1707834631.9088 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"arena_user_::1", + "anony": true, + "tstamp": 1711535675.4714 + }, + { + "model_a": "StableCascade", + "model_b": "PixArtAlpha", + "winner": "model_b", + "judge": "arena_user_::1", + "anony": true, + "tstamp": 1711541359.3793 + } +] \ No newline at end of file diff --git a/arena_elo/results/latest/elo_results_image_editing.pkl b/arena_elo/results/latest/elo_results_image_editing.pkl new file mode 100644 index 0000000000000000000000000000000000000000..1d1f322dafc91bf14a278d69ba3edfab4623682a --- /dev/null +++ b/arena_elo/results/latest/elo_results_image_editing.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e528d30840c8a5787b0d2f08f27758b02f7eb718ccab695010b30df2127efe5e +size 57064 diff --git a/arena_elo/results/latest/elo_results_t2i_generation.pkl b/arena_elo/results/latest/elo_results_t2i_generation.pkl new file mode 100644 index 0000000000000000000000000000000000000000..8ea071a828abd5fee5b48d82b9b67a1ee33f2e7b --- /dev/null +++ b/arena_elo/results/latest/elo_results_t2i_generation.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fec01fe5af62dce3990634cffd1d926330ccbf170ef0c3b5d2f07fb06c4cf149 +size 65189 diff --git a/arena_elo/results/latest/image_editing_leaderboard.csv b/arena_elo/results/latest/image_editing_leaderboard.csv new file mode 100644 index 0000000000000000000000000000000000000000..d1eecdc067a562ece6ff1ad7dfa1b2b78d89a521 --- /dev/null +++ b/arena_elo/results/latest/image_editing_leaderboard.csv @@ -0,0 +1,8 @@ +key,Model,Arena Elo rating (anony),Arena Elo rating (full),License,Organization,Link +Prompt2prompt,Prompt2prompt,1227.5508595026165,1182.7966878908553,Apache-2.0,"Google, Tel Aviv University",https://prompt-to-prompt.github.io +InstructPix2Pix,InstructPix2Pix,1160.2057367236093,1104.6020920372373,"Copyright 2023 Timothy Brooks, Aleksander Holynski, Alexei A. Efros","University of California, Berkeley",https://www.timothybrooks.com/instruct-pix2pix +PNP,PNP,1142.693603173293,1163.4165011464634,-,Weizmann Institute of Science,https://github.com/MichalGeyer/plug-and-play +MagicBrush,MagicBrush,1053.1728944865915,1104.2259166081321,CC-BY-4.0,"The Ohio State University, University of Waterloo",https://osu-nlp-group.github.io/MagicBrush +Pix2PixZero,Pix2PixZero,918.6047552604578,899.6573774461386,MIT License,"Carnegie Mellon University, Adobe Research",https://pix2pixzero.github.io +CycleDiffusion,CycleDiffusion,865.0529105743963,820.8487760803515,X11,Carnegie Mellon University,https://github.com/ChenWu98/cycle-diffusion +SDEdit,SDEdit,632.7192402790356,724.4526487908217,MIT License,Stanford University,https://sde-image-editing.github.io diff --git a/arena_elo/results/latest/t2i_generation_leaderboard.csv b/arena_elo/results/latest/t2i_generation_leaderboard.csv new file mode 100644 index 0000000000000000000000000000000000000000..25bfffa9525814d27c91a3f0ae2c6a97659da5f7 --- /dev/null +++ b/arena_elo/results/latest/t2i_generation_leaderboard.csv @@ -0,0 +1,11 @@ +key,Model,Arena Elo rating (anony),Arena Elo rating (full),License,Organization,Link +Playground v2.5,Playground v2.5,1212.4660228554317,1233.021110469063,N/A,N/A,N/A +StableCascade,StableCascade,1098.8180832734447,1081.4707812969855,stable-cascade-nc-community (other),Stability AI,https://huggingface.co/stabilityai/stable-cascade +PlayGroundV2,PlayGroundV2,1089.993871580802,1088.6262085724481,N/A,N/A,N/A +Playground v2,Playground v2,1049.6156124554975,1051.618375116693,Playground v2 Community License,Playground,https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic +SDXLLightning,SDXLLightning,1036.8582186059539,1039.3079223370821,openrail++,ByteDance,https://huggingface.co/ByteDance/SDXL-Lightning +PixArtAlpha,PixArtAlpha,1016.2085497703334,1002.5100184720693,openrail++,PixArt-alpha,https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS +SDXL,SDXL,960.5073412035289,965.3037978455568,openrail++,Stability AI,https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 +SDXLTurbo,SDXLTurbo,907.997473382927,910.1644152252661,sai-nc-community (other),Stability AI,https://huggingface.co/stabilityai/sdxl-turbo +OpenJourney,OpenJourney,836.9689192463355,827.9470053715127,creativeml-openrail-m,PromptHero,https://huggingface.co/prompthero/openjourney +LCM,LCM,790.5659076257482,805.8155782210948,MIT License,Tsinghua University,https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7 diff --git a/arena_elo/simple_test.py b/arena_elo/simple_test.py new file mode 100644 index 0000000000000000000000000000000000000000..6e52ac57444e7fc3e53991379de196e3e9844955 --- /dev/null +++ b/arena_elo/simple_test.py @@ -0,0 +1,16 @@ +import pickle +with open("./results/latest/elo_results.pkl",'rb') as f: + data = pickle.load(f) + print() + df = data["anony"]["leaderboard_table_df"] + # sort by rating + df = df.sort_values(by=["rating"], ascending=False) + print(df) + + print() + + df = data["full"]["leaderboard_table_df"] + # sort by rating + df = df.sort_values(by=["rating"], ascending=False) + print(df) + print('done') \ No newline at end of file diff --git a/arena_elo/update_elo_rating.sh b/arena_elo/update_elo_rating.sh new file mode 100644 index 0000000000000000000000000000000000000000..2be68a1a3f441a165c9614e034b9c8d769506684 --- /dev/null +++ b/arena_elo/update_elo_rating.sh @@ -0,0 +1,49 @@ +# set LOGDIR to default if not set before +if [ -z "$LOGDIR" ]; then + echo "LOGDIR is not set. Please set LOGDIR to the directory where the logs will be stored. Command: export LOGDIR=/path/to/logdir" + exit 1 +fi + +mkdir -p results + +# # for battle data +python -m elo_rating.clean_battle_data --task_name "image_editing" +edition_battle_cutoff_date=`cat cut_off_date.txt` && rm cut_off_date.txt && echo "Image editing battle data last updated on $edition_battle_cutoff_date" + +python -m elo_rating.clean_battle_data --task_name "t2i_generation" +generation_battle_cutoff_date=`cat cut_off_date.txt` && rm cut_off_date.txt && echo "T2I image generation battle data last updated on $generation_battle_cutoff_date" + +mkdir -p ./results/$edition_battle_cutoff_date +mkdir -p ./results/$generation_battle_cutoff_date + +cp clean_battle_image_editing_$edition_battle_cutoff_date.json ./results/latest/ +cp clean_battle_t2i_generation_$generation_battle_cutoff_date.json ./results/latest/ +mv clean_battle_image_editing_$edition_battle_cutoff_date.json ./results/$edition_battle_cutoff_date/clean_battle_image_editing.json +mv clean_battle_t2i_generation_$generation_battle_cutoff_date.json ./results/$generation_battle_cutoff_date/clean_battle_t2i_generation.json + + +python3 -m elo_rating.elo_analysis --clean-battle-file clean_battle_image_editing_$edition_battle_cutoff_date.json +mv ./elo_results_$edition_battle_cutoff_date.pkl ./results/$edition_battle_cutoff_date/elo_results_image_editing.pkl + +python3 -m elo_rating.elo_analysis --clean-battle-file clean_battle_t2i_generation_$generation_battle_cutoff_date.json +mv ./elo_results_$generation_battle_cutoff_date.pkl ./results/$generation_battle_cutoff_date/elo_results_t2i_generation.pkl + +# generat the leaderboard + +python -m elo_rating.generate_leaderboard \ + --model_info_file "./edition_model_info.json" \ + --elo_rating_pkl "./results/$edition_battle_cutoff_date/elo_results_image_editing.pkl" \ + --output_csv "./results/$edition_battle_cutoff_date/image_editing_leaderboard.csv" + +python -m elo_rating.generate_leaderboard \ + --model_info_file "./generation_model_info.json" \ + --elo_rating_pkl "./results/$generation_battle_cutoff_date/elo_results_t2i_generation.pkl" \ + --output_csv "./results/$generation_battle_cutoff_date/t2i_generation_leaderboard.csv" + +mkdir -p ./results/latest +cp ./results/$edition_battle_cutoff_date/image_editing_leaderboard.csv ./results/latest/image_editing_leaderboard.csv +cp ./results/$generation_battle_cutoff_date/t2i_generation_leaderboard.csv ./results/latest/t2i_generation_leaderboard.csv +cp ./results/$edition_battle_cutoff_date/elo_results_image_editing.pkl ./results/latest/elo_results_image_editing.pkl +cp ./results/$generation_battle_cutoff_date/elo_results_t2i_generation.pkl ./results/latest/elo_results_t2i_generation.pkl + + diff --git a/examples/.DS_Store b/examples/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 Binary files /dev/null and b/examples/.DS_Store differ diff --git a/examples/banana.jpg 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-1,22 +0,0 @@ - - - - - - Vision Arena by WildVision Team - - - - - - diff --git a/model/__init__.py b/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/model/fal_api_utils.py b/model/fal_api_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f6c2042420954b2d59478de3bef9f64ae634dad5 --- /dev/null +++ b/model/fal_api_utils.py @@ -0,0 +1,49 @@ +import fal + +class FalModel(): + def __init__(self, model_name, model_type): + self.model_name = model_name + self.modle_type = model_type + + def __call__(self, *args, **kwargs): + + if self.model_type == "text2image": + assert "prompt" in kwargs, "prompt is required for text2image model" + handler = fal.apps.submit( + f"fal-ai/{self.model_name}", + arguments={ + "prompt": kwargs["prompt"] + }, + ) + + for event in handler.iter_events(): + if isinstance(event, fal.apps.InProgress): + print('Request in progress') + print(event.logs) + + result = handler.get() + return result + elif self.model_type == "image2image": + assert "image" in kwargs or "image_url" in kwargs, "image or image_url is required for image2image model" + if "image" in kwargs: + image_url = None + pass + handler = fal.apps.submit( + f"fal-ai/{self.model_name}", + arguments={ + "image_url": image_url + }, + ) + + for event in handler.iter_events(): + if isinstance(event, fal.apps.InProgress): + print('Request in progress') + print(event.logs) + + result = handler.get() + return result + else: + raise ValueError("model_type must be text2image or image2image") + +def load_fal_model(model_name, model_type): + return FalModel(model_name, model_type) \ No newline at end of file diff --git a/model/imagenhub_utils.py b/model/imagenhub_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f1840f577e37ad22d3be2a6e6e1de417d44902d4 --- /dev/null +++ b/model/imagenhub_utils.py @@ -0,0 +1,11 @@ +import imagen_hub + +class ImagenHubModel(): + def __init__(self, model_name): + self.model = imagen_hub.load(model_name) + + def __call__(self, *args, **kwargs): + return self.model.infer_one_image(*args, **kwargs) + +def load_imagenhub_model(model_name, model_type=None): + return ImagenHubModel(model_name) \ No newline at end of file diff --git a/model/model_manager.py b/model/model_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..acc83c9170e50eb978505a18f121069f9cbb718a --- /dev/null +++ b/model/model_manager.py @@ -0,0 +1,117 @@ +import concurrent.futures +import random +import gradio as gr +# from fal_api_utils import load_fal_model +from .imagenhub_utils import load_imagenhub_model +import spaces +import requests +import io, base64, json +from PIL import Image + +IMAGE_GENERATION_MODELS = ['imagenhub_LCM_generation','imagenhub_SDXLTurbo_generation','imagenhub_SDXL_generation', 'imagenhub_PixArtAlpha_generation', + 'imagenhub_OpenJourney_generation','imagenhub_SDXLLightning_generation', 'imagenhub_StableCascade_generation', + 'imagenhub_PlayGroundV2_generation', 'imagenhub_PlayGroundV2.5_generation'] +IMAGE_EDITION_MODELS = ['imagenhub_CycleDiffusion_edition', 'imagenhub_Pix2PixZero_edition', 'imagenhub_Prompt2prompt_edition', + 'imagenhub_SDEdit_edition', 'imagenhub_InstructPix2Pix_edition', 'imagenhub_MagicBrush_edition', 'imagenhub_PNP_edition'] + +class ModelManager: + def __init__(self): + self.model_ig_list = IMAGE_GENERATION_MODELS + self.model_ie_list = IMAGE_EDITION_MODELS + self.loaded_models = {} + @spaces.GPU + def load_model_pipe(self, model_name): + model_source, model_name, model_type = model_name.split("_") + if not model_name in self.loaded_models: + if model_source == "imagenhub": + pipe = load_imagenhub_model(model_name) + # elif model_source == "fal": + # pipe = load_fal_model(model_name, model_type) + else: + raise ValueError(f"Model source {model_source} not supported") + self.loaded_models[model_name] = pipe + else: + pipe = self.loaded_models[model_name] + return pipe + + def generate_image_playground(self, model_name, prompt): + if model_name == "imagenhub_PlayGroundV2_generation": + model_name = "Playground_v2" + elif model_name == "imagenhub_PlayGroundV2.5_generation": + model_name = "Playground_v2.5" + + headers = { + 'Content-Type': 'application/json', + 'Authorization': 'Bearer pg_0061b0a63475918714c4be28ec9a4a861a5012b57b12b77adaee97677cb35a87', + } + + data = json.dumps({"prompt": prompt, "filter_model": model_name, "scheduler": "DPMPP_2M_K", "guidance_scale": 3}) + + response = requests.post('https://playground.com/api/models/external/v1', headers=headers, data=data) + response.raise_for_status() + json_obj = response.json() + image_base64 = json_obj['images'][0] + img = Image.open(io.BytesIO(base64.decodebytes(bytes(image_base64, "utf-8")))) + + return img + @spaces.GPU + def generate_image_ig(self, prompt, model_name): + if 'playground' in model_name.lower(): + result = self.generate_image_playground(model_name=model_name, prompt=prompt) + else: + pipe = self.load_model_pipe(model_name) + result = pipe(prompt=prompt) + return result + # @spaces.GPU + def generate_image_ig_parallel_anony(self, prompt, model_A, model_B): + if model_A == "" and model_B == "": + model_names = random.sample([model for model in self.model_ig_list], 2) + else: + model_names = [model_A, model_B] + + results = [] + with concurrent.futures.ThreadPoolExecutor() as executor: + future_to_result = {executor.submit(self.generate_image_ig, prompt, model): model for model in model_names} + for future in concurrent.futures.as_completed(future_to_result): + result = future.result() + results.append(result) + return results[0], results[1], model_names[0], model_names[1] + # @spaces.GPU + def generate_image_ig_parallel(self, prompt, model_A, model_B): + results = [] + model_names = [model_A, model_B] + with concurrent.futures.ThreadPoolExecutor() as executor: + future_to_result = {executor.submit(self.generate_image_ig, prompt, model): model for model in model_names} + for future in concurrent.futures.as_completed(future_to_result): + result = future.result() + results.append(result) + return results[0], results[1] + @spaces.GPU + def generate_image_ie(self, textbox_source, textbox_target, textbox_instruct, source_image, model_name): + pipe = self.load_model_pipe(model_name) + result = pipe(src_image = source_image, src_prompt = textbox_source, target_prompt = textbox_target, instruct_prompt = textbox_instruct) + return result + # @spaces.GPU + def generate_image_ie_parallel(self, textbox_source, textbox_target, textbox_instruct, source_image, model_A, model_B): + results = [] + model_names = [model_A, model_B] + with concurrent.futures.ThreadPoolExecutor() as executor: + future_to_result = {executor.submit(self.generate_image_ie, textbox_source, textbox_target, textbox_instruct, source_image, model): model for model in model_names} + for future in concurrent.futures.as_completed(future_to_result): + result = future.result() + results.append(result) + return results[0], results[1] + # @spaces.GPU + def generate_image_ie_parallel_anony(self, textbox_source, textbox_target, textbox_instruct, source_image, model_A, model_B): + if model_A == "" and model_B == "": + model_names = random.sample([model for model in self.model_ie_list], 2) + else: + model_names = [model_A, model_B] + results = [] + # model_names = [model_A, model_B] + with concurrent.futures.ThreadPoolExecutor() as executor: + future_to_result = {executor.submit(self.generate_image_ie, textbox_source, textbox_target, textbox_instruct, source_image, model): model for model in model_names} + for future in concurrent.futures.as_completed(future_to_result): + result = future.result() + results.append(result) + return results[0], results[1], model_names[0], model_names[1] \ No newline at end of file diff --git a/model/model_registry.py b/model/model_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..39c64ecf67fe113f4e832c35df93228560c19605 --- /dev/null +++ b/model/model_registry.py @@ -0,0 +1,173 @@ +from collections import namedtuple +from typing import List + +ModelInfo = namedtuple("ModelInfo", ["simple_name", "link", "description"]) +model_info = {} + +def register_model_info( + full_names: List[str], simple_name: str, link: str, description: str +): + info = ModelInfo(simple_name, link, description) + + for full_name in full_names: + model_info[full_name] = info + +def get_model_info(name: str) -> ModelInfo: + if name in model_info: + return model_info[name] + else: + # To fix this, please use `register_model_info` to register your model + return ModelInfo( + name, "", "Register the description at fastchat/model/model_registry.py" + ) + +def get_model_description_md(model_list): + model_description_md = """ +| | | | +| ---- | ---- | ---- | +""" + ct = 0 + visited = set() + for i, name in enumerate(model_list): + minfo = get_model_info(name) + if minfo.simple_name in visited: + continue + visited.add(minfo.simple_name) + one_model_md = f"[{minfo.simple_name}]({minfo.link}): {minfo.description}" + + if ct % 3 == 0: + model_description_md += "|" + model_description_md += f" {one_model_md} |" + if ct % 3 == 2: + model_description_md += "\n" + ct += 1 + return model_description_md + +# regist image generation models + +register_model_info( + ["imagenhub_LCM_generation"], + "LCM", + "https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7", + "Latent Consistency Models.", +) + +register_model_info( + ["imagenhub_PlayGroundV2_generation"], + "Playground v2", + "https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic", + "Playground v2 – 1024px Aesthetic Model", +) + +register_model_info( + ["imagenhub_PlayGroundV2.5_generation"], + "Playground v2.5", + "https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic", + "Playground v2.5 is the state-of-the-art open-source model in aesthetic quality", +) + +register_model_info( + ["imagenhub_OpenJourney_generation"], + "Openjourney", + "https://huggingface.co/prompthero/openjourney", + "Openjourney is an open source Stable Diffusion fine tuned model on Midjourney images, by PromptHero.", +) + +register_model_info( + ["imagenhub_SDXLTurbo_generation"], + "SDXLTurbo", + "https://huggingface.co/stabilityai/sdxl-turbo", + "SDXL-Turbo is a fast generative text-to-image model.", +) + +register_model_info( + ["imagenhub_SDXL_generation"], + "SDXL", + "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", + "SDXL is a Latent Diffusion Model that uses two fixed, pretrained text encoders.", +) + +register_model_info( + ["imagenhub_PixArtAlpha_generation"], + "PixArtAlpha", + "https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS", + "Pixart-Ξ± consists of pure transformer blocks for latent diffusion.", +) + +register_model_info( + ["imagenhub_SDXLLightning_generation"], + "SDXL-Lightning", + "https://huggingface.co/ByteDance/SDXL-Lightning", + "SDXL-Lightning is a lightning-fast text-to-image generation model.", +) + +register_model_info( + ["imagenhub_StableCascade_generation"], + "StableCascade", + "https://huggingface.co/stabilityai/stable-cascade", + "StableCascade is built upon the WΓΌrstchen architecture and working at a much smaller latent space.", +) + +# regist image edition models +register_model_info( + ["imagenhub_CycleDiffusion_edition"], + "CycleDiffusion", + "https://github.com/ChenWu98/cycle-diffusion?tab=readme-ov-file", + "A latent space for stochastic diffusion models.", +) + +register_model_info( + ["imagenhub_Pix2PixZero_edition"], + "Pix2PixZero", + "https://pix2pixzero.github.io/", + "A zero-shot Image-to-Image translation model.", +) + +register_model_info( + ["imagenhub_Prompt2prompt_edition"], + "Prompt2prompt", + "https://prompt-to-prompt.github.io/", + "Image Editing with Cross-Attention Control.", +) + +# register_model_info( +# ["imagenhub_SDEdit_edition"], +# "SDEdit", +# "", +# "xxx", +# ) + +register_model_info( + ["imagenhub_InstructPix2Pix_edition"], + "InstructPix2Pix", + "https://www.timothybrooks.com/instruct-pix2pix", + "An instruction-based image editing model.", +) + +register_model_info( + ["imagenhub_MagicBrush_edition"], + "MagicBrush", + "https://osu-nlp-group.github.io/MagicBrush/", + "Manually Annotated Dataset for Instruction-Guided Image Editing.", +) + +register_model_info( + ["imagenhub_PNP_edition"], + "PNP", + "https://github.com/MichalGeyer/plug-and-play", + "Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation.", +) + +register_model_info( + ["fal_stable_cascade"], + "StableCascade", + "https://fal.ai/models/stable-cascade/api", + "StableCascade is a generative model that can generate high-quality images from text prompts.", +) + + +models = ['imagenhub_LCM_generation','imagenhub_SDXLTurbo_generation','imagenhub_SDXL_generation', + 'imagenhub_OpenJourney_generation','imagenhub_PixArtAlpha_generation','imagenhub_SDXLLightning_generation', + 'imagenhub_StableCascade_generation','imagenhub_PlaygroundV2_generation', 'fal_Playground-v25_generation', 'fal_stable_cascade', + 'imagenhub_CycleDiffusion_edition', 'imagenhub_Pix2PixZero_edition', 'imagenhub_Prompt2prompt_edition', + 'imagenhub_SDEdit_edition', 'imagenhub_InstructPix2Pix_edition', 'imagenhub_MagicBrush_edition', 'imagenhub_PNP_edition'] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3b20e767702a506571b282832eb53432744dffb3 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,50 @@ +flask +flask_restful +flask_cors +faiss-cpu +fire +h5py +xformers~=0.0.20 +numpy>=1.24.0 +pandas<2.0.0 +peft +torch +torchvision +torchaudio +jupyterlab>=4.0.2 +notebook>=6.5.4 +albumentations>=1.1.0 +opencv-python>=4.2.0 +pudb~=2019.2 +imageio>=2.14.1 +imageio-ffmpeg>=0.4.7 +pytorch-lightning>=1.5.9 +omegaconf~=2.1.1 +gradio==4.21.0 +pillow~=9.5.0 +einops>=0.4.1 +torch-fidelity>=0.3.0 +setuptools>=59.5.0 +transformers==4.37.2 +torchmetrics>=0.6.0 +lpips +dreamsim +image-reward +kornia>=0.6 +diffusers>=0.18.0 +accelerate>=0.20.3 +safetensors +datasets +tqdm>=4.64.1 +matplotlib>=3.7.1 +taming-transformers-rom1504~=0.0.6 +madgrad>=1.1 +-e git+https://github.com/openai/CLIP.git@main#egg=clip +dominate>=2.8.0 +-e git+https://github.com/CompVis/latent-diffusion.git#egg=latent-diffusion #ldm +openai +nltk~=3.8.1 +krippendorff +statsmodels +-e git+https://github.com/TIGER-AI-Lab/ImagenHub.git#egg=imagen-hub +plotly \ No newline at end of file diff --git a/serve/__init__.py b/serve/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/serve/constants.py b/serve/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..38aa36a53a3c49b8f3457a9329654da19dca2cab --- /dev/null +++ b/serve/constants.py @@ -0,0 +1,16 @@ +import os + +LOGDIR = os.getenv("LOGDIR", "./GenAI-Arena-hf-logs/vote_log") +IMAGE_DIR = os.getenv("IMAGE_DIR", f"{LOGDIR}/images") + +SERVER_PORT = os.getenv("SERVER_PORT", 7860) +ROOT_PATH = os.getenv("ROOT_PATH", None) +ELO_RESULTS_DIR = os.getenv("ELO_RESULTS_DIR", "./arena_elo/results/latest") + +LOG_SERVER = os.getenv("LOG_SERVER", "https://tigerai.ca") +LOG_SERVER_SUBDOAMIN = os.getenv("LOG_SERVER_SUBDIR", "GenAI-Arena-hf-logs") +LOG_SERVER_ADDR = os.getenv("LOG_SERVER_ADDR", f"{LOG_SERVER}/{LOG_SERVER_SUBDOAMIN}") +# LOG SERVER API ENDPOINTS +APPEND_JSON = "append_json" +SAVE_IMAGE = "save_image" + diff --git a/serve/gradio_web.py b/serve/gradio_web.py new file mode 100644 index 0000000000000000000000000000000000000000..e7578c302c14e0022e6f7438f6e8ffd92f4a437f --- /dev/null +++ b/serve/gradio_web.py @@ -0,0 +1,469 @@ +from .utils import * +from .vote_utils import ( + upvote_last_response_ig as upvote_last_response, + downvote_last_response_ig as downvote_last_response, + flag_last_response_ig as flag_last_response, + leftvote_last_response_igm as leftvote_last_response, + rightvote_last_response_igm as rightvote_last_response, + tievote_last_response_igm as tievote_last_response, + bothbad_vote_last_response_igm