remove unwanted files

load_data.py

@@ -1,115 +0,0 @@
-import os
-import sys
-import requests
-import time
-import pandas as pd
-import argilla as rg
-from datasets import load_dataset
-from argilla.labeling.text_classification import Rule, add_rules
-
-
-def load_datasets():
-    # This is the code that you want to execute when the endpoint is available
-    print("Argilla is available! Loading datasets")
-    api_key = sys.argv[-1]
-    rg.init(api_key=api_key, workspace="admin")
-
-    # load dataset from json
-    my_dataframe = pd.read_json(
-        "https://raw.githubusercontent.com/recognai/datasets/main/sst-sentimentclassification.json")
-
-    # convert pandas dataframe to DatasetForTextClassification
-    dataset_rg = rg.DatasetForTextClassification.from_pandas(my_dataframe)
-
-    # Define labeling schema to avoid UI user modification
-    settings = rg.TextClassificationSettings(label_schema=["POSITIVE", "NEGATIVE"])
-    rg.configure_dataset(name="sst-sentiment-explainability", settings=settings)
-
-    # log the dataset
-    rg.log(
-        dataset_rg,
-        name="sst-sentiment-explainability",
-        tags={
-            "description": "The sst2 sentiment dataset with predictions from a pretrained pipeline and explanations from Transformers Interpret."
-        }
-    )
-
-    dataset = load_dataset("argilla/news-summary", split="train").select(range(100))
-    dataset_rg = rg.read_datasets(dataset, task="Text2Text")
-
-    # log the dataset
-    rg.log(
-        dataset_rg,
-        name="news-text-summarization",
-        tags={
-            "description": "A text summarization dataset with news pieces and their predicted summaries."
-        }
-    )
-
-    # Read dataset from Hub
-    dataset_rg = rg.read_datasets(
-        load_dataset("argilla/agnews_weak_labeling", split="train"),
-        task="TextClassification",
-    )
-
-    # Define labeling schema to avoid UI user modification
-    settings = rg.TextClassificationSettings(label_schema=["World", "Sports", "Sci/Tech", "Business"])
-    rg.configure_dataset(name="news-programmatic-labeling", settings=settings)
-
-    # log the dataset
-    rg.log(
-        dataset_rg,
-        name="news-programmatic-labeling",
-        tags={
-            "description": "The AG News with programmatic labeling rules (see weak labeling mode in the UI)."
-        }
-    )
-
-    # define queries and patterns for each category (using ES DSL)
-    queries = [
-        (["money", "financ*", "dollar*"], "Business"),
-        (["war", "gov*", "minister*", "conflict"], "World"),
-        (["*ball", "sport*", "game", "play*"], "Sports"),
-        (["sci*", "techno*", "computer*", "software", "web"], "Sci/Tech"),
-    ]
-
-    # define rules
-    rules = [Rule(query=term, label=label) for terms, label in queries for term in terms]
-
-    # add rules to the dataset
-    add_rules(dataset="news-programmatic-labeling", rules=rules)
-
-    # load dataset from the hub
-    dataset = load_dataset("argilla/gutenberg_spacy-ner", split="train")
-
-    # read in dataset, assuming its a dataset for token classification
-    dataset_rg = rg.read_datasets(dataset, task="TokenClassification")
-
-    # Define labeling schema to avoid UI user modification
-    labels = ["CARDINAL", "DATE", "EVENT", "FAC", "GPE", "LANGUAGE", "LAW", "LOC", "MONEY", "NORP", "ORDINAL", "ORG",
-              "PERCENT", "PERSON", "PRODUCT", "QUANTITY", "TIME", "WORK_OF_ART"]
-    settings = rg.TokenClassificationSettings(label_schema=labels)
-    rg.configure_dataset(name="gutenberg_spacy-ner-monitoring", settings=settings)
-
-    # log the dataset
-    rg.log(
-        dataset_rg,
-        "gutenberg_spacy-ner-monitoring",
-        tags={
-            "description": "A dataset containing text from books with predictions from two spaCy NER pre-trained models."
-        }
-    )
-
-
-while True:
-    try:
-        response = requests.get("http://0.0.0.0:6900/")
-        if response.status_code == 200:
-            load_datasets()
-            break
-        else:
-            time.sleep(10)
-    except Exception as e:
-        print(e)
-        time.sleep(10)
-        pass

start.sh

@@ -1,40 +0,0 @@
-#!/usr/bin/env bash
-
-set -e
-
-# Changing user
-sudo -S su user
-
-# Update API_KEY and PASSWORD from users.yml
-echo "sed"
-sudo sed -i 's,API_KEY,'"$API_KEY"',g' /packages/users.yml
-sudo sed -i 's,ADMIN_PASSWORD,'"$ADMIN_PASSWORD"',g' /packages/users.yml
-sudo sed -i 's,ARGILLA_PASSWORD,'"$ARGILLA_PASSWORD"',g' /packages/users.yml
-
-# Disable security in elasticsearch configuration
-sudo sed -i "s/xpack.security.enabled: true/xpack.security.enabled: false/g" /etc/elasticsearch/elasticsearch.yml
-sudo sed -i "s/cluster.initial_master_nodes/#cluster.initial_master_nodes/g" /etc/elasticsearch/elasticsearch.yml
-echo "cluster.routing.allocation.disk.threshold_enabled: false" | sudo tee -a /etc/elasticsearch/elasticsearch.yml
-
-# Create elasticsearch directory and change ownerships
-sudo mkdir -p /var/run/elasticsearch
-sudo chown -R elasticsearch:elasticsearch /var/run/elasticsearch
-sudo chown -R user:user /load_data.py
-
-# Start elasticsearch
-echo "reload"
-sudo systemctl daemon-reload
-echo "enable"
-sudo systemctl enable elasticsearch.service
-echo "starting"
-sudo systemctl start elasticsearch.service
-echo "started"
-
-# Load data
-echo "load data"
-pip3 install datasets
-python3.9 /load_data.py "$API_KEY" &
-
-# Start argilla
-echo "start argilla"
-uvicorn argilla:app --host "0.0.0.0"

start_test.sh

@@ -1,12 +0,0 @@
-set -e
-
-# Changing user
-sudo -S su user
-
-sudo systemctl start elasticsearch
-
-# Load data
-python3.9 /load_data.py &
-
-# Start argilla
-uvicorn argilla:app --host "0.0.0.0"

users.yml

@@ -1,13 +0,0 @@
-- username: "admin"
-  api_key: API_KEY
-  full_name: Hugging Face
-  email: hfdemo@argilla.io
-  hashed_password: ADMIN_PASSWORD
-  workspaces: [ ]
-
-- username: "argilla"
-  api_key: API_KEY
-  full_name: Hugging Face
-  email: hfdemo@argilla.io
-  hashed_password: ARGILLA_PASSWORD
-  workspaces: [ "admin" ]