import os import uuid from pathlib import Path 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_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, # "single_column_regression": 10, } AUTOTRAIN_TASK_TO_HUB_TASK = { "binary_classification": "text-classification", "multi_class_classification": "text-classification", # "multi_label_classification": "text-classification", # Not fully supported in AutoTrain "entity_extraction": "token-classification", "extractive_question_answering": "question-answering", "translation": "translation", "summarization": "summarization", # "single_column_regression": 10, } HUB_TASK_TO_AUTOTRAIN_TASK = {v: k for k, v in AUTOTRAIN_TASK_TO_HUB_TASK.items()} ########### ### 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 🤗 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", list(AUTOTRAIN_TASK_TO_HUB_TASK.values())) ### 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() print(all_splits) for split in all_splits["splits"]: print(selected_config) if split["config"] == selected_config: split_names.append(split["split"]) selected_split = st.selectbox("Select a split", split_names) # , index=split_names.index(eval_split)) ## Show columns rows_resp = http_get( path="/rows", domain="https://datasets-preview.huggingface.tech", params={"dataset": selected_dataset, "config": selected_config, "split": selected_split}, ).json() columns = rows_resp["columns"] col_names = [] for c in columns: col_names.append(c["column"]["name"]) # splits = metadata[0]["splits"] # split_names = list(splits.values()) # eval_split = splits.get("eval_split", split_names[0]) # selected_split = st.selectbox("Select a split", split_names, index=split_names.index(eval_split)) # TODO: add a function to handle the mapping task <--> column mapping # col_mapping = metadata[0]["col_mapping"] # col_names = list(col_mapping.keys()) st.markdown("**Map your data columns**") col1, col2 = st.columns(2) # TODO: find a better way to layout these items # TODO: propagate this information to payload # TODO: make it task specific col_mapping = {} with col1: if selected_task == "text-classification": st.markdown("`text` column") st.text("") st.text("") st.text("") st.text("") st.markdown("`target` column") elif selected_task == "question-answering": st.markdown("`context` column") st.text("") st.text("") st.text("") st.text("") st.markdown("`question` column") with col2: text_col = st.selectbox("This column should contain the text you want to classify", col_names, index=0) target_col = st.selectbox( "This column should contain the labels you want to assign to the text", col_names, index=1 ) col_mapping[text_col] = "text" col_mapping[target_col] = "target" 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 ) # , compatible_models[0]) submit_button = st.form_submit_button("Make submission") if submit_button: project_id = str(uuid.uuid4())[:3] autotrain_task_name = HUB_TASK_TO_AUTOTRAIN_TASK[selected_task] payload = { "username": AUTOTRAIN_USERNAME, "proj_name": f"my-eval-project-{project_id}", "task": TASK_TO_ID[autotrain_task_name], "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, }, }, } 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, "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"✅ Successfully submitted evaluation job with project ID {project_id}") st.markdown( f""" Evaluation takes appoximately 1 hour to complete, so grab a ☕ or 🍵 while you wait: * 📊 Click [here](https://huggingface.co/spaces/huggingface/leaderboards) to view the results from your submission """ ) else: st.error("🙈 Oh noes, there was an error submitting your submission!")