as bothbad_vote_last_response, + share_click_igm as share_click, + generate_ig, + generate_igm, + generate_igm_annoy, + share_js +) +from functools import partial + +def build_side_by_side_ui_anony(models): + notice_markdown = """ +# βš”οΈ GenAI-Arena βš”οΈ : Benchmarking Visual Generative Models in the Wild +| [GitHub](https://github.com/TIGER-AI-Lab/ImagenHub) | [Paper](https://arxiv.org/abs/2310.01596) | [Dataset](https://huggingface.co/ImagenHub) | +## πŸ“œ Rules +- Input prompt to two anonymous models in same area (e.g., LCM, SDXL, SDXL-turbo in Text-guided Image Generation Model, MagicBrush, InstructPix2Pix in Text-guided Image Editing Model) and vote for the better one! +- When the results are ready, click the button below to vote. +- Vote won't be counted if model identity is revealed during conversation. +- Click "New Round" to start a new round. + +## πŸ† Arena Elo +Find out who is the πŸ₯‡conditional image generation models! More models are going to be supported. + +## πŸ‘‡ Generating now! + +""" + + model_list = models.model_ig_list + + state0 = gr.State() + state1 = gr.State() + gen_func = partial(generate_igm_annoy, models.generate_image_ig_parallel_anony) + + gr.Markdown(notice_markdown, elem_id="notice_markdown") + + with gr.Group(elem_id="share-region-anony"): + with gr.Accordion("πŸ” Expand to see all Arena players", open=False): + model_description_md = get_model_description_md(model_list) + gr.Markdown(model_description_md, elem_id="model_description_markdown") + with gr.Row(): + with gr.Column(): + chatbot_left = gr.Image(height=512, width=512, label = "Model A") + with gr.Column(): + chatbot_right = gr.Image(height=512, width=512, label = "Model B") + + with gr.Row(): + with gr.Column(): + model_selector_left =gr.Markdown("") + with gr.Column(): + model_selector_right = gr.Markdown("") + with gr.Row(): + slow_warning = gr.Markdown("", elem_id="notice_markdown") + + with gr.Row(): + leftvote_btn = gr.Button( + value="πŸ‘ˆ A is better", visible=False, interactive=False + ) + rightvote_btn = gr.Button( + value="πŸ‘‰ B is better", visible=False, interactive=False + ) + tie_btn = gr.Button(value="🀝 Tie", visible=False, interactive=False) + bothbad_btn = gr.Button( + value="πŸ‘Ž Both are bad", visible=False, interactive=False + ) + + with gr.Row(): + textbox = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your prompt and press ENTER", + container=True, + elem_id="input_box", + ) + send_btn = gr.Button(value="Send", variant="primary", scale=0) + + with gr.Row(): + clear_btn = gr.Button(value="🎲 New Round", interactive=False) + regenerate_btn = gr.Button(value="πŸ”„ Regenerate", interactive=False) + share_btn = gr.Button(value="πŸ“· Share") + + gr.Markdown(acknowledgment_md, elem_id="ack_markdown") + + dummy_img_output = gr.Image(height=512, width=512, visible=False) + gr.Examples( + examples=[["a cute dog is playing a ball", os.path.join("./examples", "dog.jpg")], + ["Buildings on fire, old film still", os.path.join("./examples", "fire.jpg")], + ["Lonely evil bananas on a table, hard light chiaroscuro, realistic",os.path.join("./examples", "banana.jpg")], + ["A futuristic hopeful busy city, purple and green color scheme", os.path.join("./examples", "city.jpg")]], + inputs = [textbox, dummy_img_output]) + + btn_list = [leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, regenerate_btn, clear_btn,] + + + textbox.submit( + gen_func, + inputs=[state0, state1, textbox, model_selector_left, model_selector_right], + outputs=[state0, state1, chatbot_left, chatbot_right, model_selector_left, model_selector_right], + api_name="submit_btn_annony" + ).then( + enable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + send_btn.click( + gen_func, + inputs=[state0, state1, textbox, model_selector_left, model_selector_right], + outputs=[state0, state1, chatbot_left, chatbot_right, model_selector_left, model_selector_right], + api_name="send_btn_annony" + ).then( + enable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + + clear_btn.click( + clear_history_side_by_side_anony, + inputs=None, + outputs=[state0, state1, textbox, chatbot_left, chatbot_right, model_selector_left, model_selector_right], + api_name="clear_btn_annony" + ).then( + disable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + + regenerate_btn.click( + gen_func, + inputs=[state0, state1, textbox, model_selector_left, model_selector_right], + outputs=[state0, state1, chatbot_left, chatbot_right, model_selector_left, model_selector_right], + api_name="regenerate_btn_annony" + ).then( + enable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + + leftvote_btn.click( + leftvote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn] + ) + rightvote_btn.click( + rightvote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn] + ) + tie_btn.click( + tievote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn] + ) + bothbad_btn.click( + bothbad_vote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn] + ) + + + share_btn.click( + share_click, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[], + js=share_js + ) + + +def build_side_by_side_ui_named(models): + notice_markdown = """ +# βš”οΈ GenAI-Arena βš”οΈ : Benchmarking Visual Generative Models in the Wild +| [GitHub](https://github.com/TIGER-AI-Lab/ImagenHub) | [Paper](https://arxiv.org/abs/2310.01596) | [Dataset](https://huggingface.co/ImagenHub) | + +## πŸ“œ Rules +- Generate with any two selected models side-by-side and vote! +- Input prompt you want to generate. +- Click "Send" to submit the prompt. +- Click "Clear history" to start a new round. + +## πŸ€– Choose two models to compare +""" + model_list = models.model_ig_list + + state0 = gr.State() + state1 = gr.State() + gen_func = partial(generate_igm, models.generate_image_ig_parallel) + gr.Markdown(notice_markdown, elem_id="notice_markdown") + + with gr.Group(elem_id="share-region-named"): + with gr.Row(): + with gr.Column(): + model_selector_left = gr.Dropdown( + choices=model_list, + value=model_list[0] if len(model_list) > 0 else "", + interactive=True, + show_label=False, + container=False, + ) + with gr.Column(): + model_selector_right = gr.Dropdown( + choices=model_list, + value=model_list[1] if len(model_list) > 1 else "", + interactive=True, + show_label=False, + container=False, + ) + with gr.Row(): + with gr.Accordion("πŸ” Expand to see all model descriptions", open=False): + model_description_md = get_model_description_md(model_list) + gr.Markdown(model_description_md, elem_id="model_description_markdown") + + with gr.Row(): + with gr.Column(): + chatbot_left = gr.Image(height=512, width=512, label = "Model A") + with gr.Column(): + chatbot_right = gr.Image(height=512, width=512, label = "Model B") + with gr.Row(): + leftvote_btn = gr.Button( + value="πŸ‘ˆ A is better", visible=False, interactive=False + ) + rightvote_btn = gr.Button( + value="πŸ‘‰ B is better", visible=False, interactive=False + ) + tie_btn = gr.Button(value="🀝 Tie", visible=False, interactive=False) + bothbad_btn = gr.Button( + value="πŸ‘Ž Both are bad", visible=False, interactive=False + ) + + with gr.Row(): + textbox = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your prompt and press ENTER", + elem_id="input_box" + ) + send_btn = gr.Button(value="Send", variant="primary", scale=0) + + with gr.Row(): + clear_btn = gr.Button(value="πŸ—‘οΈ Clear history", interactive=False) + regenerate_btn = gr.Button(value="πŸ”„ Regenerate", interactive=False) + share_btn = gr.Button(value="πŸ“· Share") + + gr.Markdown(acknowledgment_md, elem_id="ack_markdown") + + dummy_img_output = gr.Image(height=512, width=512, visible=False) + gr.Examples( + examples=[["a cute dog is playing a ball", os.path.join("./examples", "dog.jpg")], + ["Buildings on fire, old film still", os.path.join("./examples", "fire.jpg")], + ["Lonely evil bananas on a table, hard light chiaroscuro, realistic",os.path.join("./examples", "banana.jpg")], + ["A futuristic hopeful busy city, purple and green color scheme", os.path.join("./examples", "city.jpg")]], + inputs = [textbox, dummy_img_output]) + + model_selector_left.change(clear_history_side_by_side, inputs=None, outputs=[state0, state1, textbox, chatbot_left, chatbot_right], api_name="model_selector_left_side_by_side") + model_selector_right.change(clear_history_side_by_side, inputs=None, outputs=[state0, state1, textbox, chatbot_left, chatbot_right], api_name="model_selector_right_side_by_side") + + btn_list = [leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, regenerate_btn, clear_btn] + + textbox.submit( + gen_func, + inputs=[state0, state1, textbox, model_selector_left, model_selector_right], + outputs=[state0, state1, chatbot_left, chatbot_right], + api_name="textbox_side_by_side" + ).then( + enable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + + send_btn.click( + gen_func, + inputs=[state0, state1, textbox, model_selector_left, model_selector_right], + outputs=[state0, state1, chatbot_left, chatbot_right], + api_name="send_side_by_side" + ).then( + enable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + regenerate_btn.click( + gen_func, + inputs=[state0, state1, textbox, model_selector_left, model_selector_right], + outputs=[state0, state1, chatbot_left, chatbot_right], + api_name="regenerate_side_by_side" + ).then( + enable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + + clear_btn.click( + clear_history_side_by_side, + inputs=None, + outputs=[state0, state1, textbox, chatbot_left, chatbot_right], + api_name="clear_btn_side_by_side" + ).then( + disable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + + leftvote_btn.click( + leftvote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn] + ) + rightvote_btn.click( + rightvote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn] + ) + tie_btn.click( + tievote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn] + ) + bothbad_btn.click( + bothbad_vote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn] + ) + + share_btn.click( + share_click, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[], + js=share_js + ) + +def build_single_model_ui(models, add_promotion_links=False): + promotion = ( + """ +- | [GitHub](https://github.com/TIGER-AI-Lab/ImagenHub) | [Paper](https://arxiv.org/abs/2310.01596) | [Dataset](https://huggingface.co/ImagenHub) | +""" + if add_promotion_links + else "" + ) + + notice_markdown = f""" +# πŸ”οΈ Play with Image Generation Models +{promotion} + +## πŸ€– Choose any model to generate +""" + + state = gr.State() + gen_func = partial(generate_ig, models.generate_image_ig) + gr.Markdown(notice_markdown, elem_id="notice_markdown") + + model_list = models.model_ig_list + + with gr.Row(elem_id="model_selector_row"): + model_selector = gr.Dropdown( + choices=model_list, + value=model_list[0] if len(model_list) > 0 else "", + interactive=True, + show_label=False + ) + + with gr.Row(): + with gr.Accordion( + "πŸ” Expand to see all model descriptions", + open=False, + elem_id="model_description_accordion", + ): + model_description_md = get_model_description_md(model_list) + gr.Markdown(model_description_md, elem_id="model_description_markdown") + + with gr.Row(): + textbox = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your prompt and press ENTER", + elem_id="input_box" + ) + + send_btn = gr.Button(value="Send", variant="primary", scale=0) + + with gr.Row(): + chatbot = gr.Image(height=512, width=512) + + with gr.Row() as button_row: + upvote_btn = gr.Button(value="πŸ‘ Upvote", interactive=False) + downvote_btn = gr.Button(value="πŸ‘Ž Downvote", interactive=False) + flag_btn = gr.Button(value="⚠️ Flag", interactive=False) + regenerate_btn = gr.Button(value="πŸ”„ Regenerate", interactive=False) + clear_btn = gr.Button(value="πŸ—‘οΈ Clear history", interactive=False) + + if add_promotion_links: + gr.Markdown(acknowledgment_md, elem_id="ack_markdown") + + dummy_img_output = gr.Image(height=512, width=512, visible=False) + gr.Examples( + examples=[["a cute dog is playing a ball", os.path.join("./examples", "dog.jpg")], + ["Buildings