parsing.py

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from uap_analyzer import UAPParser, UAPAnalyzer, UAPVisualizer
# import ChartGen
# from ChartGen import ChartGPT
from Levenshtein import distance
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from stqdm import stqdm
stqdm.pandas()
import streamlit.components.v1 as components
from dateutil import parser
from sentence_transformers import SentenceTransformer
import torch
import squarify
import matplotlib.colors as mcolors
import textwrap
import datamapplot
import openai
from openai import OpenAI
import os
import json
# this is a test comment
import plotly.graph_objects as go

st.set_option('deprecation.showPyplotGlobalUse', False)

from pandas.api.types import (
    is_categorical_dtype,
    is_datetime64_any_dtype,
    is_numeric_dtype,
    is_object_dtype,
)



def load_data(file_path, key='df'):
    return pd.read_hdf(file_path, key=key)


def gemini_query(question, selected_data, gemini_key):

    if question == "":
        question = "Summarize the following data in relevant bullet points"

    import pathlib
    import textwrap

    import google.generativeai as genai

    from IPython.display import display
    from IPython.display import Markdown


    def to_markdown(text):
        text = text.replace('•', '  *')
        return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))
    
    # selected_data is a list
    # remove empty

    filtered = [str(x) for x in selected_data if str(x) != '' and x is not None]
    # make a string
    context = '\n'.join(filtered)

    genai.configure(api_key=gemini_key)
    query_model = genai.GenerativeModel('models/gemini-1.5-pro-latest')
    response = query_model.generate_content([f"{question}\n Answer based on this context: {context}\n\n"])
    return(response.text)

def plot_treemap(df, column, top_n=32):
        # Get the value counts and the top N labels
        value_counts = df[column].value_counts()
        top_labels = value_counts.iloc[:top_n].index
        
        # Use np.where to replace all values not in the top N with 'Other'
        revised_column = f'{column}_revised'
        df[revised_column] = np.where(df[column].isin(top_labels), df[column], 'Other')

        # Get the value counts including the 'Other' category
        sizes = df[revised_column].value_counts().values
        labels = df[revised_column].value_counts().index

        # Get a gradient of colors
        # colors = list(mcolors.TABLEAU_COLORS.values())

        n_colors = len(sizes)
        colors = plt.cm.Oranges(np.linspace(0.3, 0.9, n_colors))[::-1]


        # Get % of each category
        percents = sizes / sizes.sum()

        # Prepare labels with percentages
        labels = [f'{label}\n {percent:.1%}' for label, percent in zip(labels, percents)]

        fig, ax = plt.subplots(figsize=(20, 12))

        # Plot the treemap
        squarify.plot(sizes=sizes, label=labels, alpha=0.7, pad=True, color=colors, text_kwargs={'fontsize': 10})

        ax = plt.gca()
        # Iterate over text elements and rectangles (patches) in the axes for color adjustment
        for text, rect in zip(ax.texts, ax.patches):
            background_color = rect.get_facecolor()
            r, g, b, _ = mcolors.to_rgba(background_color)
            brightness = np.average([r, g, b])
            text.set_color('white' if brightness < 0.5 else 'black')

            # Adjust font size based on rectangle's area and wrap long text
  

st.set_option('deprecation.showPyplotGlobalUse', False)

from pandas.api.types import (
    is_categorical_dtype,
    is_datetime64_any_dtype,
    is_numeric_dtype,
    is_object_dtype,
)


class CachedUAPParser(UAPParser):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if 'parsed_responses' not in st.session_state:
            st.session_state['parsed_responses'] = {}

    def parse_responses(self):
        parsed_responses = {}
        not_parsed = 0
        try:
            for k, v in self.responses.items():
                try:
                    parsed_responses[k] = json.loads(v)
                except:
                    try:
                        parsed_responses[k] = json.loads(v.replace("'", '"'))
                    except:
                        not_parsed += 1

