app

app.py

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+import streamlit as st
+import pandas as pd
+
+# 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}")