app.py

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!")