            # Update the cached responses
            st.session_state['parsed_responses'] = parsed_responses
        except Exception as e:
            st.error(f"Error parsing responses: {e}")
 
        st.write(f"Number of unparsed responses: {not_parsed}")
        st.write(f"Number of parsed responses: {len(parsed_responses)}")
        return st.session_state['parsed_responses']

    def responses_to_df(self, col, parsed_responses):
        try:
            parsed_df = pd.DataFrame(parsed_responses).T
            if col is not None:
                parsed_df2 = pd.json_normalize(parsed_df[col])
                parsed_df2.index = parsed_df.index
            else:
                parsed_df2 = pd.json_normalize(parsed_df)
                parsed_df2.index = parsed_df.index
            
            # Convert problematic columns to string
            for column in parsed_df2.columns:
                if parsed_df2[column].dtype == 'object':
                    parsed_df2[column] = parsed_df2[column].astype(str)
            
            return parsed_df2
        except Exception as e:
            st.error(f"Error converting responses to DataFrame: {e}")
            return pd.DataFrame()  # Return an empty DataFrame if conversion fails


def load_data(file_path, key='df'):
    return pd.read_hdf(file_path, key=key)


def gemini_query(question, selected_data, gemini_key):

    if question == "":
        question = "Summarize the following data in relevant bullet points"

    import pathlib
    import textwrap

    import google.generativeai as genai

    from IPython.display import display
    from IPython.display import Markdown


    def to_markdown(text):
        text = text.replace('•', '  *')
        return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))
    
    # selected_data is a list
    # remove empty

    filtered = [str(x) for x in selected_data if str(x) != '' and x is not None]
    # make a string
    context = '\n'.join(filtered)

    genai.configure(api_key=gemini_key)
    query_model = genai.GenerativeModel('models/gemini-1.5-pro-latest')
    response = query_model.generate_content([f"{question}\n Answer based on this context: {context}\n\n"])
    return(response.text)


def plot_hist(df, column, bins=10, kde=True):
        fig, ax = plt.subplots(figsize=(12, 6))
        sns.histplot(data=df, x=column, kde=True, bins=bins,color='orange')
        # set the ticks and frame in orange
        ax.spines['bottom'].set_color('orange')
        ax.spines['top'].set_color('orange')
        ax.spines['right'].set_color('orange')
        ax.spines['left'].set_color('orange')
        ax.xaxis.label.set_color('orange')
        ax.yaxis.label.set_color('orange')
        ax.tick_params(axis='x', colors='orange')
        ax.tick_params(axis='y', colors='orange')
        ax.title.set_color('orange')

        # Set transparent background
        fig.patch.set_alpha(0)
        ax.patch.set_alpha(0)
        return fig



def is_api_key_valid(api_key, model='gpt-3.5-turbo'):
    try:
        os.environ['OPENAI_API_KEY'] = api_key
        client = OpenAI()
        response = client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": 'Say Hello World!'}])
        text = response.choices[0].message.content
        if len(text) >= 0:
            return True
    except Exception as e:
       st.error(f'Error with the API key :{e}')
       return False

def download_json(data):
    json_str = json.dumps(data, indent=2)
    return json_str


def convert_cached_data_to_df(parser):
    if 'parsed_responses' in st.session_state:
        #parser = CachedUAPParser(api_key=API_KEY, model='gpt-3.5-turbo-0125')
        try:
            responses_df = parser.responses_to_df('sightingDetails', st.session_state['parsed_responses'])
        except Exception as e:
            st.warning(f"Error parsing with 'sightingDetails': {e}")
            responses_df = parser.responses_to_df(None, st.session_state['parsed_responses'])
        if not responses_df.empty:
            st.dataframe(responses_df)
            st.session_state['parsed_responses_df'] = responses_df.copy()
            st.success("Successfully converted cached data to DataFrame.")
        else:
            st.error("Failed to create DataFrame from cached responses.")
    else:
        st.warning("No cached data available. Please parse the dataset first.")

def plot_line(df, x_column, y_columns, figsize=(12, 10), color='orange', title=None, rolling_mean_value=2):
    import matplotlib.cm as cm
    # Sort the dataframe by the date column
    df = df.sort_values(by=x_column)

    # Calculate rolling mean for each y_column
    if rolling_mean_value:
        df[y_columns] = df[y_columns].rolling(len(df) // rolling_mean_value).mean()

    # Create the plot
    fig, ax = plt.subplots(figsize=figsize)

    colors = cm.Oranges(np.linspace(0.2, 1, len(y_columns)))

