TypeError: unsupported operand type(s) for +: 'float' and 'str'.

[dosafecorporation]

import streamlit as st
import time
import numpy as np
import pandas as pd
from io import StringIO
import datetime
import plotly.express as px
import warnings
warnings.filterwarnings('ignore')   


st.set_page_config(page_title="Finance Dashboard", page_icon="📈",layout="wide")
st.title(" :bar_chart: Tableau des bords des finances")
st.markdown('<style>div.block-container{padding-top:1rem;}</style>',unsafe_allow_html=True)

#import database
file = st.file_uploader(":file_folder: Selectionne la BD Excel à analyser")

#test is the file is selected

if file is not None:
    
    # Can be used wherever a "file-like" object is accepted:
    
    dataframe=pd.read_excel(file)
   
    #creating a filter for the data according to a period selected

    col1, col2 = st.columns((2))
    dataframe["MOIS"] = pd.to_datetime(dataframe["MOIS"])

    # Getting the min and max date 
    startDate = pd.to_datetime(dataframe["MOIS"]).min()
    endDate = pd.to_datetime(dataframe["MOIS"]).max()

    with col1:
        date1 = pd.to_datetime(st.date_input("Start Date", startDate))

    with col2:
        date2 = pd.to_datetime(st.date_input("End Date", endDate))

    dataframes = dataframe[(dataframe["MOIS"] >= date1) & (dataframe["MOIS"] <= date2)].copy()

    #Defining filters
    
    st.sidebar.header("Choisis les filtres: ")
    # Create filter for Prestataire
    Prestataire = st.sidebar.multiselect("Selectionne les prestataires", dataframes["Prestataires"].unique())
    if not Prestataire:
        dataframe_2 = dataframes.copy()
    else:
        dataframe_2 = dataframes[dataframes["Prestataires"].isin(Prestataire)]
    
    # Create filter for Assureur
    Assureur = st.sidebar.multiselect("Choisir les assureurs", dataframe_2["Groupes"].unique())
    if not Assureur:
        dataframe_3 = dataframe_2.copy()
    else:
        dataframe_3 = dataframe_2[dataframe_2["Groupes"].isin(Assureur)]

    # Create filter for Client 
    Client = st.sidebar.multiselect("Choisir les clients", dataframe_3["Clients"].unique())
    
    if not Client:
        dataframe_4 = dataframe_3.copy()
    else:
        dataframe_4 = dataframe_3[dataframe_3["Clients"].isin(Client)]

    
    if not Prestataire and not Assureur and not Prestataire:
        filtered_dataframes = dataframe_4
    elif not Assureur and not Client:
        filtered_dataframes = dataframes[dataframes["Prestataires"].isin(Prestataire)]
    elif not Prestataire and not Client:
        filtered_dataframes = dataframes[dataframes["Groupes"].isin(Assureur)]
    elif Assureur and Client:
        filtered_dataframes = dataframe_3[dataframes["Groupes"].isin(Assureur) & dataframe_3["Clients"].isin(Client)]
    elif Prestataire and Client:
        filtered_dataframes = dataframe_3[dataframes["Prestataires"].isin(Prestataire) & dataframe_3["Clients"].isin(Client)]
    elif Prestataire and Assureur:
        filtered_dataframes = dataframe_3[dataframes["Prestataires"].isin(Prestataire) & dataframe_3["Groupes"].isin(Assureur)]
    elif Client:
        filtered_dataframes = dataframe_3[dataframe_3["Clients"].isin(Client)]
    else:
        filtered_dataframes = dataframe_3[dataframe_3["Prestataires"].isin(Prestataire) & dataframe_3["Groupes"].isin(Assureur) & dataframe_3["Clients"].isin(Client)]
    
    #creating aggregates to view

    Claims_recus=filtered_dataframes["Claims reçus"].sum()
    Montant_reçu=filtered_dataframes["Montant de la facture"].sum()
    Reste_payer=filtered_dataframes["Reste à payer"].sum()
    

    col1, col2, col3 = st.columns(3)
    col1.metric("Nombre de claims reçus", Claims_recus)
    col2.metric("Montant de la facture", Montant_reçu)
    col3.metric("Reste à payer", Reste_payer)
    
    number_of_result = filtered_dataframes.shape[0]

    st.markdown(f'*Available Results: {number_of_result}*')

    category_df = filtered_dataframes.groupby(by = ["Groupes"], as_index = False)["Reste à payer"].sum()

    st.subheader("Reste à payer par clients")
    
    st.subheader("Reste à payer par assureurs")
    fig = px.bar(category_df, x = "Groupes", y = "Reste à payer", 
                    template = "seaborn")
        
    st.plotly_chart(fig,use_container_width=True, height = 200)
 
# rerun.
st.button("Re-run")

[liZe]

Hi!

Could you please share a backtrace? Installing the dependencies of your script doesn’t install WeasyPrint, so it’s hard for us to reproduce.

(And we’re not an AI, we’re real people, you can write a few words in introduction!)

[liZe]

Feel free to add a comment if there’s anything we can do for you!