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| import os | |
| from pathlib import Path | |
| import pandas as pd | |
| import streamlit as st | |
| from datasets import load_dataset | |
| from dotenv import load_dotenv | |
| if Path(".env").is_file(): | |
| load_dotenv(".env") | |
| st.set_page_config(layout="wide") | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| ds = load_dataset("HuggingFaceH4/instruction-pilot-outputs-filtered", split="train", use_auth_token=HF_TOKEN) | |
| st.markdown("# Instruction Model Outputs") | |
| st.markdown( | |
| """This app shows the outputs of various open-souce, instruction-trained models from a [dataset](https://huggingface.co/datasets/HuggingFaceH4/instruction-pilot-outputs-filtered) of human demonstrations filtered for overlap with the original prompt and canned responses. Hit the button below to view a few random samples from the generated outputs.""" | |
| ) | |
| st.markdown( | |
| """**Notes** | |
| * Some outputs contain a `Human:` prefix - this is likely due to the fact each model was prompted to be a dialogue agent. | |
| * The outputs were generated deterministically with `temperature=0` and `max_new_tokens=100` | |
| """ | |
| ) | |
| button = st.button("Show me what you got!") | |
| if button is True: | |
| sample_ds = ds.shuffle().select(range(5)) | |
| for sample in sample_ds: | |
| st.markdown(f'**Prompt:** {sample["prompt"]}') | |
| df = pd.DataFrame.from_records(sample["filtered_outputs"]) | |
| # CSS to inject contained in a string | |
| hide_table_row_index = """ | |
| <style> | |
| thead tr th:first-child {display:none} | |
| tbody th {display:none} | |
| </style> | |
| """ | |
| # Inject CSS with Markdown | |
| st.markdown(hide_table_row_index, unsafe_allow_html=True) | |
| st.table(df) | |