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import os | |
import uuid | |
from pathlib import Path | |
import pandas as pd | |
import streamlit as st | |
from datasets import get_dataset_config_names | |
from dotenv import load_dotenv | |
from huggingface_hub import list_datasets | |
from utils import (get_compatible_models, get_key, get_metadata, http_get, | |
http_post) | |
if Path(".env").is_file(): | |
load_dotenv(".env") | |
HF_TOKEN = os.getenv("HF_TOKEN") | |
AUTOTRAIN_USERNAME = os.getenv("AUTOTRAIN_USERNAME") | |
AUTOTRAIN_BACKEND_API = os.getenv("AUTOTRAIN_BACKEND_API") | |
DATASETS_PREVIEW_API = os.getenv("DATASETS_PREVIEW_API") | |
TASK_TO_ID = { | |
"binary_classification": 1, | |
"multi_class_classification": 2, | |
# "multi_label_classification": 3, # Not fully supported in AutoTrain | |
"entity_extraction": 4, | |
"extractive_question_answering": 5, | |
"translation": 6, | |
"summarization": 8, | |
} | |
supported_tasks = list(TASK_TO_ID.keys()) | |
########### | |
### APP ### | |
########### | |
st.title("Evaluation as a Service") | |
st.markdown( | |
""" | |
Welcome to Hugging Face's Evaluation as a Service! This application allows | |
you to evaluate any π€ Transformers model with a dataset on the Hub. Please | |
select the dataset and configuration below. The results of your evaluation | |
will be displayed on the public leaderboard | |
[here](https://huggingface.co/spaces/autoevaluate/leaderboards). | |
""" | |
) | |
all_datasets = [d.id for d in list_datasets()] | |
query_params = st.experimental_get_query_params() | |
default_dataset = all_datasets[0] | |
if "dataset" in query_params: | |
if len(query_params["dataset"]) > 0 and query_params["dataset"][0] in all_datasets: | |
default_dataset = query_params["dataset"][0] | |
selected_dataset = st.selectbox("Select a dataset", all_datasets, index=all_datasets.index(default_dataset)) | |
st.experimental_set_query_params(**{"dataset": [selected_dataset]}) | |
# TODO: In general this will be a list of multiple configs => need to generalise logic here | |
metadata = get_metadata(selected_dataset) | |
if metadata is None: | |
st.warning("No evaluation metadata found. Please configure the evaluation job below.") | |
with st.expander("Advanced configuration"): | |
## Select task | |
selected_task = st.selectbox( | |
"Select a task", | |
supported_tasks, | |
index=supported_tasks.index(metadata[0]["task_id"]) if metadata is not None else 0, | |
) | |
### Select config | |
configs = get_dataset_config_names(selected_dataset) | |
selected_config = st.selectbox("Select a config", configs) | |
## Select splits | |
splits_resp = http_get(path="/splits", domain=DATASETS_PREVIEW_API, params={"dataset": selected_dataset}) | |
if splits_resp.status_code == 200: | |
split_names = [] | |
all_splits = splits_resp.json() | |
for split in all_splits["splits"]: | |
if split["config"] == selected_config: | |
split_names.append(split["split"]) | |
selected_split = st.selectbox( | |
"Select a split", | |
split_names, | |
index=split_names.index(metadata[0]["splits"]["eval_split"]) if metadata is not None else 0, | |
) | |
## Select columns | |
rows_resp = http_get( | |
path="/rows", | |
domain=DATASETS_PREVIEW_API, | |
params={"dataset": selected_dataset, "config": selected_config, "split": selected_split}, | |
).json() | |
col_names = list(pd.json_normalize(rows_resp["rows"][0]["row"]).columns) | |
st.markdown("**Map your data columns**") | |
col1, col2 = st.columns(2) | |
# TODO: find a better way to layout these items | |
# TODO: need graceful way of handling dataset <--> task mismatch for datasets with metadata | |
col_mapping = {} | |
if selected_task in ["binary_classification", "multi_class_classification"]: | |
with col1: | |
st.markdown("`text` column") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.markdown("`target` column") | |
with col2: | |
text_col = st.selectbox( | |
"This column should contain the text you want to classify", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "text")) if metadata is not None else 0, | |
) | |
target_col = st.selectbox( | |
"This column should contain the labels you want to assign to the text", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "target")) if metadata is not None else 0, | |
) | |
col_mapping[text_col] = "text" | |
col_mapping[target_col] = "target" | |
elif selected_task == "entity_extraction": | |
with col1: | |
st.markdown("`tokens` column") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.markdown("`tags` column") | |
with col2: | |
tokens_col = st.selectbox( | |
"This column should contain the parts of the text (as an array of tokens) you want to assign labels to", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "tokens")) if metadata is not None else 0, | |
) | |
tags_col = st.selectbox( | |
"This column should contain the labels to associate to each part of the text", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "tags")) if metadata is not None else 0, | |
) | |
col_mapping[tokens_col] = "tokens" | |
col_mapping[tags_col] = "tags" | |
elif selected_task == "translation": | |