on fire, old film still", os.path.join("./examples", "fire.jpg")], + ["Lonely evil bananas on a table, hard light chiaroscuro, realistic",os.path.join("./examples", "banana.jpg")], + ["A futuristic hopeful busy city, purple and green color scheme", os.path.join("./examples", "city.jpg")]], + inputs = [textbox, dummy_img_output]) + + model_selector.change(clear_history, inputs=None, outputs=[state, textbox, chatbot], api_name="model_selector_single") + + + + btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn] + + + textbox.submit( + gen_func, + inputs=[state, textbox, model_selector], + outputs=[state, chatbot], + api_name="submit_btn_single", + show_progress = "full" + ).success( + enable_buttons, + inputs=None, + outputs=btn_list + ) + send_btn.click( + gen_func, + inputs=[state, textbox, model_selector], + outputs=[state, chatbot], + api_name="send_btn_single", + show_progress = "full" + ).success( + enable_buttons, + inputs=None, + outputs=btn_list + ) + + upvote_btn.click( + upvote_last_response, + inputs=[state, model_selector], + outputs=[textbox, upvote_btn, downvote_btn, flag_btn] + ) + + downvote_btn.click( + downvote_last_response, + inputs=[state, model_selector], + outputs=[textbox, upvote_btn, downvote_btn, flag_btn] + ) + flag_btn.click( + flag_last_response, + inputs=[state, model_selector], + outputs=[textbox, upvote_btn, downvote_btn, flag_btn] + ) + regenerate_btn.click( + gen_func, + inputs=[state, textbox, model_selector], + outputs=[state, chatbot], + api_name="regenerate_btn_single", + show_progress = "full" + ).success( + enable_buttons, + inputs=None, + outputs=btn_list + ) + clear_btn.click( + clear_history, + inputs=None, + outputs=[state, textbox, chatbot], + api_name="clear_history_single", + show_progress="full" + ).then( + disable_buttons, + inputs=None, + outputs=btn_list + ) + diff --git a/serve/gradio_web_image_editing.py b/serve/gradio_web_image_editing.py new file mode 100644 index 0000000000000000000000000000000000000000..0a83ba2c7947679db7acc680374018b06595a33a --- /dev/null +++ b/serve/gradio_web_image_editing.py @@ -0,0 +1,543 @@ +from .utils import * +from .vote_utils import ( + upvote_last_response_ie as upvote_last_response, + downvote_last_response_ie as downvote_last_response, + flag_last_response_ie as flag_last_response, + leftvote_last_response_iem as leftvote_last_response, + rightvote_last_response_iem as rightvote_last_response, + tievote_last_response_iem as tievote_last_response, + bothbad_vote_last_response_iem as bothbad_vote_last_response, + share_click_iem as share_click, + generate_ie, + generate_iem, + generate_iem_annoy, + share_js +) +from functools import partial + +def build_side_by_side_ui_anony_ie(models): + notice_markdown = """ +# βš”οΈ GenAI-Arena βš”οΈ : Benchmarking Visual Generative Models in the Wild +| [GitHub](https://github.com/TIGER-AI-Lab/ImagenHub) | [Paper](https://arxiv.org/abs/2310.01596) | [Dataset](https://huggingface.co/ImagenHub) | + +## πŸ“œ Rules +- Edit with two selected models side-by-side and vote! +- Upload a source image that you want to edit. +- Input source prompt, target prompt and instruct prompt. +- Wait to see the results after edition. +- Click "New Round" to start a new round. +- Vote won't be counted if model identity is revealed during generation. +- The model could output a totally black image or noise. It's not a bug but the failure case of the model. + +## πŸ† Arena Elo +Find out who is the πŸ₯‡conditional image edition models! + +## πŸ‘‡ Editing now! + +""" + + model_list = models.model_ie_list + + state0, state1 = gr.State(), gr.State() + gen_func = partial(generate_iem_annoy, models.generate_image_ie_parallel_anony) + gr.Markdown(notice_markdown, elem_id="notice_markdown") + + with gr.Group(elem_id="share-region-anony"): + with gr.Accordion("πŸ” Expand to see all Arena players", open=False): + model_description_md = get_model_description_md(model_list) + gr.Markdown(model_description_md, elem_id="model_description_markdown") + with gr.Row(): + with gr.Column(): + chatbot_left = gr.Image(height=512, width=512, type="pil") + with gr.Column(): + chatbot_right = gr.Image(height=512, width=512, type="pil") + + with gr.Row(): + with gr.Column(): + model_selector_left =gr.Markdown("") + with gr.Column(): + model_selector_right = gr.Markdown("") + with gr.Row(): + slow_warning = gr.Markdown("", elem_id="notice_markdown") + + with gr.Row(): + leftvote_btn = gr.Button( + value="πŸ‘ˆ A is better", visible=False, interactive=False + ) + rightvote_btn = gr.Button( + value="πŸ‘‰ B is better", visible=False, interactive=False + ) + tie_btn = gr.Button(value="🀝 Tie", visible=False, interactive=False) + bothbad_btn = gr.Button( + value="πŸ‘Ž Both are bad", visible=False, interactive=False + ) + + with gr.Row(): + + textbox_source = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your source prompt", + elem_id="input_box_s", + ) + textbox_target = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your target prompt", + elem_id="input_box_t", + ) + textbox_instruct = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your instruct prompt", + elem_id="input_box_t", + ) + + with gr.Row(): + source_image = gr.Image(type="pil") + send_btn = gr.Button(value="Send", variant="primary", scale=0) + + with gr.Row() as button_row: + clear_btn = gr.Button(value="🎲 New Round", interactive=False) + regenerate_btn = gr.Button(value="πŸ”„ Regenerate", interactive=False) + share_btn = gr.Button(value="πŸ“· Share") + + gr.Markdown(acknowledgment_md, elem_id="ack_markdown") + + gr.Examples( + examples=[ + ["a bowl of strawberries", "a bowl of oranges", "change strawberries to oranges", + os.path.join("./examples", "strawberries.jpg")], + ["a pig is eating an ice cream", "a rabbit is eating an ice cream", "change pig to rabbit", + os.path.join("./examples", "pig.jpg")], + ["a rubber duck in a swimming pool", "a rubber duck with a hat in a swimming pool", "add a hat to the duck", + os.path.join("./examples", "duck.jpg")], + ["a photo of a cat", "a photo of a mouse", "change cat to mouse", + os.path.join("./examples", "cat.jpeg")]], + inputs=[textbox_source, textbox_target, textbox_instruct, source_image]) + + # random_models = random.sample([model for model in model_list], 2) + # model_selector_left =gr.Markdown(random_models[0]) + # model_selector_right =gr.Markdown(random_models[1]) + + btn_list = [leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, regenerate_btn, clear_btn] + + send_btn.click( + gen_func, + inputs=[ + state0, + state1, + textbox_source, + textbox_target, + textbox_instruct, + source_image, + model_selector_left, + model_selector_right + ], + outputs=[state0, state1, chatbot_left, chatbot_right, model_selector_left, model_selector_right], + api_name="send_btn_side_by_side_anony" + ).then( + enable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + regenerate_btn.click( + gen_func, + inputs=[ + state0, + state1, + textbox_source, + textbox_target, + textbox_instruct, + source_image, + model_selector_left, + model_selector_right + ], + outputs=[state0, state1, chatbot_left, chatbot_right, model_selector_left, model_selector_right], + api_name="regenerate_btn_side_by_side_anony" + ).then( + enable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + + clear_btn.click( + clear_history_side_by_side_ie_anony, + inputs=None, + outputs=[state0, state1, textbox_source, textbox_target, textbox_instruct, + source_image, chatbot_left, chatbot_right, + model_selector_left, model_selector_right], + api_name="clear_btn_side_by_side_anony" + ).then( + disable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + vote_btns = [leftvote_btn, rightvote_btn, tie_btn, bothbad_btn] + input_list = [textbox_source, textbox_target, source_image, textbox_instruct] + leftvote_btn.click( + leftvote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[model_selector_left, model_selector_right] + input_list + vote_btns, + ) + rightvote_btn.click( + rightvote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[model_selector_left, model_selector_right] + input_list + vote_btns, + ) + tie_btn.click( + tievote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[model_selector_left, model_selector_right] + input_list + vote_btns, + ) + bothbad_btn.click( + bothbad_vote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[model_selector_left, model_selector_right] + input_list + vote_btns, + ) + share_btn.click( + share_click, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[], + js=share_js + ) + + + + +def build_side_by_side_ui_named_ie(models): + notice_markdown = """ +# βš”οΈ GenAI-Arena βš”οΈ : Benchmarking Visual Generative Models in the Wild +| [GitHub](https://github.com/TIGER-AI-Lab/ImagenHub) | [Paper](https://arxiv.org/abs/2310.01596) | [Dataset](https://huggingface.co/ImagenHub) | + +## πŸ“œ Rules +- Edit with two selected models side-by-side and vote! +- Upload a source image that you want to edit. +- Input source prompt, target prompt and instruct prompt. +- Wait to see the results after edition. +- Click "Clear history" to start a new round. +- The model could output a totally black image or noise. It's not a bug but the failure case of the model. + +## πŸ€– Choose two models to compare +""" + + model_list = models.model_ie_list + + state0, state1 = gr.State(), gr.State() + gen_func = partial(generate_iem, models.generate_image_ie_parallel) + gr.Markdown(notice_markdown, elem_id="notice_markdown") + + with gr.Group(elem_id="share-region-named"): + with gr.Row(): + with gr.Column(): + model_selector_left = gr.Dropdown( + choices=model_list, + value=model_list[0] if len(model_list) > 0 else "", + interactive=True, + show_label=False, + container=False, + ) + with gr.Column(): + model_selector_right = gr.Dropdown( + choices=model_list, + value=model_list[1] if len(model_list) > 1 else "", + interactive=True, + show_label=False, + container=False, + ) + with gr.Row(): + with gr.Accordion("πŸ” Expand to see all model descriptions", open=False): + model_description_md = get_model_description_md(model_list) + gr.Markdown(model_description_md, elem_id="model_description_markdown") + + with gr.Row(): + with gr.Column(): + chatbot_left = gr.Image(height=512, width=512, label = "Model A") + with gr.Column(): + chatbot_right = gr.Image(height=512, width=512, label = "Model B") + + with gr.Row(): + leftvote_btn = gr.Button( + value="πŸ‘ˆ A is better", visible=False, interactive=False + ) + rightvote_btn = gr.Button( + value="πŸ‘‰ B is better", visible=False, interactive=False + ) + tie_btn = gr.Button(value="🀝 Tie", visible=False, interactive=False) + bothbad_btn = gr.Button( + value="πŸ‘Ž Both are bad", visible=False, interactive=False + ) + + with gr.Row(): + + textbox_source = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your source prompt", + elem_id="input_box_s", + ) + textbox_target = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your target prompt", + elem_id="input_box_t", + ) + textbox_instruct = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your instruct prompt", + elem_id="input_box_t", + ) + + with gr.Row(): + source_image = gr.Image(type="pil") + send_btn = gr.Button(value="Send", variant="primary", scale=0) + + with gr.Row() as button_row: + clear_btn = gr.Button(value="πŸ—‘οΈ Clear history", interactive=False) + regenerate_btn = gr.Button(value="πŸ”„ Regenerate", interactive=False) + share_btn = gr.Button(value="πŸ“· Share") + + gr.Markdown(acknowledgment_md, elem_id="ack_markdown") + gr.Examples( + examples=[ + ["a bowl of strawberries", "a bowl of oranges", "change strawberries to oranges", + os.path.join("./examples", "strawberries.jpg")], + ["a pig is eating an ice cream", "a rabbit is eating an ice cream", "change pig to rabbit", + os.path.join("./examples", "pig.jpg")], + ["a rubber duck in a swimming pool", "a rubber duck with a hat in a swimming pool", "add a hat to the duck", + os.path.join("./examples", "duck.jpg")], + ["a photo of a cat", "a photo of a mouse", "change cat to