    # Plot each y_column as a separate line with a different color
    for i, y_column in enumerate(y_columns):
        df.plot(x=x_column, y=y_column, ax=ax, color=colors[i], label=y_column, linewidth=.5)

    # Rotate x-axis labels
    ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha='right')

    # Format x_column as date if it is
    if np.issubdtype(df[x_column].dtype, np.datetime64) or np.issubdtype(df[x_column].dtype, np.timedelta64):
        df[x_column] = pd.to_datetime(df[x_column]).dt.date

    # Set title, labels, and legend
    ax.set_title(title or f'{", ".join(y_columns)} over {x_column}', color=color, fontweight='bold')
    ax.set_xlabel(x_column, color=color)
    ax.set_ylabel(', '.join(y_columns), color=color)
    ax.spines['bottom'].set_color('orange')
    ax.spines['top'].set_color('orange')
    ax.spines['right'].set_color('orange')
    ax.spines['left'].set_color('orange')
    ax.xaxis.label.set_color('orange')
    ax.yaxis.label.set_color('orange')
    ax.tick_params(axis='x', colors='orange')
    ax.tick_params(axis='y', colors='orange')
    ax.title.set_color('orange')

    ax.legend(loc='upper right', bbox_to_anchor=(1, 1), facecolor='black', framealpha=.4, labelcolor='orange', edgecolor='orange')

    # Remove background
    fig.patch.set_alpha(0)
    ax.patch.set_alpha(0)

    return fig


def plot_bar(df, x_column, y_column, figsize=(12, 10), color='orange', title=None):
    fig, ax = plt.subplots(figsize=figsize)

    sns.barplot(data=df, x=x_column, y=y_column, color=color, ax=ax)

    # Rotate x-axis labels
    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')

    ax.set_title(title if title else f'{y_column} by {x_column}', color=color, fontweight='bold')
    ax.set_xlabel(x_column, color=color)
    ax.set_ylabel(y_column, color=color)

    ax.tick_params(axis='x', colors=color)
    ax.tick_params(axis='y', colors=color)

    # Remove background
    fig.patch.set_alpha(0)
    ax.patch.set_alpha(0)
    ax.spines['bottom'].set_color('orange')
    ax.spines['top'].set_color('orange')
    ax.spines['right'].set_color('orange')
    ax.spines['left'].set_color('orange')
    ax.xaxis.label.set_color('orange')
    ax.yaxis.label.set_color('orange')
    ax.tick_params(axis='x', colors='orange')
    ax.tick_params(axis='y', colors='orange')
    ax.title.set_color('orange')
    ax.legend(loc='upper right', bbox_to_anchor=(1, 1), facecolor='black', framealpha=.4, labelcolor='orange', edgecolor='orange')
    return fig

def plot_grouped_bar(df, x_columns, y_column, figsize=(12, 10), colors=None, title=None):
    fig, ax = plt.subplots(figsize=figsize)

    width = 0.8 / len(x_columns)  # the width of the bars
    x = np.arange(len(df))  # the label locations

    for i, x_column in enumerate(x_columns):
        sns.barplot(data=df, x=x, y=y_column, color=colors[i] if colors else None, ax=ax, width=width, label=x_column)
        x += width  # add the width of the bar to the x position for the next bar

    ax.set_title(title if title else f'{y_column} by {", ".join(x_columns)}', color='orange', fontweight='bold')
    ax.set_xlabel('Groups', color='orange')
    ax.set_ylabel(y_column, color='orange')

    ax.set_xticks(x - width * len(x_columns) / 2)
    ax.set_xticklabels(df.index)

    ax.tick_params(axis='x', colors='orange')
    ax.tick_params(axis='y', colors='orange')

    # Remove background
    fig.patch.set_alpha(0)
    ax.patch.set_alpha(0)
    ax.spines['bottom'].set_color('orange')
    ax.spines['top'].set_color('orange')
    ax.spines['right'].set_color('orange')
    ax.spines['left'].set_color('orange')
    ax.xaxis.label.set_color('orange')
    ax.yaxis.label.set_color('orange')
    ax.title.set_color('orange')
    ax.legend(loc='upper right', bbox_to_anchor=(1, 1), facecolor='black', framealpha=.4, labelcolor='orange', edgecolor='orange')

    return fig

@st.cache_data
def convert_df(df):
    # IMPORTANT: Cache the conversion to prevent computation on every rerun
    try:
        csv = df.to_csv().encode("utf-8")
    except:
        csv = df.to_csv().encode("utf-8-sig")
    return csv


def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame:
    """
    Adds a UI on top of a dataframe to let viewers filter columns

