data

README.md

@@ -8,4 +8,6 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+poetry run streamlit run app.py

app.py

@@ -1,130 +1,32 @@
-# @Email:  [email protected]
-# @Website:  https://pythonandvba.com
-# @YouTube:  https://youtube.com/c/CodingIsFun
-# @Project:  Sales Dashboard w/ Streamlit
+import streamlit as st
+import pandas as pd
 
 
+# Function to highlight the given value
+def highlight_value(val, highlight):
+    color = 'yellow' if val == highlight else ''
+    return f'background-color: {color}'
 
-import pandas as pd  # pip install pandas openpyxl
-import plotly.express as px  # pip install plotly-express
-import streamlit as st  # pip install streamlit
+# Value to be highlighted
+highlight = "ongoing"
 
-# emojis: https://www.webfx.com/tools/emoji-cheat-sheet/
-st.set_page_config(page_title="Sales Dashboard", page_icon=":bar_chart:", layout="wide")
+df = pd.read_csv('board.csv')
 
-# ---- READ EXCEL ----
-@st.cache_data
-def get_data_from_excel():
-    df = pd.read_excel(
-        io="supermarkt_sales.xlsx",
-        engine="openpyxl",
-        sheet_name="Sales",
-        skiprows=3,
-        usecols="B:R",
-        nrows=1000,
-    )
-    # Add 'hour' column to dataframe
-    df["hour"] = pd.to_datetime(df["Time"], format="%H:%M:%S").dt.hour
-    return df
+# Apply the highlighting
+df_styled = df.style.applymap(lambda x: highlight_value(x, highlight))
 
-df = get_data_from_excel()
+# Convert the styled DataFrame to HTML
+df_html = df_styled.set_table_attributes('class="dataframe"')
 
-# ---- SIDEBAR ----
-st.sidebar.header("Please Filter Here:")
-city = st.sidebar.multiselect(
-    "Select the City:",
-    options=df["City"].unique(),
-    default=df["City"].unique()
-)
+# Add CSS for setting cell width
+css = """
+<style>
+.dataframe td {
+    max-width: 100px;
+    word-wrap: break-word;
+}
+</style>
+"""
 
-customer_type = st.sidebar.multiselect(
-    "Select the Customer Type:",
-    options=df["Customer_type"].unique(),
-    default=df["Customer_type"].unique(),
-)
-
-gender = st.sidebar.multiselect(
-    "Select the Gender:",
-    options=df["Gender"].unique(),
-    default=df["Gender"].unique()
-)
-
-df_selection = df.query(
-    "City == @city & Customer_type ==@customer_type & Gender == @gender"
-)
-
-# Check if the dataframe is empty:
-if df_selection.empty:
-    st.warning("No data available based on the current filter settings!")
-    st.stop() # This will halt the app from further execution.
-
-# ---- MAINPAGE ----
-st.title(":bar_chart: Sales Dashboard")
-st.markdown("##")
-
-# TOP KPI's
-total_sales = int(df_selection["Total"].sum())
-average_rating = round(df_selection["Rating"].mean(), 1)
-star_rating = ":star:" * int(round(average_rating, 0))
-average_sale_by_transaction = round(df_selection["Total"].mean(), 2)
-
-left_column, middle_column, right_column = st.columns(3)
-with left_column:
-    st.subheader("Total Sales:")
-    st.subheader(f"US $ {total_sales:,}")
-with middle_column:
-    st.subheader("Average Rating:")
-    st.subheader(f"{average_rating} {star_rating}")
-with right_column:
-    st.subheader("Average Sales Per Transaction:")
-    st.subheader(f"US $ {average_sale_by_transaction}")
-
-st.markdown("""---""")
-
-# SALES BY PRODUCT LINE [BAR CHART]
-sales_by_product_line = df_selection.groupby(by=["Product line"])[["Total"]].sum().sort_values(by="Total")
-fig_product_sales = px.bar(
-    sales_by_product_line,
-    x="Total",
-    y=sales_by_product_line.index,
-    orientation="h",
-    title="<b>Sales by Product Line</b>",
-    color_discrete_sequence=["#0083B8"] * len(sales_by_product_line),
-    template="plotly_white",
-)
-fig_product_sales.update_layout(
-    plot_bgcolor="rgba(0,0,0,0)",
-    xaxis=(dict(showgrid=False))
-)
-
-# SALES BY HOUR [BAR CHART]
-sales_by_hour = df_selection.groupby(by=["hour"])[["Total"]].sum()
-fig_hourly_sales = px.bar(
-    sales_by_hour,
-    x=sales_by_hour.index,
-    y="Total",
-    title="<b>Sales by hour</b>",
-    color_discrete_sequence=["#0083B8"] * len(sales_by_hour),
-    template="plotly_white",
-)
-fig_hourly_sales.update_layout(
-    xaxis=dict(tickmode="linear"),
-    plot_bgcolor="rgba(0,0,0,0)",
-    yaxis=(dict(showgrid=False)),
-)
-
-
-left_column, right_column = st.columns(2)
-left_column.plotly_chart(fig_hourly_sales, use_container_width=True)
-right_column.plotly_chart(fig_product_sales, use_container_width=True)
-
-
-# ---- HIDE STREAMLIT STYLE ----
-hide_st_style = """
-            <style>
-            #MainMenu {visibility: hidden;}
-            footer {visibility: hidden;}
-            header {visibility: hidden;}
-            </style>
-            """
-st.markdown(hide_st_style, unsafe_allow_html=True)
+# Display the styled DataFrame with custom CSS in Streamlit
+st.write(df_html, unsafe_allow_html=True)

board.csv

@@ -0,0 +1,12 @@
+City,parcel,street,building,buildablelot,due,tsec,status
+San Jose, 247614, 41241,324217,0,0,2010432,ongoing
+San Francisco, 0, 0,0,0,0,0,tostart
+Los Angeles, 0, 0,0,0,0,0,tostart
+San Diego, 0, 0,0,0,0,0,tostart
+Sacramento, 0, 0,0,0,0,0,tostart
+Fresno, 0, 0,0,0,0,0,tostart
+Oakland, 0, 0,0,0,0,0,tostart
+Long Beach, 0, 0,0,0,0,0,tostart
+Anaheim, 0, 0,0,0,0,0,tostart
+Bakersfield, 0, 0,0,0,0,0,tostart
+Riverside, 0, 0,0,0,0,0,tostart

pyproject.toml

@@ -0,0 +1,15 @@
+[tool.poetry]
+name = "dashboard"
+version = "0.1.0"
+description = ""
+authors = ["Jun Xiong"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.12"
+pandas = "^2.2.2"
+streamlit = "^1.36.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

requirements.txt

@@ -1,4 +1,2 @@
-openpyxl
-pandas==2.0.1
-plotly==5.13.1
-streamlit==1.25.0
+pandas==1.1.3
+streamlit==0.72.0