mouse", + os.path.join("./examples", "cat.jpeg")]], + inputs=[textbox_source, textbox_target, textbox_instruct, source_image]) + + + model_selector_left.change( + clear_history_side_by_side_ie, + inputs=None, + outputs=[state0, state1, textbox_source, textbox_target, textbox_instruct, + source_image, chatbot_left, chatbot_right + ], + api_name="model_selector_left_side_by_side") + model_selector_right.change(clear_history_side_by_side_ie, inputs=None, outputs=[state0, state1, textbox_source, textbox_target, textbox_instruct, source_image, chatbot_left, chatbot_right], api_name="model_selector_right_side_by_side") + + + btn_list = [leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, regenerate_btn, clear_btn] + + send_btn.click( + gen_func, + inputs=[ + state0, + state1, + textbox_source, + textbox_target, + textbox_instruct, + source_image, + model_selector_left, + model_selector_right + ], + outputs=[state0, state1, chatbot_left, chatbot_right], + api_name="send_btn_side_by_side" + ).then( + enable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + regenerate_btn.click( + gen_func, + inputs=[ + state0, + state1, + textbox_source, + textbox_target, + textbox_instruct, + source_image, + model_selector_left, + model_selector_right + ], + outputs=[state0, state1, chatbot_left, chatbot_right], + api_name="regenerate_btn_side_by_side" + ).then( + enable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + + clear_btn.click( + clear_history_side_by_side_ie, + inputs=None, + outputs=[state0, state1, textbox_source, textbox_target, textbox_instruct, + source_image, chatbot_left, chatbot_right], + api_name="clear_btn_side_by_side" + ).then( + disable_buttons_side_by_side, + inputs=None, + outputs=btn_list + ) + vote_btns = [leftvote_btn, rightvote_btn, tie_btn, bothbad_btn] + input_list = [textbox_source, textbox_target, source_image, textbox_instruct] + leftvote_btn.click( + leftvote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[model_selector_left, model_selector_right] + input_list + vote_btns, + ) + rightvote_btn.click( + rightvote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[model_selector_left, model_selector_right] + input_list + vote_btns, + ) + tie_btn.click( + tievote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[model_selector_left, model_selector_right] + input_list + vote_btns, + ) + bothbad_btn.click( + bothbad_vote_last_response, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[model_selector_left, model_selector_right] + input_list + vote_btns, + ) + + share_btn.click( + share_click, + inputs=[state0, state1, model_selector_left, model_selector_right], + outputs=[], + js=share_js + ) + + +def build_single_model_ui_ie(models, add_promotion_links=False): + promotion = ( + """ +- | [GitHub](https://github.com/TIGER-AI-Lab/ImagenHub) | [Paper](https://arxiv.org/abs/2310.01596) | [Dataset](https://huggingface.co/ImagenHub) | +""" + if add_promotion_links + else "" + ) + + notice_markdown = f""" +# πŸ”οΈ Play with Image Edition Models +{promotion} + +## πŸ€– Choose any model to edit image +""" + gr.Markdown(notice_markdown, elem_id="notice_markdown") + + state = gr.State() + gen_func = partial(generate_ie, models.generate_image_ie) + model_list = models.model_ie_list + + with gr.Row(elem_id="model_selector_row"): + model_selector = gr.Dropdown( + choices=model_list, + value=model_list[0] if len(model_list) > 0 else "", + interactive=True, + show_label=False + ) + with gr.Row(): + with gr.Accordion( + "πŸ” Expand to see all model descriptions", + open=False, + elem_id="model_description_accordion", + ): + model_description_md = get_model_description_md(model_list) + gr.Markdown(model_description_md, elem_id="model_description_markdown") + + with gr.Row(): + + textbox_source = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your source prompt", + elem_id="input_box_s", + ) + textbox_target = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your target prompt", + elem_id="input_box_t", + ) + textbox_instruct = gr.Textbox( + show_label=False, + placeholder="πŸ‘‰ Enter your instruct prompt", + elem_id="input_box_t", + ) + + with gr.Row(): + source_image = gr.Image(type="pil") + send_btn = gr.Button(value="Send", variant="primary", scale=0) + + with gr.Row(): + chatbot = gr.Image() + + with gr.Row() as button_row: + upvote_btn = gr.Button(value="πŸ‘ Upvote", interactive=False) + downvote_btn = gr.Button(value="πŸ‘Ž Downvote", interactive=False) + flag_btn = gr.Button(value="⚠️ Flag", interactive=False) + regenerate_btn = gr.Button(value="πŸ”„ Regenerate", interactive=False) + clear_btn = gr.Button(value="πŸ—‘οΈ Clear history", interactive=False) + + if add_promotion_links: + gr.Markdown(acknowledgment_md, elem_id="ack_markdown") + + gr.Examples( + examples=[ + ["a bowl of strawberries", "a bowl of oranges", "change strawberries to oranges", + os.path.join("./examples", "strawberries.jpg")], + ["a pig is eating an ice cream", "a rabbit is eating an ice cream", "change pig to rabbit", + os.path.join("./examples", "pig.jpg")], + ["a rubber duck in a swimming pool", "a rubber duck with a hat in a swimming pool", "add a hat to the duck", + os.path.join("./examples", "duck.jpg")], + ["a photo of a cat", "a photo of a mouse", "change cat to mouse", + os.path.join("./examples", "cat.jpeg")]], + inputs=[textbox_source, textbox_target, textbox_instruct, source_image]) + + # Register listeners + btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn] + + model_selector.change(clear_history_ie, inputs=None, outputs=[state, textbox_source, textbox_target, textbox_instruct, source_image, chatbot], api_name="model_selector_single") + + btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn] + + send_btn.click( + gen_func, + inputs=[state, textbox_source, textbox_target, textbox_instruct, source_image, model_selector], + outputs=[state, chatbot], + api_name="send_btn_single", + show_progress = "full" + ).then( + enable_buttons, + inputs=None, + outputs=btn_list + ) + + vote_btns = [upvote_btn, downvote_btn, flag_btn] + model_inputs = [textbox_source, textbox_instruct, source_image, textbox_target] + upvote_btn.click( + upvote_last_response, + inputs=[state, model_selector], + outputs=model_inputs + vote_btns, + ) + downvote_btn.click( + downvote_last_response, + inputs=[state, model_selector], + outputs=model_inputs + vote_btns, + ) + flag_btn.click( + flag_last_response, + inputs=[state, model_selector], + outputs=model_inputs + vote_btns, + ) + regenerate_btn.click( + gen_func, + inputs=[state, textbox_source, textbox_target, textbox_instruct, source_image, model_selector], + outputs=[state, chatbot], + api_name="regenerate_btn_single", + show_progress = "full" + ).then( + enable_buttons, + inputs=None, + outputs=btn_list + ) + clear_btn.click( + clear_history_ie, + inputs=None, + outputs=[state, textbox_source, textbox_target, textbox_instruct, source_image, chatbot], + api_name="clear_history_single", + show_progress="full" + ).then( + disable_buttons, + inputs=None, + outputs=btn_list + ) diff --git a/serve/leaderboard.py b/serve/leaderboard.py new file mode 100644 index 0000000000000000000000000000000000000000..6a17bb2dd2b1ce661aa01e898da347db83b0c4f1 --- /dev/null +++ b/serve/leaderboard.py @@ -0,0 +1,309 @@ +""" +Live monitor of the website statistics and leaderboard. + +Dependency: +sudo apt install pkg-config libicu-dev +pip install pytz gradio gdown plotly polyglot pyicu pycld2 tabulate +""" + +import argparse +import ast +import pickle +import os +import threading +import time + +import gradio as gr +import numpy as np +import pandas as pd + + +basic_component_values = [None] * 6 +leader_component_values = [None] * 5 + + +# def make_leaderboard_md(elo_results): +# leaderboard_md = f""" +# # πŸ† Chatbot Arena Leaderboard +# | [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) | + +# This leaderboard is based on the following three benchmarks. +# - [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) - a crowdsourced, randomized battle platform. We use 100K+ user votes to compute Elo ratings. +# - [MT-Bench](https://arxiv.org/abs/2306.05685) - a set of challenging multi-turn questions. We use GPT-4 to grade the model responses. +# - [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a model's multitask accuracy on 57 tasks. + +# πŸ’» Code: The Arena Elo ratings are computed by this [notebook]({notebook_url}). The MT-bench scores (single-answer grading on a scale of 10) are computed by [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge). The MMLU scores are mostly computed by [InstructEval](https://github.com/declare-lab/instruct-eval). Higher values are better for all benchmarks. Empty cells mean not available. Last updated: November, 2023. +# """ +# return leaderboard_md + +def make_leaderboard_md(elo_results): + leaderboard_md = f""" +# πŸ† GenAI-Arena Leaderboard +| [GitHub](https://github.com/TIGER-AI-Lab/ImagenHub) | [Dataset](https://huggingface.co/ImagenHub) | [Twitter](https://twitter.com/TianleLI123/status/1757245259149422752) | + +""" + return leaderboard_md + + +def make_leaderboard_md_live(elo_results): + leaderboard_md = f""" +# Leaderboard +Last updated: {elo_results["last_updated_datetime"]} +{elo_results["leaderboard_table"]} +""" + return leaderboard_md + + +def model_hyperlink(model_name, link): + return f'{model_name}' + + +def load_leaderboard_table_csv(filename, add_hyperlink=True): + df = pd.read_csv(filename) + for col in df.columns: + if "Arena Elo rating" in col: + df[col] = df[col].apply(lambda x: int(x) if x != "-" else np.nan) + elif col == "MMLU": + df[col] = df[col].apply(lambda x: round(x * 100, 1) if x != "-" else np.nan) + elif col == "MT-bench (win rate %)": + df[col] = df[col].apply(lambda x: round(x, 1) if x != "-" else np.nan) + elif col == "MT-bench (score)": + df[col] = df[col].apply(lambda x: round(x, 2) if x != "-" else np.nan) + + if add_hyperlink and col == "Model": + df[col] = df.apply(lambda row: model_hyperlink(row[col], row["Link"]), axis=1) + return df + + + +def build_basic_stats_tab(): + empty = "Loading ..." + basic_component_values[:] = [empty, None, empty, empty, empty, empty] + + md0 = gr.Markdown(empty) + gr.Markdown("#### Figure 1: Number of model calls and votes") + plot_1 = gr.Plot(show_label=False) + with gr.Row(): + with gr.Column(): + md1 = gr.Markdown(empty) + with gr.Column(): + md2 = gr.Markdown(empty) + with gr.Row(): + with gr.Column(): + md3 = gr.Markdown(empty) + with gr.Column(): + md4 = gr.Markdown(empty) + return [md0, plot_1, md1, md2, md3, md4] + + +def get_full_table(anony_arena_df, full_arena_df, model_table_df): + values = [] + for i in range(len(model_table_df)): + row = [] + model_key = model_table_df.iloc[i]["key"] + model_name = model_table_df.iloc[i]["Model"] + # model display name + row.append(model_name) + if model_key in anony_arena_df.index: + idx = anony_arena_df.index.get_loc(model_key) + row.append(round(anony_arena_df.iloc[idx]["rating"])) + else: + row.append(np.nan) + if model_key in full_arena_df.index: + idx = full_arena_df.index.get_loc(model_key) + row.append(round(full_arena_df.iloc[idx]["rating"])) + else: + row.append(np.nan) + # row.append(model_table_df.iloc[i]["MT-bench (score)"]) + # row.append(model_table_df.iloc[i]["Num Battles"]) + # row.append(model_table_df.iloc[i]["MMLU"]) + # Organization + row.append(model_table_df.iloc[i]["Organization"]) + # license + row.append(model_table_df.iloc[i]["License"]) + + values.append(row) + values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9) + return values + + +def get_arena_table(arena_df, model_table_df): + # sort by rating + arena_df = arena_df.sort_values(by=["rating"], ascending=False) + values = [] + for i in range(len(arena_df)): + row = [] + model_key = arena_df.index[i] + model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[ + 0 + ] + + # rank + row.append(i + 1) + # model display name + row.append(model_name) + # elo rating + row.append(round(arena_df.iloc[i]["rating"])) + upper_diff = round(arena_df.iloc[i]["rating_q975"] - arena_df.iloc[i]["rating"]) + lower_diff = round(arena_df.iloc[i]["rating"] - arena_df.iloc[i]["rating_q025"]) + row.append(f"+{upper_diff}/-{lower_diff}") + # num battles + row.append(round(arena_df.iloc[i]["num_battles"])) + # Organization + row.append( + model_table_df[model_table_df["key"] == model_key]["Organization"].values[0] + ) + # license + row.append( + model_table_df[model_table_df["key"] == model_key]["License"].values[0] + ) + + values.append(row) + return values + +def make_arena_leaderboard_md(elo_results): + arena_df = elo_results["leaderboard_table_df"] + last_updated = elo_results["last_updated_datetime"] + total_votes = sum(arena_df["num_battles"]) // 2 + total_models = len(arena_df) + + leaderboard_md = f""" + + +Total #models: **{total_models}**(anonymous). Total #votes: **{total_votes}**. Last updated: {last_updated}. +(Note: Only anonymous votes are considered here. Check the full leaderboard for all votes.) + +Contribute the votes πŸ—³οΈ at [GenAI-Arena](https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena)! + +If you want to see more models, please help us [add them](https://github.com/TIGER-AI-Lab/ImagenHub?tab=readme-ov-file#-contributing-). +""" + return leaderboard_md + +def make_full_leaderboard_md(elo_results): + arena_df = elo_results["leaderboard_table_df"] + last_updated = elo_results["last_updated_datetime"] + total_votes = sum(arena_df["num_battles"]) // 2 + total_models = len(arena_df) + + leaderboard_md = f""" +Total #models: **{total_models}**(full:anonymous+open). Total #votes: **{total_votes}**. Last updated: {last_updated}. + +Contribute your vote πŸ—³οΈ at [vision-arena](https://huggingface.co/spaces/WildVision/vision-arena)! +""" + return leaderboard_md + +def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=False): + if elo_results_file is None: # Do live update + md = "Loading ..." + p1 = p2 = p3 = p4 = None + else: + with open(elo_results_file, "rb") as fin: + elo_results = pickle.load(fin) + + anony_elo_results = elo_results["anony"] + full_elo_results = elo_results["full"] + anony_arena_df = anony_elo_results["leaderboard_table_df"] + full_arena_df = full_elo_results["leaderboard_table_df"] + p1 = anony_elo_results["win_fraction_heatmap"] + p2 = anony_elo_results["battle_count_heatmap"] + p3 = anony_elo_results["bootstrap_elo_rating"] + p4 = anony_elo_results["average_win_rate_bar"] + + md = make_leaderboard_md(anony_elo_results) + + md_1 = gr.Markdown(md, elem_id="leaderboard_markdown") + + if leaderboard_table_file: + model_table_df = load_leaderboard_table_csv(leaderboard_table_file) + with gr.Tabs() as tabs: + # arena table + arena_table_vals = get_arena_table(anony_arena_df, model_table_df) + with gr.Tab("Arena Elo", id=0): + md = make_arena_leaderboard_md(anony_elo_results) + gr.Markdown(md, elem_id="leaderboard_markdown") + gr.Dataframe( + headers=[ + "Rank", + "πŸ€– Model", + "⭐ Arena Elo", + "πŸ“Š 95% CI", + "πŸ—³οΈ Votes", + "Organization", + "License", + ], + datatype=[ + "str", + "markdown", + "number", + "str", + "number", + "str", + "str", + ], + value=arena_table_vals, + elem_id="arena_leaderboard_dataframe", + height=700, + column_widths=[50, 200, 100, 100, 100, 150, 150], + wrap=True, + ) + with gr.Tab("Full Leaderboard", id=1): + md = make_full_leaderboard_md(full_elo_results) + gr.Markdown(md, elem_id="leaderboard_markdown") + full_table_vals = get_full_table(anony_arena_df, full_arena_df, model_table_df) + gr.Dataframe( + headers=[ + "πŸ€– Model", + "⭐ Arena Elo (anony)", + "⭐ Arena Elo (full)", + "Organization", + "License", + ], + datatype=["markdown", "number", "number", "str", "str"], + value=full_table_vals, + elem_id="full_leaderboard_dataframe", + column_widths=[200, 100, 100, 100, 150, 150], + height=700, + wrap=True, + ) + if not show_plot: + gr.Markdown( + """ ## We are still collecting more votes on more models. The ranking will be updated very fruquently. Please stay tuned! + """, + elem_id="leaderboard_markdown", + ) + else: + pass + + leader_component_values[:] = [md, p1, p2, p3, p4] + + """ + with gr.Row(): + with gr.Column(): + gr.Markdown( + "#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles" + ) + plot_1 = gr.Plot(p1, show_label=False) + with gr.Column(): + gr.Markdown( + "#### Figure 2: Battle Count for Each Combination of Models (without Ties)" + ) + plot_2 = gr.Plot(p2, show_label=False) + with gr.Row(): + with gr.Column(): + gr.Markdown( + "#### Figure 3: Bootstrap of Elo Estimates (1000 Rounds of Random Sampling)" + ) + plot_3 = gr.Plot(p3, show_label=False) + with gr.Column(): + gr.Markdown( + "#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)" + ) + plot_4 = gr.Plot(p4, show_label=False) + """ + + from .utils import acknowledgment_md + + gr.Markdown(acknowledgment_md) + + # return [md_1, plot_1, plot_2, plot_3, plot_4] + return [md_1] \ No newline at end of file diff --git a/serve/log_server.py b/serve/log_server.py new file mode 100644 index 0000000000000000000000000000000000000000..61bf46d24dd5d9193f264785b45173fbbf0cd4f5 --- /dev/null +++ b/serve/log_server.py @@ -0,0 +1,39 @@ +from fastapi import FastAPI, File, UploadFile, Form, APIRouter +from typing import Optional +import json +import os +import aiofiles +from .log_utils import build_logger +from .constants import LOG_SERVER_SUBDOAMIN, APPEND_JSON, SAVE_IMAGE + +logger = build_logger("log_server", "log_server.log") + +app = APIRouter(prefix=f"/{LOG_SERVER_SUBDOAMIN}") + +@app.post(f"/{APPEND_JSON}") +async def append_json(json_str: str = Form(...), file_name: str = Form(...)): + """ + Appends a JSON string to a specified file. + """ + # Convert the string back to a JSON object (dict) + data = json.loads(json_str) + # Append the data to the specified file + os.makedirs(os.path.dirname(file_name), exist_ok=True) + async with aiofiles.open(file_name, mode='a') as f: + await f.write(json.dumps(data) + "\n") + + logger.info(f"Appended 1 JSON object to {file_name}") + return {"message": "JSON data appended successfully"} + +@app.post(f"/{SAVE_IMAGE}") +async def save_image(image: UploadFile = File(...), image_path: str = Form(...)): + """ + Saves an uploaded image to the specified path. + """ + # Note: 'image_path' should include the file name and extension for the image to be saved. + os.makedirs(os.path.dirname(image_path), exist_ok=True) + async with aiofiles.open(image_path, mode='wb') as f: + content = await image.read() # Read the content of the uploaded image + await f.write(content) # Write the image content to a file + logger.info(f"Image saved successfully at {image_path}") + return {"message": f"Image saved successfully at {image_path}"} diff --git a/serve/log_utils.py b/serve/log_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..88545b53b28331b44e88f3acbf9f4ed98e32f340 --- /dev/null +++ b/serve/log_utils.py @@ -0,0 +1,111 @@ +""" +Common utilities. +""" +from asyncio import AbstractEventLoop +import json +import logging +import logging.handlers +import os +import platform +import sys +from typing import AsyncGenerator, Generator +import warnings + +import requests + +from .constants import LOGDIR + + +handler = None +visited_loggers = set() + + +def build_logger(logger_name, logger_filename): + global handler + + formatter = logging.Formatter( + fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + # Set the format of root handlers + if not logging.getLogger().handlers: + if sys.version_info[1] >= 9: + # This is for windows + logging.basicConfig(level=logging.INFO, encoding="utf-8") + else: + if platform.system() == "Windows": + warnings.warn( + "If you are running on Windows, " + "we recommend you use Python >= 3.9 for UTF-8 encoding." + ) + logging.basicConfig(level=logging.INFO) + logging.getLogger().handlers[0].setFormatter(formatter) + + # Redirect stdout and stderr to loggers + stdout_logger = logging.getLogger("stdout") + stdout_logger.setLevel(logging.INFO) + sl = StreamToLogger(stdout_logger, logging.INFO) + sys.stdout = sl + + stderr_logger = logging.getLogger("stderr") + stderr_logger.setLevel(logging.ERROR) + sl = StreamToLogger(stderr_logger, logging.ERROR) + sys.stderr = sl + + # Get logger + logger = logging.getLogger(logger_name) + logger.setLevel(logging.INFO) + + # if LOGDIR is empty, then don't try output log to local file + if LOGDIR != "": + os.makedirs(LOGDIR, exist_ok=True) + filename = os.path.join(LOGDIR, logger_filename) + handler = logging.handlers.TimedRotatingFileHandler( + filename, when="D", utc=True, encoding="utf-8" + ) + handler.setFormatter(formatter) + + for l in [stdout_logger, stderr_logger, logger]: + if l in visited_loggers: + continue + visited_loggers.add(l) + l.addHandler(handler) + + return logger + + +class StreamToLogger(object): + """ + Fake file-like stream object that redirects writes to a logger instance. + """ + + def __init__(self, logger, log_level=logging.INFO): + self.terminal = sys.stdout + self.logger = logger + self.log_level = log_level + self.linebuf = "" + + def __getattr__(self, attr): + return getattr(self.terminal, attr) + + def write(self, buf): + temp_linebuf = self.linebuf + buf + self.linebuf = "" + for line in temp_linebuf.splitlines(True): + # From the io.TextIOWrapper docs: + # On output, if newline is None, any '\n' characters written + # are translated to the system default line separator. + # By default sys.stdout.write() expects '\n' newlines and then + # translates them so this is still cross platform. + if line[-1] == "\n": + encoded_message = line.encode("utf-8", "ignore").decode("utf-8") + self.logger.log(self.log_level, encoded_message.rstrip()) + else: + self.linebuf += line + + def flush(self): + if self.linebuf != "": + encoded_message = self.linebuf.encode("utf-8", "ignore").decode("utf-8") + self.logger.log(self.log_level, encoded_message.rstrip()) + self.linebuf = "" \ No newline at end of file diff --git a/serve/utils.py b/serve/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..791176594c137905609ed4d2463fe257c08b5e2d --- /dev/null +++ b/serve/utils.py @@ -0,0 +1,162 @@ +import os +import json +import datetime +import requests +import numpy as np +import gradio as gr +from pathlib import Path +from model.model_registry import * +from .constants import LOGDIR, LOG_SERVER_ADDR, APPEND_JSON, SAVE_IMAGE +from typing import Union + + +enable_btn = gr.update(interactive=True, visible=True) +disable_btn = gr.update(interactive=False) +invisible_btn = gr.update(interactive=False, visible=False) +no_change_btn = gr.update(value="No Change", interactive=True, visible=True) + + +def build_about(): + about_markdown = f""" +# About Us +This is a project from TIGER Lab at University of Waterloo. + +## Contributors: +[Tianle Li](https://scholar.google.com/citations?user=g213g7YAAAAJ&hl=en), [Dongfu Jiang](https://jdf-prog.github.io/), [Yuansheng Ni](https://yuanshengni.github.io/). + +## Contact: +Email: t29li@uwaterloo.ca (Tianle Li) + +## Advisors +[Wenhu Chen](https://wenhuchen.github.io/) + +## Sponsorship +We are keep looking for sponsorship to support the arena project for the long term. Please contact us if you are interested in supporting this project. +""" + + gr.Markdown(about_markdown, elem_id="about_markdown") + + +acknowledgment_md = """ +### Acknowledgment +
+

Our code base is built upon FastChat and ImagenHub

. +