    Args:
        df (pd.DataFrame): Original dataframe

    Returns:
        pd.DataFrame: Filtered dataframe
    """

    title_font = "Arial"
    body_font = "Arial"
    title_size = 32
    colors = ["red", "green", "blue"]
    interpretation = False
    extract_docx = False
    title = "My Chart"
    regex = ".*"
    img_path = 'default_image.png'


    #try:
    #    modify = st.checkbox("Add filters on raw data")
    #except:
    #    try:
    #        modify = st.checkbox("Add filters on processed data")
    #    except:
    #        try:
    #            modify = st.checkbox("Add filters on parsed data")
    #        except:
    #            pass

    #if not modify:
    #    return df

    df_ = df.copy()
    # Try to convert datetimes into a standard format (datetime, no timezone)

#modification_container = st.container()

#with modification_container:
    to_filter_columns = st.multiselect("Filter dataframe on", df_.columns)

    date_column = None
    filtered_columns = []

    for column in to_filter_columns:
        left, right = st.columns((1, 20))
        # Treat columns with < 200 unique values as categorical if not date or numeric
        if is_categorical_dtype(df_[column]) or (df_[column].nunique() < 120 and not is_datetime64_any_dtype(df_[column]) and not is_numeric_dtype(df_[column])):
            user_cat_input = right.multiselect(
                f"Values for {column}",
                df_[column].value_counts().index.tolist(),
                default=list(df_[column].value_counts().index)
            )
            df_ = df_[df_[column].isin(user_cat_input)]
            filtered_columns.append(column)

            with st.status(f"Category Distribution: {column}", expanded=False) as stat:
                st.pyplot(plot_treemap(df_, column))

        elif is_numeric_dtype(df_[column]):
            _min = float(df_[column].min())
            _max = float(df_[column].max())
            step = (_max - _min) / 100
            user_num_input = right.slider(
                f"Values for {column}",
                min_value=_min,
                max_value=_max,
                value=(_min, _max),
                step=step,
            )
            df_ = df_[df_[column].between(*user_num_input)]
            filtered_columns.append(column)

            # Chart_GPT = ChartGPT(df_, title_font, body_font, title_size,
            #      colors, interpretation, extract_docx, img_path)

            with st.status(f"Numerical Distribution: {column}", expanded=False) as stat_:
                st.pyplot(plot_hist(df_, column, bins=int(round(len(df_[column].unique())-1)/2)))

        elif is_object_dtype(df_[column]):
            try:
                df_[column] = pd.to_datetime(df_[column], infer_datetime_format=True, errors='coerce')
            except Exception:
                try:
                    df_[column] = df_[column].apply(parser.parse)
                except Exception:
                    pass

            if is_datetime64_any_dtype(df_[column]):
                df_[column] = df_[column].dt.tz_localize(None)
                min_date = df_[column].min().date()
                max_date = df_[column].max().date()
                user_date_input = right.date_input(
                    f"Values for {column}",
                    value=(min_date, max_date),
                    min_value=min_date,
                    max_value=max_date,
                )
                # if len(user_date_input) == 2:
                #     start_date, end_date = user_date_input
                #     df_ = df_.loc[df_[column].dt.date.between(start_date, end_date)]
                if len(user_date_input) == 2:
                    user_date_input = tuple(map(pd.to_datetime, user_date_input))
                    start_date, end_date = user_date_input
                     # Determine the most appropriate time unit for plot
                    time_units = {
                        'year': df_[column].dt.year,
                        'month': df_[column].dt.to_period('M'),
                        'day': df_[column].dt.date
                    }
                    unique_counts = {unit: col.nunique() for unit, col in time_units.items()}
                    closest_to_36 = min(unique_counts, key=lambda k: abs(unique_counts[k] - 36))

