File size: 2,817 Bytes
42d3faf f480eb4 42d3faf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | import streamlit as st
import pandas as pd
import openpyxl
# Load data from Excel file
@st.cache_data
def load_data(file_path):
return pd.read_excel(file_path)
# Path to the Excel file
file_path = "BullSale.xlsx"
# Columns to display
columns_to_display = [
"Animal ID", "Name", "Sex", "Birth Date", # Static columns
"ProS", "ProS%", "HerdBuilder", "HerdBuilder %", "GridMaster", "GridMaster %",
"CED", "CED Acc", "CED %", "BW", "BW Acc", "BW %", "WW", "WW Acc", "WW %", "YW",
"YW Acc", "YW %", "ADG", "ADG Acc", "ADG %", "DMI", "DMI Acc", "DMI %", "Milk",
"Milk Acc", "Milk %", "ME", "ME Acc", "ME %", "HPG", "HPG Acc", "HPG %", "CEM",
"CEM Acc", "CEM %", "Stay", "Stay Acc", "Stay %", "Marb", "Marb Acc", "Marb %",
"YG", "YG Acc", "YG %", "CW", "CW Acc", "CW %", "RE", "RE Acc", "RE %", "BF",
"BF Acc", "BF %", "Actual BW", "WW Date", "Actual WW", "Sire ID", "Sire Reg #",
]
try:
# Load data
data = load_data(file_path)
# Ensure Type column exists
if "Type" not in data.columns:
st.error("The 'Type' column is missing. Please check your file.")
else:
st.title("Bull Sale Inventory Dashboard")
# Get unique bull types
types = data["Type"].unique()
# Sidebar for selecting bull types
st.sidebar.header("Filter by Bull Type")
selected_types = st.sidebar.multiselect(
"Select the type(s) of bulls you're interested in:",
options=types,
default=types, # Select all types by default
)
if selected_types:
for selected_type in selected_types:
# Filter data by each selected type
filtered_data = data[data["Type"] == selected_type]
# Display the editable data table for each selected type using st.dataframe
st.subheader(f"{selected_type} Inventory")
st.dataframe(
filtered_data[columns_to_display],
use_container_width=True
)
# Download the edited data for this type
@st.cache_data
def convert_df_to_csv(df):
return df.to_csv(index=False).encode("utf-8")
csv = convert_df_to_csv(filtered_data)
st.download_button(
label=f"Download {selected_type} Data as CSV",
data=csv,
file_name=f"{selected_type}_data.csv",
mime="text/csv",
)
else:
st.info("Please select at least one bull type to view the data.")
except FileNotFoundError:
st.error(f"The file at {file_path} was not found. Please check the path and try again.")
except Exception as e:
st.error(f"An error occurred: {e}")
|