I did that but still same error shows
ImportError: This app has encountered an error. The original error message is redacted to prevent data leaks. Full error details have been recorded in the logs (if you’re on Streamlit Cloud, click on ‘Manage app’ in the lower right of your app).
Traceback:
File "/home/appuser/venv/lib/python3.9/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 565, in _run_script
exec(code, module.__dict__)File "/app/mlp-project/Adidas.py", line 30, in <module>
df = pd.read_excel("adidas.xlsx")File "/home/appuser/venv/lib/python3.9/site-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)File "/home/appuser/venv/lib/python3.9/site-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)File "/home/appuser/venv/lib/python3.9/site-packages/pandas/io/excel/_base.py", line 482, in read_excel
io = ExcelFile(io, storage_options=storage_options, engine=engine)File "/home/appuser/venv/lib/python3.9/site-packages/pandas/io/excel/_base.py", line 1695, in __init__
self._reader = self._engines[engine](self._io, storage_options=storage_options)File "/home/appuser/venv/lib/python3.9/site-packages/pandas/io/excel/_openpyxl.py", line 556, in __init__
import_optional_dependency("openpyxl")File "/home/appuser/venv/lib/python3.9/site-packages/pandas/compat/_optional.py", line 144, in import_optional_dependency
raise ImportError(msg)
here is my code
import pandas as pd
import plotly.express as px
import streamlit as st
from PIL import Image
import plotly.graph_objects as go
import matplotlib.pyplot as plt
In[2]:
st.set_page_config(page_title=“Adidas sales Dashboard”,
page_icon=“bar_chart:”,
layout=“wide”)
In:
In[14]:
df = pd.read_excel(“adidas.xlsx”)
#df = pd.read_excel(“C:/Users/Shubham/Desktop/Dash/adidas.xlsx”)
df1=df.sort_values(“Price per Unit”)
In[15]:
df = df.set_index(“Retailer”)
In[12]:
#st.dataframe(df)
In[10]:
#pie_chart=px.pie(df, title=‘Price per Unit’, values=‘Units Sold’, names=‘Product’)
#pie_chart
In[11]:
#fig=px.bar(df, x=“Region”, y=“Units Sold”, color=“City”, title=“dghdg”)
#fig.show()
In[17]:
st.sidebar.header(‘Please Filter Here:’)
Region = st.sidebar.multiselect(
“Select the Region:”,
options=df[“Region”].unique(),
default=df[“Region”].unique())
In:
Product = st.sidebar.multiselect(
“Select the store:”,
options=df[“Product”].unique(),
default=df[“Product”].unique())
Sales_Method = st.sidebar.multiselect(
“Select the store:”,
options=df[“Sales_Method”].unique(),
default=df[“Sales_Method”].unique())
In:
df_selection = df1.query(
“Region ==@Region & Product ==@Product & Sales_Method ==@Sales_Method”)
st.dataframe(df_selection)
In:
st.title(‘Adidas US Sales’)
st.markdown(‘##’)
total_sales= int(df_selection[‘Units_Sold’].sum())
average_Price= round(df_selection[“Price per Unit”].mean(),1)
average_sales = round(df_selection[‘Total_Sales’].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 Price’)
st.subheader(f’US ${average_Price:,}’)
with right_column:
st.subheader(‘Average sales’)
st.subheader(f’US ${average_sales:,}')
st.markdown(“—”)
sales_by_product = df_selection.groupby(by=[‘Product’]).sum()[[‘Total_Sales’]].sort_values(by=[‘Total_Sales’], ascending=False)
fig_Product_sales = px.bar(
sales_by_product,
x=‘Total_Sales’, y = sales_by_product.index,
orientation=‘h’, title=“Sales by Product”,
color_discrete_sequence=[“#0083B8”]* len(sales_by_product),
template=“plotly_white”,)
#fig_Product_sales.update_layout(plot_bgcolor=“rgba(0,0,0,0)”,
xasis=(dict(showgrid=False)))
bar_chart=px.bar(df_selection, x=“Region”, y=“Units_Sold”,
color_discrete_sequence=[“#0083B8”]* len(Region))
left_column, right_column = st.columns(2)
left_column.plotly_chart(bar_chart, use_container_width=True)
right_column.plotly_chart(fig_Product_sales, use_container_width=True)
Pie_chart= px.pie(df_selection, title=‘Price per Unit’, values=‘Units_Sold’, color_discrete_sequence=[“#0083B8”], names=‘Product’)
Regional_sales =px.pie(df_selection, title=‘Sale Region Wise’, values=‘Total_Sales’, color_discrete_sequence=[“#0083B8”], names=‘Region’)
right_column, left_column= st.columns(2)
right_column.plotly_chart(Pie_chart, use_container_width=True)
left_column.plotly_chart(Regional_sales, use_container_width=True)
Histo = px.histogram(df, x=“Price per Unit”, nbins=20)
Store_sales =px.pie(df_selection, title=‘Sale Store Wise’, values=‘Total_Sales’, color_discrete_sequence=[“#0083B8”],names=‘Sales_Method’)
right_column, left_column= st.columns(2)
left_column.plotly_chart( Histo,use_container_width=True)
right_column.plotly_chart( Store_sales,use_container_width=True)
df2=df_selection.copy()
df2[‘Region’]=pd.factorize(df2.Region)[0]
df2[‘State’]=pd.factorize(df2.State)[0]
df2[‘City’]=pd.factorize(df2.City)[0]
df2[‘Product’]=pd.factorize(df2.Product)[0]
df2[‘Retailer’]=pd.factorize(df2.Retailer)[0]
df2.rename(columns = {‘Sales_Method’:‘Method’}, inplace = True)
df2[‘Method’]=pd.factorize(df2.Method)[0]
df2 = df2.drop(‘Retailer ID’,axis=1)
df2 = df2.drop(‘Invoice Date’,axis=1)
df2.head()
corr=df2.corr()
print(corr)
fig = px.imshow(df2.corr())
st.write(fig, use_container_width=True)
hide_st_style = “”"
#mainMenu {Visibility: hidden;}
footer {visibility: hidden;}
header {visibility: hidden;}
“”"
st.markdown(hide_st_style, unsafe_allow_html=True)