                    # Group by the most appropriate time unit and count occurrences
                    grouped = df_.groupby(time_units[closest_to_36]).size().reset_index(name='count')
                    grouped.columns = [column, 'count']

                    # Create a complete date range
                    if closest_to_36 == 'year':
                        date_range = pd.date_range(start=f"{start_date.year}-01-01", end=f"{end_date.year}-12-31", freq='YS')
                    elif closest_to_36 == 'month':
                        date_range = pd.date_range(start=start_date.replace(day=1), end=end_date + pd.offsets.MonthEnd(0), freq='MS')
                    else:  # day
                        date_range = pd.date_range(start=start_date, end=end_date, freq='D')

                    # Create a DataFrame with the complete date range
                    complete_range = pd.DataFrame({column: date_range})

                    # Convert the date column to the appropriate format based on closest_to_36
                    if closest_to_36 == 'year':
                        complete_range[column] = complete_range[column].dt.year
                    elif closest_to_36 == 'month':
                        complete_range[column] = complete_range[column].dt.to_period('M')

                    # Merge the complete range with the grouped data
                    final_data = pd.merge(complete_range, grouped, on=column, how='left').fillna(0)

                    with st.status(f"Date Distributions: {column}", expanded=False) as stat:
                        try:
                            st.pyplot(plot_bar(final_data, column, 'count'))
                        except Exception as e:
                            st.error(f"Error plotting bar chart: {e}")
                    df_ = df_.loc[df_[column].between(start_date, end_date)]

                date_column = column

                if date_column and filtered_columns:
                    numeric_columns = [col for col in filtered_columns if is_numeric_dtype(df_[col])]
                    if numeric_columns:
                        fig = plot_line(df_, date_column, numeric_columns)
                        #st.pyplot(fig)
                    # now to deal with categorical columns
                    categorical_columns = [col for col in filtered_columns if is_categorical_dtype(df_[col])]
                    if categorical_columns:
                        fig2 = plot_bar(df_, date_column, categorical_columns[0])
                        #st.pyplot(fig2)
                    with st.status(f"Date Distribution: {column}", expanded=False) as stat:
                        try:
                            st.pyplot(fig)
                        except Exception as e:
                            st.error(f"Error plotting line chart: {e}")
                            pass
                        try:
                            st.pyplot(fig2)
                        except Exception as e:
                            st.error(f"Error plotting bar chart: {e}")


        else:
            user_text_input = right.text_input(
                f"Substring or regex in {column}",
            )
            if user_text_input:
                df_ = df_[df_[column].astype(str).str.contains(user_text_input)]
    # write len of df after filtering with % of original
    st.write(f"{len(df_)} rows ({len(df_) / len(df) * 100:.2f}%)")
    return df_


from config import API_KEY, GEMINI_KEY, FORMAT_LONG

with torch.no_grad():
    torch.cuda.empty_cache()

#st.set_page_config(
#    page_title="UAP ANALYSIS",
#    page_icon=":alien:",
#    layout="wide",
#    initial_sidebar_state="expanded",
#)

st.title('UAP Feature Extraction')

# Initialize session state
if 'analyzers' not in st.session_state:
    st.session_state['analyzers'] = []
if 'col_names' not in st.session_state:
    st.session_state['col_names'] = []
if 'clusters' not in st.session_state:
    st.session_state['clusters'] = {}
if 'new_data' not in st.session_state:
    st.session_state['new_data'] = pd.DataFrame()
if 'dataset' not in st.session_state:
    st.session_state['dataset'] = pd.DataFrame()
if 'data_processed' not in st.session_state:
    st.session_state['data_processed'] = False
if 'stage' not in st.session_state:
    st.session_state['stage'] = 0
if 'filtered_data' not in st.session_state:
    st.session_state['filtered_data'] = None
if 'gemini_answer' not in st.session_state:
    st.session_state['gemini_answer'] = None
if 'parsed_responses' not in st.session_state:
    st.session_state['parsed_responses'] = None
if 'parsed_responses_df' not in st.session_state:
    st.session_state['parsed_responses_df'] = None
if 'json_format' not in st.session_state:
    st.session_state['json_format'] = None
if 'api_key_valid' not in st.session_state:
    st.session_state['api_key_valid'] = False
if 'previous_api_key' not in st.session_state:
    st.session_state['previous_api_key'] = None

