Spaces:
Runtime error
Runtime error
File size: 7,513 Bytes
8773ff3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
import streamlit as st
from streamlit.errors import EntryNotFoundError
from hub import pull_seed_data_from_repo, push_pipeline_to_hub
from defaults import (
DEFAULT_SYSTEM_PROMPT,
PIPELINE_PATH,
PROJECT_NAME,
ARGILLA_SPACE_REPO_ID,
DATASET_REPO_ID,
ARGILLA_SPACE_NAME,
ARGILLA_URL,
PROJECT_SPACE_REPO_ID,
HUB_USERNAME,
)
from utils import project_sidebar
from pipeline import serialize_pipeline, run_pipeline, create_pipelines_run_command
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 project details, including the project name, domain, and API credentials"
)
###############################################################
# CONFIGURATION
###############################################################
st.divider()
st.markdown("### Pipeline Configuration")
st.write("🤗 Hub details to pull the seed data")
hub_username = st.text_input("Hub Username", HUB_USERNAME)
project_name = st.text_input("Project Name", PROJECT_NAME)
repo_id = f"{hub_username}/{project_name}"
hub_token = st.text_input("Hub Token", type="password")
st.write("🤖 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:"
)
st.link_button(
"🤗 Inference compaptible models on the hub",
"https://huggingface.co/models?pipeline_tag=text-generation&other=endpoints_compatible&sort=trending",
)
base_url = st.text_input("Base URL")
st.write("🔬 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.write(
"Once you've defined the pipeline configuration, you can run the pipeline locally or on this space."
)
st.write(
"""We recommend running the pipeline locally if you're planning on generating a large dataset. \
But running the pipeline on this space is a handy way to get started quickly. Your synthetic
samples will be pushed to Argilla and available for review.
"""
)
st.write(
"""If you're planning on running the pipeline on the space, be aware that it \
will take some time to complete and you will need to maintain a \
connection to the space."""
)
if st.button("💻 Run pipeline locally", key="run_pipeline_local"):
if all(
[
argilla_api_key,
argilla_url,
base_url,
hub_username,
project_name,
hub_token,
argilla_dataset_name,
]
):
with st.spinner("Pulling seed data from the Hub..."):
seed_data = pull_seed_data_from_repo(
repo_id=f"{hub_username}/{project_name}",
hub_token=hub_token,
)
domain = seed_data["domain"]
perspectives = seed_data["perspectives"]
topics = seed_data["topics"]
examples = seed_data["examples"]
domain_expert_prompt = seed_data["domain_expert_prompt"]
with st.spinner("Serializing the pipeline configuration..."):
serialize_pipeline(
argilla_api_key=argilla_api_key,
argilla_dataset_name=argilla_dataset_name,
argilla_api_url=argilla_url,
topics=topics,
perspectives=perspectives,
pipeline_config_path=PIPELINE_PATH,
domain_expert_prompt=domain_expert_prompt or DEFAULT_SYSTEM_PROMPT,
hub_token=hub_token,
endpoint_base_url=base_url,
examples=examples,
)
push_pipeline_to_hub(
pipeline_path=PIPELINE_PATH,
hub_token=hub_token,
hub_username=hub_username,
project_name=project_name,
)
st.success(f"Pipeline configuration saved to {hub_username}/{project_name}")
st.info(
"To run the pipeline locally, you need to have the `distilabel` library installed. You can install it using the following command:"
)
st.text(
"Execute the following command to generate a synthetic dataset from the seed data:"
)
command_to_run = create_pipelines_run_command(
hub_token=hub_token,
pipeline_config_path=PIPELINE_PATH,
argilla_dataset_name=argilla_dataset_name,
)
st.code(
f"""
pip install git+https://github.com/argilla-io/distilabel.git
git clone https://huggingface.co/{hub_username}/{project_name}
cd {project_name}
{' '.join(command_to_run[2:])}
""",
language="bash",
)
else:
st.error("Please fill all the required fields.")
###############################################################
# SPACE
###############################################################
if st.button("🔥 Run pipeline right here, right now!"):
if all(
[
argilla_api_key,
argilla_url,
base_url,
hub_username,
project_name,
hub_token,
argilla_dataset_name,
]
):
with st.spinner("Pulling seed data from the Hub..."):
try:
seed_data = pull_seed_data_from_repo(
repo_id=f"{hub_username}/{project_name}",
hub_token=hub_token,
)
except EntryNotFoundError:
st.error(
"Seed data not found. Please make sure you pushed the data seed in Step 2."
)
domain = seed_data["domain"]
perspectives = seed_data["perspectives"]
topics = seed_data["topics"]
examples = seed_data["examples"]
domain_expert_prompt = seed_data["domain_expert_prompt"]
with st.spinner("Serializing the pipeline configuration..."):
serialize_pipeline(
argilla_api_key=argilla_api_key,
argilla_dataset_name=argilla_dataset_name,
argilla_api_url=argilla_url,
topics=topics,
perspectives=perspectives,
pipeline_config_path=PIPELINE_PATH,
domain_expert_prompt=domain_expert_prompt or DEFAULT_SYSTEM_PROMPT,
hub_token=hub_token,
endpoint_base_url=base_url,
examples=examples,
)
with st.spinner("Starting the pipeline..."):
logs = run_pipeline(PIPELINE_PATH)
st.success(f"Pipeline started successfully! 🚀")
with st.expander(label="View Logs", expanded=True):
for out in logs:
st.text(out)
else:
st.error("Please fill all the required fields.")
|