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import streamlit as st
from defaults import ARGILLA_URL
from hub import push_pipeline_params, push_pipeline_to_hub
from utils import project_sidebar
st.set_page_config(
page_title="Domain Data Grower",
page_icon="🧑🌾",
)
project_sidebar()
################################################################################
# HEADER
################################################################################
st.header("🧑🌾 Domain Data Grower")
st.divider()
st.subheader("Step 3. Run the pipeline to generate synthetic data")
st.write("Define the distilabel pipeline for generating the dataset.")
###############################################################
# CONFIGURATION
###############################################################
hub_username = st.session_state.get("hub_username")
project_name = st.session_state.get("project_name")
hub_token = st.session_state.get("hub_token")
st.divider()
st.markdown("#### 🤖 Inference configuration")
st.write(
"Add the url of the Huggingface inference API or endpoint that your pipeline should use. You can find compatible models here:"
)
with st.expander("🤗 Recommended Models"):
st.write("All inference endpoint compatible models can be found via the link below")
st.link_button(
"🤗 Inference compaptible models on the hub",
"https://huggingface.co/models?pipeline_tag=text-generation&other=endpoints_compatible&sort=trending",
)
st.write("🔋Projects with sufficient resources could take advantage of LLama3 70b")
st.code("https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B")
st.write("🪫Projects with less resources could take advantage of LLama 3 8b")
st.code("https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B")
st.write("🍃Projects with even less resources could take advantage of Phi-2")
st.code("https://api-inference.huggingface.co/models/microsoft/phi-2")
st.write("Note Hugggingface Pro gives access to more compute resources")
st.link_button(
"🤗 Huggingface Pro",
"https://huggingface.co/pricing",
)
base_url = st.text_input(
label="Base URL for the Inference API",
value="https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta",
)
st.divider()
st.markdown("#### 🔬 Argilla API details to push the generated dataset")
argilla_url = st.text_input("Argilla API URL", ARGILLA_URL)
argilla_api_key = st.text_input("Argilla API Key", "owner.apikey")
argilla_dataset_name = st.text_input("Argilla Dataset Name", project_name)
st.divider()
###############################################################
# LOCAL
###############################################################
st.markdown("## Run the pipeline")
st.markdown(
"Once you've defined the pipeline configuration above, you can run the pipeline from your local machine."
)
if all(
[
argilla_api_key,
argilla_url,
base_url,
hub_token,
project_name,
hub_token,
argilla_dataset_name,
]
):
push_pipeline_params(
pipeline_params={
"argilla_api_key": argilla_api_key,
"argilla_api_url": argilla_url,
"argilla_dataset_name": argilla_dataset_name,
"endpoint_base_url": base_url,
},
hub_username=hub_username,
hub_token=hub_token,
project_name=project_name,
)
push_pipeline_to_hub(
pipeline_path="pipeline.py",
hub_username=hub_username,
hub_token=hub_token,
project_name=project_name,
)
st.markdown(
"To run the pipeline locally, you need to have the `distilabel` library installed. You can install it using the following command:"
)
st.code(
f"""
# Install the distilabel library
pip install git+https://github.com/argilla-io/distilabel.git
"""
)
st.markdown("Next, you'll need to clone your dataset repo and run the pipeline:")
st.code(
f"""
git clone https://huggingface.co/datasets/{hub_username}/{project_name}
cd {project_name}
pip install -r requirements.txt
"""
)
st.markdown("Finally, you can run the pipeline using the following command:")
st.code(
"""
huggingface-cli login
python pipeline.py""",
language="bash",
)
st.markdown(
"👩🚀 If you want to customise the pipeline take a look in `pipeline.py` and teh [distilabel docs](https://distilabel.argilla.io/)"
)
else:
st.info("Please fill all the required fields.")
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