    
# Unparsed data
#unparsed_tickbox = st.checkbox('Data Parsing')
#if unparsed_tickbox:
unparsed = st.file_uploader("Upload Raw DataFrame", type=["csv", "xlsx"])
if unparsed is not None:
    try:
        data = pd.read_csv(unparsed) if unparsed.type == "text/csv" else pd.read_excel(unparsed)
        filtered_data = filter_dataframe(data)
        st.dataframe(filtered_data)
    except Exception as e:
        st.error(f"An error occurred while reading the file: {e}")
    
    modify_json = st.checkbox('Custom JSON')
    API_KEY = st.text_input('OpenAI API Key', API_KEY, type='password', help="Enter your OpenAI API key")       
        
    
    
    if modify_json:
        FORMAT_LONG = st.text_area('Custom JSON', FORMAT_LONG, height=500)
        st.download_button("Save Format", FORMAT_LONG)
    try:
        json.loads(FORMAT_LONG)
        st.session_state['json_format'] = True
    except json.JSONDecodeError as e:
        st.error(f"Invalid JSON format: {str(e)}")
        st.session_state['json_format'] = False
        st.stop()  # Stop execution if JSON is invalid

    # If the DataFrame is successfully created, allow the user to select a column
    col_unparsed = st.selectbox("Select column corresponding to text", data.columns)
    
        
    if st.button("Parse Dataset") and st.session_state['json_format']:
        if API_KEY:
            # Only validate if the API key has changed
            if API_KEY != st.session_state['previous_api_key']:
                if is_api_key_valid(API_KEY):
                    st.session_state['api_key_valid'] = True
                    st.session_state['previous_api_key'] = API_KEY
                    st.success("API key is valid!")
                else:
                    st.session_state['api_key_valid'] = False
                    st.error("Invalid API key. Please check and try again.")
            elif st.session_state['api_key_valid']:
                st.success("API key is valid!")
        if not API_KEY:# or not st.session_state['api_key_valid']:
            st.warning("Please enter your API key to proceed.")
            st.stop()
        selected_column_data = filtered_data[col_unparsed].tolist()
        st.session_state.result = selected_column_data
        with st.status("Parsing...", expanded=True) as stat:
            try:
                st.write("Parsing descriptions...")
                parser = CachedUAPParser(api_key=API_KEY, model='gpt-3.5-turbo-0125', col=st.session_state.result)
                descriptions = st.session_state.result
                format_long = FORMAT_LONG
                parser.process_descriptions(descriptions, format_long)
                st.session_state['parsed_responses'] = parser.parse_responses()
                try:
                    responses_df = parser.responses_to_df('sightingDetails', st.session_state['parsed_responses'])
                except Exception as e:
                    st.warning(f"Error parsing with 'sightingDetails': {e}")
                    responses_df = parser.responses_to_df(None, st.session_state['parsed_responses'])
                
                if not responses_df.empty:
                    st.dataframe(responses_df)
                    st.session_state['parsed_responses_df'] = responses_df.copy()
                    stat.update(label="Parsing complete", state="complete", expanded=False)
                else:
                    st.error("Failed to create DataFrame from parsed responses.")
            except Exception as e:
                st.error(f"An error occurred during parsing: {str(e)}")

    # Add download button for parsed data
    if st.session_state['parsed_responses'] is not None:
        json_str = download_json(st.session_state['parsed_responses'])
        st.download_button(
            label="Download Parsed Data as JSON",
            data=json_str,
            file_name="parsed_responses.json",
            mime="application/json"
        )
        # Add button to convert cached data to DataFrame
        if st.button("Convert Cached Data to DataFrame"):
            convert_cached_data_to_df(st.session_state['parsed_responses'])
            
    if st.session_state['parsed_responses_df'] is not None:
        st.download_button(
        label="Save CSV",
        data=convert_df(st.session_state['parsed_responses_df']),
        file_name="uap_data.csv",
        mime="text/csv",
        )

    
        



#except Exception as e:
#    stat.update(label=f"Parsing failed: {e}", state="error")
# st.write("Parsing descriptions...")
# st.update_status("Parsing descriptions...")