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import os | |
from textwrap import dedent | |
import streamlit as st | |
from defaults import ( | |
PROJECT_NAME, | |
ARGILLA_URL, | |
DIBT_PARENT_APP_URL, | |
DATASET_URL, | |
DATASET_REPO_ID, | |
) | |
def project_sidebar(): | |
if PROJECT_NAME == "DEFAULT_DOMAIN": | |
st.warning( | |
"Please set up the project configuration in the parent app before proceeding." | |
) | |
st.stop() | |
st.sidebar.subheader(f"A Data Growing Project in the domain of {PROJECT_NAME}") | |
st.sidebar.markdown( | |
""" | |
This space helps you create a dataset seed for building diverse domain-specific datasets for aligning models. | |
""" | |
) | |
st.sidebar.link_button(f"π Dataset Repo", DATASET_URL) | |
st.sidebar.link_button(f"π€ Argilla Space", ARGILLA_URL) | |
hub_username = DATASET_REPO_ID.split("/")[0] | |
project_name = DATASET_REPO_ID.split("/")[1] | |
st.session_state["project_name"] = project_name | |
st.session_state["hub_username"] = hub_username | |
st.session_state["hub_token"] = st.sidebar.text_input( | |
"Hub Token", type="password", value=os.environ.get("HF_TOKEN") | |
) | |
if st.checkbox("Save Hub Token") and st.session_state["hub_token"] is not None: | |
os.environ["HF_TOKEN"] = st.session_state["hub_token"] | |
st.sidebar.link_button( | |
"π€ Get your Hub Token", "https://huggingface.co/settings/tokens" | |
) | |
if all( | |
( | |
st.session_state.get("project_name"), | |
st.session_state.get("hub_username"), | |
st.session_state.get("hub_token"), | |
) | |
): | |
st.success(f"Using the dataset repo {hub_username}/{project_name} on the Hub") | |
st.sidebar.divider() | |
st.sidebar.link_button("π§βπΎ New Project", DIBT_PARENT_APP_URL) | |
if st.session_state["hub_token"] is None: | |
st.error("Please provide a Hub token to generate answers") | |
st.stop() | |
def create_seed_terms(topics: list[str], perspectives: list[str]) -> list[str]: | |
"""Create seed terms for self intruct to start from.""" | |
return [ | |
f"{topic} from a {perspective} perspective" | |
for topic in topics | |
for perspective in perspectives | |
] | |
def create_application_instruction(domain: str, examples: list[dict[str, str]]) -> str: | |
"""Create the instruction for Self-Instruct task.""" | |
system_prompt = dedent( | |
f"""You are an AI assistant than generates queries around the domain of {domain}. | |
Your should not expect basic but profound questions from your users. | |
The queries should reflect a diversxamity of vision and economic positions and political positions. | |
The queries may know about different methods of {domain}. | |
The queries can be positioned politically, economically, socially, or practically. | |
Also take into account the impact of diverse causes on diverse domains.""" | |
) | |
for example in examples: | |
question = example["question"] | |
answer = example["answer"] | |
system_prompt += f"""\n- Question: {question}\n- Answer: {answer}\n""" | |
return system_prompt | |