with col1: | |
st.markdown("`source` column") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.markdown("`target` column") | |
with col2: | |
text_col = st.selectbox( | |
"This column should contain the text you want to translate", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "source")) if metadata is not None else 0, | |
) | |
target_col = st.selectbox( | |
"This column should contain an example translation of the source text", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "target")) if metadata is not None else 0, | |
) | |
col_mapping[text_col] = "source" | |
col_mapping[target_col] = "target" | |
elif selected_task == "summarization": | |
with col1: | |
st.markdown("`text` column") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.markdown("`target` column") | |
with col2: | |
text_col = st.selectbox( | |
"This column should contain the text you want to summarize", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "text")) if metadata is not None else 0, | |
) | |
target_col = st.selectbox( | |
"This column should contain an example summarization of the text", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "target")) if metadata is not None else 0, | |
) | |
col_mapping[text_col] = "text" | |
col_mapping[target_col] = "target" | |
elif selected_task == "extractive_question_answering": | |
with col1: | |
st.markdown("`context` column") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.markdown("`question` column") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.markdown("`answers.text` column") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.text("") | |
st.markdown("`answers.answer_start` column") | |
with col2: | |
context_col = st.selectbox( | |
"This column should contain the question's context", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "context")) if metadata is not None else 0, | |
) | |
question_col = st.selectbox( | |
"This column should contain the question to be answered, given the context", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "question")) if metadata is not None else 0, | |
) | |
answers_text_col = st.selectbox( | |
"This column should contain example answers to the question, extracted from the context", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "answers.text")) | |
if metadata is not None | |
else 0, | |
) | |
answers_start_col = st.selectbox( | |
"This column should contain the indices in the context of the first character of each answers.text", | |
col_names, | |
index=col_names.index(get_key(metadata[0]["col_mapping"], "answers.answer_start")) | |
if metadata is not None | |
else 0, | |
) | |
col_mapping[context_col] = "context" | |
col_mapping[question_col] = "question" | |
col_mapping[answers_text_col] = "answers.text" | |
col_mapping[answers_start_col] = "answers.answer_start" | |
with st.form(key="form"): | |
compatible_models = get_compatible_models(selected_task, selected_dataset) | |
selected_models = st.multiselect("Select the models you wish to evaluate", compatible_models) | |
print("Selected models:", selected_models) | |
submit_button = st.form_submit_button("Make submission") | |
if submit_button: | |
project_id = str(uuid.uuid4())[:3] | |
payload = { | |
"username": AUTOTRAIN_USERNAME, | |
"proj_name": f"my-eval-project-{project_id}", | |
"task": TASK_TO_ID[selected_task], | |
"config": { | |
"language": "en", | |
"max_models": 5, | |
"instance": { | |
"provider": "aws", | |
"instance_type": "ml.g4dn.4xlarge", | |
"max_runtime_seconds": 172800, | |
"num_instances": 1, | |
"disk_size_gb": 150, | |
}, | |
"evaluation": { | |
"metrics": [], | |
"models": selected_models, | |
}, | |
}, | |
} | |
print(f"Payload: {payload}") | |
project_json_resp = http_post( | |
path="/projects/create", payload=payload, token=HF_TOKEN, domain=AUTOTRAIN_BACKEND_API | |
).json() | |
print(project_json_resp) | |
if project_json_resp["created"]: | |
payload = { | |
"split": 4, # use "auto" split choice in AutoTrain | |
"col_mapping": col_mapping, | |
"load_config": {"max_size_bytes": 0, "shuffle": False}, | |
} | |
data_json_resp = http_post( | |
path=f"/projects/{project_json_resp['id']}/data/{selected_dataset}", | |
payload=payload, | |
token=HF_TOKEN, | |
domain=AUTOTRAIN_BACKEND_API, | |
params={"type": "dataset", "config_name": selected_config, "split_name": selected_split}, | |
).json() | |
print(data_json_resp) | |
if data_json_resp["download_status"] == 1: | |
train_json_resp = http_get( | |
path=f"/projects/{project_json_resp['id']}/data/start_process", | |
token=HF_TOKEN, | |
domain=AUTOTRAIN_BACKEND_API, | |
).json() | |
print(train_json_resp) | |
if train_json_resp["success"]: | |
st.success(f"β Successfully submitted evaluation job with project ID {project_id}") | |
st.markdown( | |
f""" | |
Evaluation takes appoximately 1 hour to complete, so grab a β or π΅ while you wait: | |
* π Click [here](https://huggingface.co/spaces/autoevaluate/leaderboards) to view the results from your submission | |
""" | |
) | |
else: | |
st.error("π Oh noes, there was an error submitting your submission!") | |