skannan-maf

# Use the official Python 3.10 image as the base image
FROM python:3.10

# Set the working directory in the container
WORKDIR /app

# Install the "uv" package
RUN curl -Ls https://astral.sh/uv/install.sh | sh

# Initialize the mcp-server using "uv"

skannan-maf

from contextlib import contextmanager
import re
    
@contextmanager
def streamlit_focus(tab_id, snippet_id):
    '''
    This is a context manager that will focus the widget with the given widget_key
    '''
    def make_valid_identifier_string(text: str, char_prefix='A') -> str:
        """

skannan-maf

 #
 # This gist solves 2 problems
 # 1. Streamlit selectbox sometimes does not return correct value correpsonding to what user selected.
 #    This is a very finicky behavior where the user-selected option, occasionally, reverts back to older option
 #    and thus violating WYSIWYG. This function fixed it
 # 2. In multi-page apps, the user-selected values in selectbox gets lost when user switches to another page.
 #    This function saves the state and helps these widget maintain state even in multi-page apps
 #
 def stateful_selectbox(
        name, 

skannan-maf

import ngram

def fuzzy_match_list_of_strings(
        l1, 
        l2, 
        common_words=[],
        verbose=True
    ):
    '''
    Accepts 2 lists of strings as arguments and optionally "common_words" as 3rd argument.

skannan-maf

def st_select_rows_from_df(pdf, widget_base_key, exclusive=False, ignore_columns=[]):
    '''
    This function displays each row with a selectable checkbox
    Users can select only one of them (or) any number of them depending on the argument "exclusive"
    Function returns a list of booleans indicating whether the corresponding row was selected or not
    '''
    valid_cols = [col for col in pdf.columns if col not in ignore_columns]
    
    # Write header
    view_cols = st.columns(1+len(valid_cols))

skannan-maf

import streamlit as st
import pandas as pd
from pandas.api.types import (
    is_categorical_dtype,
    is_datetime64_any_dtype,
    is_numeric_dtype,
    is_object_dtype,
)
from datetime import datetime

skannan-maf

import time
import streamlit as st
from streamlit.components.v1 import html

#### HEADER: Start a new div
st.markdown('<div id="my-section-{tab_id}"></div>'.format(tab_id = "<your-tab-id>"), unsafe_allow_html=True)

#### PLACEHOLDER: BODY: Render the section as per your app using streamlit primitives (st.plotly_chart
#
# Your Streamlit code rendering whatever you want goes here.

skannan-maf

def get_all_cookies():
    '''
    WARNING: We use unsupported features of Streamlit
             However, this is quite fast and works well with
             the latest version of Streamlit (1.27)
    RETURNS:
    Returns the cookies as a dictionary of kv pairs
    '''
    from streamlit.web.server.websocket_headers import _get_websocket_headers 
    # https://github.com/streamlit/streamlit/pull/5457

skannan-maf

import plotly
import plotly.graph_objects as go

def plotly_display_number(n, what_is_n, color='Black'):
    '''
    Use this display big numbers in plotly
    Automatically recognizes Thousands/million/Billion and displays with a suitable prefix
    '''
    if n > 1e9:
        n /= 1e9

skannan-maf

# Basket iterator class
class tagged_basket_iterator:
    def __init__(self, tb):
        self._df = tb._df
        self._index = 0
        
    def __next__(self):
        total_len = len(self._df)
        if self._index >= total_len:
            #print("Raising after {}".format(total_len))