+""" + +block_css = """ +#notice_markdown { + font-size: 110% +} +#notice_markdown th { + display: none; +} +#notice_markdown td { + padding-top: 6px; + padding-bottom: 6px; +} +#model_description_markdown { + font-size: 110% +} +#leaderboard_markdown { + font-size: 110% +} +#leaderboard_markdown td { + padding-top: 6px; + padding-bottom: 6px; +} +#leaderboard_dataframe td { + line-height: 0.1em; +} +#about_markdown { + font-size: 110% +} +#ack_markdown { + font-size: 110% +} +#input_box textarea { +} +footer { + display:none !important +} +.image-about img { + margin: 0 30px; + margin-top: 30px; + height: 60px; + max-height: 100%; + width: auto; + float: left; +.input-image, .image-preview { + margin: 0 30px; + height: 30px; + max-height: 100%; + width: auto; + max-width: 30%;} +} +""" + +def enable_buttons_side_by_side(): + return tuple(gr.update(visible=True, interactive=True) for i in range(6)) + +def disable_buttons_side_by_side(): + return tuple(gr.update(visible=i>=4, interactive=False) for i in range(6)) + +def enable_buttons(): + return tuple(gr.update(interactive=True) for _ in range(5)) + +def disable_buttons(): + return tuple(gr.update(interactive=False) for _ in range(5)) + +def clear_history(): + return None, "", None + +def clear_history_side_by_side(): + return None, None, "", None, None + +def clear_history_side_by_side_anony(): + return None, None, "", None, None, gr.Markdown(""), gr.Markdown("") + +def clear_history_ie(): + return None, "", "", "", None, None + +def clear_history_side_by_side_ie(): + return None, None, "", "", "", None, None, None + +def clear_history_side_by_side_ie_anony(): + return None, None, "", "", "", None, None, None, gr.Markdown(""), gr.Markdown("") + +def get_ip(request: gr.Request): + if request: + if "cf-connecting-ip" in request.headers: + ip = request.headers["cf-connecting-ip"] + else: + ip = request.client.host + else: + ip = None + return ip + +def get_conv_log_filename(): + t = datetime.datetime.now() + name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json") + return name + +def save_image_file_on_log_server(image_file:str): + + image_file = Path(image_file).absolute().relative_to(os.getcwd()) + image_file = str(image_file) + # Open the image file in binary mode + url = f"{LOG_SERVER_ADDR}/{SAVE_IMAGE}" + with open(image_file, 'rb') as f: + # Make the POST request, sending the image file and the image path + response = requests.post(url, files={'image': f}, data={'image_path': image_file}) + return response + +def append_json_item_on_log_server(json_item: Union[dict, str], log_file: str): + if isinstance(json_item, dict): + json_item = json.dumps(json_item) + log_file = Path(log_file).absolute().relative_to(os.getcwd()) + log_file = str(log_file) + url = f"{LOG_SERVER_ADDR}/{APPEND_JSON}" + # Make the POST request, sending the JSON string and the log file name + response = requests.post(url, data={'json_str': json_item, 'file_name': log_file}) + return response \ No newline at end of file diff --git a/serve/vote_utils.py b/serve/vote_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3b03a8d1c677b67ba023275c5a4c4fe719b398 --- /dev/null +++ b/serve/vote_utils.py @@ -0,0 +1,657 @@ +import datetime +import time +import json +import uuid +import gradio as gr +from pathlib import Path +from .utils import * +from .log_utils import build_logger +from .constants import IMAGE_DIR + +ig_logger = build_logger("gradio_web_server_image_generation", "gr_web_image_generation.log") # ig = image generation, loggers for single model direct chat +igm_logger = build_logger("gradio_web_server_image_generation_multi", "gr_web_image_generation_multi.log") # igm = image generation multi, loggers for side-by-side and battle +ie_logger = build_logger("gradio_web_server_image_editing", "gr_web_image_editing.log") # ie = image editing, loggers for single model direct chat +iem_logger = build_logger("gradio_web_server_image_editing_multi", "gr_web_image_editing_multi.log") # iem = image editing multi, loggers for side-by-side and battle + +def vote_last_response_ig(state, vote_type, model_selector, request: gr.Request): + with open(get_conv_log_filename(), "a") as fout: + data = { + "tstamp": round(time.time(), 4), + "type": vote_type, + "model": model_selector, + "state": state.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + +def vote_last_response_igm(states, vote_type, model_selectors, request: gr.Request): + with open(get_conv_log_filename(), "a") as fout: + data = { + "tstamp": round(time.time(), 4), + "type": vote_type, + "models": [x for x in model_selectors], + "states": [x.dict() for x in states], + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + for state in states: + output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg' + with open(output_file, 'w') as f: + state.output.save(f, 'JPEG') + save_image_file_on_log_server(output_file) + +def vote_last_response_ie(state, vote_type, model_selector, request: gr.Request): + with open(get_conv_log_filename(), "a") as fout: + data = { + "tstamp": round(time.time(), 4), + "type": vote_type, + "model": model_selector, + "state": state.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + output_file = f'{IMAGE_DIR}/edition/{state.conv_id}.jpg' + source_file = f'{IMAGE_DIR}/edition/{state.conv_id}_source.jpg' + with open(output_file, 'w') as f: + state.output.save(f, 'JPEG') + with open(source_file, 'w') as sf: + state.source_image.save(sf, 'JPEG') + save_image_file_on_log_server(output_file) + save_image_file_on_log_server(source_file) + +def vote_last_response_iem(states, vote_type, model_selectors, request: gr.Request): + with open(get_conv_log_filename(), "a") as fout: + data = { + "tstamp": round(time.time(), 4), + "type": vote_type, + "models": [x for x in model_selectors], + "states": [x.dict() for x in states], + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + for state in states: + output_file = f'{IMAGE_DIR}/edition/{state.conv_id}.jpg' + source_file = f'{IMAGE_DIR}/edition/{state.conv_id}_source.jpg' + with open(output_file, 'w') as f: + state.output.save(f, 'JPEG') + with open(source_file, 'w') as sf: + state.source_image.save(sf, 'JPEG') + save_image_file_on_log_server(output_file) + save_image_file_on_log_server(source_file) + + +## Image Generation (IG) Single Model Direct Chat +def upvote_last_response_ig(state, model_selector, request: gr.Request): + ip = get_ip(request) + ig_logger.info(f"upvote. ip: {ip}") + vote_last_response_ig(state, "upvote", model_selector, request) + return ("",) + (disable_btn,) * 3 + +def downvote_last_response_ig(state, model_selector, request: gr.Request): + ip = get_ip(request) + ig_logger.info(f"downvote. ip: {ip}") + vote_last_response_ig(state, "downvote", model_selector, request) + return ("",) + (disable_btn,) * 3 + + +def flag_last_response_ig(state, model_selector, request: gr.Request): + ip = get_ip(request) + ig_logger.info(f"flag. ip: {ip}") + vote_last_response_ig(state, "flag", model_selector, request) + return ("",) + (disable_btn,) * 3 + +## Image Generation Multi (IGM) Side-by-Side and Battle + +def leftvote_last_response_igm( + state0, state1, model_selector0, model_selector1, request: gr.Request +): + igm_logger.info(f"leftvote (named). ip: {get_ip(request)}") + vote_last_response_igm( + [state0, state1], "leftvote", [model_selector0, model_selector1], request + ) + return ("",) + (disable_btn,) * 4 + + +def rightvote_last_response_igm( + state0, state1, model_selector0, model_selector1, request: gr.Request +): + igm_logger.info(f"rightvote (named). ip: {get_ip(request)}") + vote_last_response_igm( + [state0, state1], "rightvote", [model_selector0, model_selector1], request + ) + return ("",) + (disable_btn,) * 4 + + +def tievote_last_response_igm( + state0, state1, model_selector0, model_selector1, request: gr.Request +): + igm_logger.info(f"tievote (named). ip: {get_ip(request)}") + vote_last_response_igm( + [state0, state1], "tievote", [model_selector0, model_selector1], request + ) + return ("",) + (disable_btn,) * 4 + + +def bothbad_vote_last_response_igm( + state0, state1, model_selector0, model_selector1, request: gr.Request +): + igm_logger.info(f"bothbad_vote (named). ip: {get_ip(request)}") + vote_last_response_igm( + [state0, state1], "bothbad_vote", [model_selector0, model_selector1], request + ) + return ("",) + (disable_btn,) * 4 + +## Image Editing (IE) Single Model Direct Chat + +def upvote_last_response_ie(state, model_selector, request: gr.Request): + ip = get_ip(request) + ie_logger.info(f"upvote. ip: {ip}") + vote_last_response_ie(state, "upvote", model_selector, request) + return ("", "", gr.Image(height=512, width=512, type="pil"), "",) + (disable_btn,) * 3 + +def downvote_last_response_ie(state, model_selector, request: gr.Request): + ip = get_ip(request) + ie_logger.info(f"downvote. ip: {ip}") + vote_last_response_ie(state, "downvote", model_selector, request) + return ("", "", gr.Image(height=512, width=512, type="pil"), "",) + (disable_btn,) * 3 + +def flag_last_response_ie(state, model_selector, request: gr.Request): + ip = get_ip(request) + ie_logger.info(f"flag. ip: {ip}") + vote_last_response_ie(state, "flag", model_selector, request) + return ("", "", gr.Image(height=512, width=512, type="pil"), "",) + (disable_btn,) * 3 + +## Image Editing Multi (IEM) Side-by-Side and Battle +def leftvote_last_response_iem( + state0, state1, model_selector0, model_selector1, request: gr.Request +): + iem_logger.info(f"leftvote (anony). ip: {get_ip(request)}") + vote_last_response_iem( + [state0, state1], "leftvote", [model_selector0, model_selector1], request + ) + # names = ( + # "### Model A: " + state0.model_name, + # "### Model B: " + state1.model_name, + # ) + names = (state0.model_name, state1.model_name) + return names + ("", "", gr.Image(height=512, width=512, type="pil"), "") + (disable_btn,) * 4 + +def rightvote_last_response_iem( + state0, state1, model_selector0, model_selector1, request: gr.Request +): + iem_logger.info(f"rightvote (anony). ip: {get_ip(request)}") + vote_last_response_iem( + [state0, state1], "rightvote", [model_selector0, model_selector1], request + ) + # names = ( + # "### Model A: " + state0.model_name, + # "### Model B: " + state1.model_name, + # ) + names = (state0.model_name, state1.model_name) + return names + ("", "", gr.Image(height=512, width=512, type="pil"), "") + (disable_btn,) * 4 + +def tievote_last_response_iem( + state0, state1, model_selector0, model_selector1, request: gr.Request +): + iem_logger.info(f"tievote (anony). ip: {get_ip(request)}") + vote_last_response_iem( + [state0, state1], "tievote", [model_selector0, model_selector1], request + ) + names = ( + "### Model A: " + state0.model_name, + "### Model B: " + state1.model_name, + ) + return names + ("", "", gr.Image(height=512, width=512, type="pil"), "") + (disable_btn,) * 4 + +def bothbad_vote_last_response_iem( + state0, state1, model_selector0, model_selector1, request: gr.Request +): + iem_logger.info(f"bothbad_vote (anony). ip: {get_ip(request)}") + vote_last_response_iem( + [state0, state1], "bothbad_vote", [model_selector0, model_selector1], request + ) + names = ( + "### Model A: " + state0.model_name, + "### Model B: " + state1.model_name, + ) + return names + ("", "", gr.Image(height=512, width=512, type="pil"), "") + (disable_btn,) * 4 + + +share_js = """ +function (a, b, c, d) { + const captureElement = document.querySelector('#share-region-named'); + html2canvas(captureElement) + .then(canvas => { + canvas.style.display = 'none' + document.body.appendChild(canvas) + return canvas + }) + .then(canvas => { + const image = canvas.toDataURL('image/png') + const a = document.createElement('a') + a.setAttribute('download', 'chatbot-arena.png') + a.setAttribute('href', image) + a.click() + canvas.remove() + }); + return [a, b, c, d]; +} +""" +def share_click_igm(state0, state1, model_selector0, model_selector1, request: gr.Request): + igm_logger.info(f"share (anony). ip: {get_ip(request)}") + if state0 is not None and state1 is not None: + vote_last_response_igm( + [state0, state1], "share", [model_selector0, model_selector1], request + ) + +def share_click_iem(state0, state1, model_selector0, model_selector1, request: gr.Request): + iem_logger.info(f"share (anony). ip: {get_ip(request)}") + if state0 is not None and state1 is not None: + vote_last_response_iem( + [state0, state1], "share", [model_selector0, model_selector1], request + ) + +## All Generation Gradio Interface + +class ImageStateIG: + def __init__(self, model_name): + self.conv_id = uuid.uuid4().hex + self.model_name = model_name + self.prompt = None + self.output = None + + def dict(self): + base = { + "conv_id": self.conv_id, + "model_name": self.model_name, + "prompt": self.prompt + } + return base + +class ImageStateIE: + def __init__(self, model_name): + self.conv_id = uuid.uuid4().hex + self.model_name = model_name + self.source_prompt = None + self.target_prompt = None + self.instruct_prompt = None + self.source_image = None + self.output = None + + def dict(self): + base = { + "conv_id": self.conv_id, + "model_name": self.model_name, + "source_prompt": self.source_prompt, + "target_prompt": self.target_prompt, + "instruct_prompt": self.instruct_prompt + } + return base + + + + +def generate_ig(gen_func, state, text, model_name, request: gr.Request): + if not text: + raise gr.Error("Prompt cannot be empty.") + if not model_name: + raise gr.Error("Model name cannot be empty.") + if state is None: + state = ImageStateIG(model_name) + ip = get_ip(request) + ig_logger.info(f"generate. ip: {ip}") + start_tstamp = time.time() + generated_image = gen_func(text, model_name) + state.prompt = text + state.output = generated_image + state.model_name = model_name + + yield state, generated_image + + finish_tstamp = time.time() + # logger.info(f"===output===: {output}") + + with open(get_conv_log_filename(), "a") as fout: + data = { + "tstamp": round(finish_tstamp, 4), + "type": "chat", + "model": model_name, + "gen_params": {}, + "start": round(start_tstamp, 4), + "finish": round(finish_tstamp, 4), + "state": state.