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| import os | |
| from pathlib import Path | |
| import streamlit as st | |
| from dotenv import load_dotenv | |
| from utils import get_compatible_models, get_metadata, 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") | |
| TASK_TO_ID = { | |
| "binary_classification": 1, | |
| "multi_class_classification": 2, | |
| "multi_label_classification": 3, | |
| "entity_extraction": 4, | |
| "extractive_question_answering": 5, | |
| "translation": 6, | |
| "summarization": 8, | |
| "single_column_regression": 10, | |
| } | |
| # TODO: remove this hardcorded logic and accept any dataset on the Hub | |
| DATASETS_TO_EVALUATE = ["emotion", "conll2003"] | |
| dataset_name = st.selectbox("Select a dataset", [f"lewtun/autoevaluate__{dset}" for dset in DATASETS_TO_EVALUATE]) | |
| with st.form(key="form"): | |
| # TODO: remove this step once we select real datasets | |
| # Strip out original dataset name | |
| original_dataset_name = dataset_name.split("/")[-1].split("__")[-1] | |
| # In general this will be a list of multiple configs => need to generalise logic here | |
| metadata = get_metadata(dataset_name) | |
| dataset_config = st.selectbox("Select a config", [metadata[0]["config"]]) | |
| 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)) | |
| col_mapping = metadata[0]["col_mapping"] | |
| col_names = list(col_mapping.values()) | |
| # TODO: figure out how to get all dataset column names (i.e. features) without download dataset itself | |
| st.markdown("**Map your data columns**") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown("`text` column") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.text("") | |
| st.markdown("`target` column") | |
| with col2: | |
| st.selectbox("This column should contain the text you want to classify", col_names, index=0) | |
| st.selectbox("This column should contain the labels you want to assign to the text", col_names, index=1) | |
| compatible_models = get_compatible_models(metadata[0]["task"], original_dataset_name) | |
| 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: | |
| for model in selected_models: | |
| payload = { | |
| "username": AUTOTRAIN_USERNAME, | |
| "task": TASK_TO_ID[metadata[0]["task_id"]], | |
| "model": model, | |
| "col_mapping": metadata[0]["col_mapping"], | |
| "split": selected_split, | |
| "dataset": original_dataset_name, | |
| "config": dataset_config, | |
| } | |
| json_resp = http_post( | |
| path="/evaluate/create", payload=payload, token=HF_TOKEN, domain=AUTOTRAIN_BACKEND_API | |
| ).json() | |
| st.success(f"✅ Successfully submitted model {model} for evaluation with job ID {json_resp['id']}") | |