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[theme]

# The theme that your custom theme inherits from.
#
# This can be one of the following:
# - "light": Streamlit's default light theme.
# - "dark": Streamlit's default dark theme.
# - A local file path to a TOML theme file: A local custom theme, like
# "themes/custom.toml".
# - A URL to a TOML theme file: An externally hosted custom theme, like

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# Healthcare Feedback Dataset for SetFit
# 90 examples (15 per class) - suitable for few-shot learning

# Optimized label names for ML
LABEL_MAPPING = {
    0: "access_availability",
    1: "information_provision", 
    2: "privacy_confidentiality",
    3: "continuity_care",
    4: "clinical_communication",

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import spacy
from spacy.matcher import Matcher
import math
import pandas as pd

# ============================================================================
# Installation Requirements:
# 1. pip install spacy
# 2. python -m spacy download en_core_web_sm
# ============================================================================

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import math
import os
import re
from nltk import pos_tag, word_tokenize
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import pairwise_distances
import warnings
warnings.filterwarnings("ignore")

import matplotlib.pyplot as plt

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import toml
from crewai_tools import BaseTool
from typing import ClassVar, Union, Dict, Any, List
import requests

# Load the TOML file
with open("notioncrew/config_secrets.toml", "r") as f:
    config_secrets = toml.load(f)

# Load environment variables from streamlit secrets

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import spacy
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from langchain.tools import BaseTool
from typing import Optional, Union, List
from langchain.callbacks.manager import CallbackManagerForToolRun, AsyncCallbackManagerForToolRun

class DataPreprocessingTool(BaseTool):
    name = "DataPreprocessingTool"
    description = "A tool for preprocessing and structuring unstructured data."

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In this walkthrough we will see how to use Pinecone for semantic search.

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import pandas as pd
from langchain.schema import Document
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores import Chroma
from tqdm import tqdm

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