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + + output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg' + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with open(output_file, 'w') as f: + state.output.save(f, 'JPEG') + save_image_file_on_log_server(output_file) + +def generate_igm(gen_func, state0, state1, text, model_name0, model_name1, request: gr.Request): + if not text: + raise gr.Error("Prompt cannot be empty.") + if not model_name0: + raise gr.Error("Model name A cannot be empty.") + if not model_name1: + raise gr.Error("Model name B cannot be empty.") + if state0 is None: + state0 = ImageStateIG(model_name0) + if state1 is None: + state1 = ImageStateIG(model_name1) + ip = get_ip(request) + igm_logger.info(f"generate. ip: {ip}") + start_tstamp = time.time() + generated_image0, generated_image1 = gen_func(text, model_name0, model_name1) + state0.prompt = text + state1.prompt = text + state0.output = generated_image0 + state1.output = generated_image1 + state0.model_name = model_name0 + state1.model_name = model_name1 + + yield state0, state1, generated_image0, generated_image1 + + finish_tstamp = time.time() + # logger.info(f"===output===: {output}") + + with open(get_conv_log_filename(), "a") as fout: + data = { + "tstamp": round(finish_tstamp, 4), + "type": "chat", + "model": model_name0, + "gen_params": {}, + "start": round(start_tstamp, 4), + "finish": round(finish_tstamp, 4), + "state": state0.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + data = { + "tstamp": round(finish_tstamp, 4), + "type": "chat", + "model": model_name1, + "gen_params": {}, + "start": round(start_tstamp, 4), + "finish": round(finish_tstamp, 4), + "state": state1.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + + for i, state in enumerate([state0, state1]): + output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg' + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with open(output_file, 'w') as f: + state.output.save(f, 'JPEG') + save_image_file_on_log_server(output_file) + +def generate_igm_annoy(gen_func, state0, state1, text, model_name0, model_name1, request: gr.Request): + if not text: + raise gr.Error("Prompt cannot be empty.") + if state0 is None: + state0 = ImageStateIG(model_name0) + if state1 is None: + state1 = ImageStateIG(model_name1) + ip = get_ip(request) + igm_logger.info(f"generate. ip: {ip}") + start_tstamp = time.time() + generated_image0, generated_image1, model_name0, model_name1 = gen_func(text, model_name0, model_name1) + state0.prompt = text + state1.prompt = text + state0.output = generated_image0 + state1.output = generated_image1 + state0.model_name = model_name0 + state1.model_name = model_name1 + + yield state0, state1, generated_image0, generated_image1, \ + gr.Markdown(f"### Model A: {model_name0}"), gr.Markdown(f"### Model B: {model_name1}") + + finish_tstamp = time.time() + # logger.info(f"===output===: {output}") + + with open(get_conv_log_filename(), "a") as fout: + data = { + "tstamp": round(finish_tstamp, 4), + "type": "chat", + "model": model_name0, + "gen_params": {}, + "start": round(start_tstamp, 4), + "finish": round(finish_tstamp, 4), + "state": state0.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + data = { + "tstamp": round(finish_tstamp, 4), + "type": "chat", + "model": model_name1, + "gen_params": {}, + "start": round(start_tstamp, 4), + "finish": round(finish_tstamp, 4), + "state": state1.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + + for i, state in enumerate([state0, state1]): + output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg' + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with open(output_file, 'w') as f: + state.output.save(f, 'JPEG') + save_image_file_on_log_server(output_file) + + +def generate_ie(gen_func, state, source_text, target_text, instruct_text, source_image, model_name, request: gr.Request): + if not source_text: + raise gr.Error("Source prompt cannot be empty.") + if not target_text: + raise gr.Error("Target prompt cannot be empty.") + if not instruct_text: + raise gr.Error("Instruction prompt cannot be empty.") + if not source_image: + raise gr.Error("Source image cannot be empty.") + if not model_name: + raise gr.Error("Model name cannot be empty.") + if state is None: + state = ImageStateIE(model_name) + ip = get_ip(request) + ig_logger.info(f"generate. ip: {ip}") + start_tstamp = time.time() + generated_image = gen_func(source_text, target_text, instruct_text, source_image, model_name) + state.source_prompt = source_text + state.target_prompt = target_text + state.instruct_prompt = instruct_text + state.source_image = source_image + state.output = generated_image + state.model_name = model_name + + yield state, generated_image + + finish_tstamp = time.time() + # logger.info(f"===output===: {output}") + + with open(get_conv_log_filename(), "a") as fout: + data = { + "tstamp": round(finish_tstamp, 4), + "type": "chat", + "model": model_name, + "gen_params": {}, + "start": round(start_tstamp, 4), + "finish": round(finish_tstamp, 4), + "state": state.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + + src_img_file = f'{IMAGE_DIR}/edition/{state.conv_id}_src.jpg' + os.makedirs(os.path.dirname(src_img_file), exist_ok=True) + with open(src_img_file, 'w') as f: + state.source_image.save(f, 'JPEG') + output_file = f'{IMAGE_DIR}/edition/{state.conv_id}_out.jpg' + with open(output_file, 'w') as f: + state.output.save(f, 'JPEG') + save_image_file_on_log_server(src_img_file) + save_image_file_on_log_server(output_file) + +def generate_iem(gen_func, state0, state1, source_text, target_text, instruct_text, source_image, model_name0, model_name1, request: gr.Request): + if not source_text: + raise gr.Error("Source prompt cannot be empty.") + if not target_text: + raise gr.Error("Target prompt cannot be empty.") + if not instruct_text: + raise gr.Error("Instruction prompt cannot be empty.") + if not source_image: + raise gr.Error("Source image cannot be empty.") + if not model_name0: + raise gr.Error("Model name A cannot be empty.") + if not model_name1: + raise gr.Error("Model name B cannot be empty.") + if state0 is None: + state0 = ImageStateIE(model_name0) + if state1 is None: + state1 = ImageStateIE(model_name1) + ip = get_ip(request) + igm_logger.info(f"generate. ip: {ip}") + start_tstamp = time.time() + generated_image0, generated_image1 = gen_func(source_text, target_text, instruct_text, source_image, model_name0, model_name1) + state0.source_prompt = source_text + state0.target_prompt = target_text + state0.instruct_prompt = instruct_text + state0.source_image = source_image + state0.output = generated_image0 + state0.model_name = model_name0 + state1.source_prompt = source_text + state1.target_prompt = target_text + state1.instruct_prompt = instruct_text + state1.source_image = source_image + state1.output = generated_image1 + state1.model_name = model_name1 + + yield state0, state1, generated_image0, generated_image1 + + finish_tstamp = time.time() + # logger.info(f"===output===: {output}") + + with open(get_conv_log_filename(), "a") as fout: + data = { + "tstamp": round(finish_tstamp, 4), + "type": "chat", + "model": model_name0, + "gen_params": {}, + "start": round(start_tstamp, 4), + "finish": round(finish_tstamp, 4), + "state": state0.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + data = { + "tstamp": round(finish_tstamp, 4), + "type": "chat", + "model": model_name1, + "gen_params": {}, + "start": round(start_tstamp, 4), + "finish": round(finish_tstamp, 4), + "state": state1.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + + for i, state in enumerate([state0, state1]): + src_img_file = f'{IMAGE_DIR}/edition/{state.conv_id}_src.jpg' + os.makedirs(os.path.dirname(src_img_file), exist_ok=True) + with open(src_img_file, 'w') as f: + state.source_image.save(f, 'JPEG') + output_file = f'{IMAGE_DIR}/edition/{state.conv_id}_out.jpg' + with open(output_file, 'w') as f: + state.output.save(f, 'JPEG') + save_image_file_on_log_server(src_img_file) + save_image_file_on_log_server(output_file) + + +def generate_iem_annoy(gen_func, state0, state1, source_text, target_text, instruct_text, source_image, model_name0, model_name1, request: gr.Request): + if not source_text: + raise gr.Error("Source prompt cannot be empty.") + if not target_text: + raise gr.Error("Target prompt cannot be empty.") + if not instruct_text: + raise gr.Error("Instruction prompt cannot be empty.") + if not source_image: + raise gr.Error("Source image cannot be empty.") + if state0 is None: + state0 = ImageStateIE(model_name0) + if state1 is None: + state1 = ImageStateIE(model_name1) + ip = get_ip(request) + igm_logger.info(f"generate. ip: {ip}") + start_tstamp = time.time() + generated_image0, generated_image1, model_name0, model_name1 = gen_func(source_text, target_text, instruct_text, source_image, model_name0, model_name1) + state0.source_prompt = source_text + state0.target_prompt = target_text + state0.instruct_prompt = instruct_text + state0.source_image = source_image + state0.output = generated_image0 + state0.model_name = model_name0 + state1.source_prompt = source_text + state1.target_prompt = target_text + state1.instruct_prompt = instruct_text + state1.source_image = source_image + state1.output = generated_image1 + state1.model_name = model_name1 + + yield state0, state1, generated_image0, generated_image1, \ + gr.Markdown(f"### Model A: {model_name0}"), gr.Markdown(f"### Model B: {model_name1}") + + finish_tstamp = time.time() + # logger.info(f"===output===: {output}") + + with open(get_conv_log_filename(), "a") as fout: + data = { + "tstamp": round(finish_tstamp, 4), + "type": "chat", + "model": model_name0, + "gen_params": {}, + "start": round(start_tstamp, 4), + "finish": round(finish_tstamp, 4), + "state": state0.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + data = { + "tstamp": round(finish_tstamp, 4), + "type": "chat", + "model": model_name1, + "gen_params": {}, + "start": round(start_tstamp, 4), + "finish": round(finish_tstamp, 4), + "state": state1.dict(), + "ip": get_ip(request), + } + fout.write(json.dumps(data) + "\n") + append_json_item_on_log_server(data, get_conv_log_filename()) + + for i, state in enumerate([state0, state1]): + src_img_file = f'{IMAGE_DIR}/edition/{state.conv_id}_src.jpg' + os.makedirs(os.path.dirname(src_img_file), exist_ok=True) + with open(src_img_file, 'w') as f: + state.source_image.save(f, 'JPEG') + output_file = f'{IMAGE_DIR}/edition/{state.conv_id}_out.jpg' + with open(output_file, 'w') as f: + state.output.save(f, 'JPEG') + save_image_file_on_log_server(src_img_file) + save_image_file_on_log_server(output_file) \ No newline at end of file diff --git a/style.css b/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -}