└── Complete Sales Dashboard
    ├── src
        ├── pages
        │   ├── Analysis
        │   │   ├── Analysis.md
        │   │   └── Analysis.py
        │   ├── Overview
        │   │   ├── Overview.md
        │   │   └── Overview.py
        │   ├── root.md
        │   ├── __init__.py
        │   ├── root.py
        │   └── Predictions
        │   │   ├── Predictions.md
        │   │   └── Predictions.py
        ├── requirements.txt
        ├── data
        │   ├── supermarkt_sales.xlsx
        │   └── data_augmentation.py
        ├── algorithms
        │   ├── __init__.py
        │   └── algorithms.py
        ├── configuration
        │   ├── __init__.py
        │   ├── config.toml
        │   └── config.py
        └── main.py
    └── src_single_page_taipy
        ├── requirements.txt
        └── main.py


/Complete Sales Dashboard/src/pages/Analysis/Analysis.md:
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1 | # Analysis
2 | 


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/Complete Sales Dashboard/src/pages/Overview/Overview.md:
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1 | # Overview
2 | 


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/Complete Sales Dashboard/src/requirements.txt:
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1 | taipy==3.1.0.dev0
2 | scikit-learn
3 | pmdarima
4 | plotly


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/Complete Sales Dashboard/src_single_page_taipy/requirements.txt:
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1 | taipy==3.1.0.dev0
2 | scikit-learn
3 | pmdarima
4 | plotly


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/Complete Sales Dashboard/src/data/supermarkt_sales.xlsx:
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https://raw.githubusercontent.com/AmmadHasan/Complete-Sales-Dashboard-Using-Taipy/HEAD/Complete Sales Dashboard/src/data/supermarkt_sales.xlsx


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/Complete Sales Dashboard/src/pages/root.md:
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 1 | # Sales **Dashboard**{: .color-primary}
 2 | 
 3 | <|1 1 1|layout|
 4 | <total_sales|
 5 | # **Total**{: .color-primary} sales:
 6 | ### US $ <|{int(data["Total"].sum())}|text|raw|>
 7 | |total_sales>
 8 | 
 9 | <average_rating|
10 | # Average **Rating**{: .color-primary}:
11 | ### <|{round(data["Rating"].mean(), 1)}|text|raw|>
12 | |average_rating>
13 | 
14 | <average_sale|
15 | # Average **Sales**{: .color-primary}:
16 | ### US $ <|{round(data["Total"].mean(), 2)}|text|raw|>
17 | |average_sale>
18 | |>
19 | 
20 | <center>
21 | <|navbar|>
22 | </center>
23 | 
24 | <|Data|expandable|expanded=False|
25 | <|{data}|table|>
26 | |>
27 | 
28 | 


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/Complete Sales Dashboard/src/algorithms/__init__.py:
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 1 | # Copyright 2021-2024 Avaiga Private Limited
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 4 | # the License. You may obtain a copy of the License at
 5 | #
 6 | #        http://www.apache.org/licenses/LICENSE-2.0
 7 | #
 8 | # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 9 | # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
10 | # specific language governing permissions and limitations under the License.
11 | 
12 | from .algorithms import *
13 | 


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/Complete Sales Dashboard/src/configuration/__init__.py:
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 1 | # Copyright 2021-2024 Avaiga Private Limited
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 4 | # the License. You may obtain a copy of the License at
 5 | #
 6 | #        http://www.apache.org/licenses/LICENSE-2.0
 7 | #
 8 | # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 9 | # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
10 | # specific language governing permissions and limitations under the License.
11 | 
12 | from .config import *
13 | 


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/Complete Sales Dashboard/src/pages/__init__.py:
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 1 | # Copyright 2021-2024 Avaiga Private Limited
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 4 | # the License. You may obtain a copy of the License at
 5 | #
 6 | #        http://www.apache.org/licenses/LICENSE-2.0
 7 | #
 8 | # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 9 | # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
10 | # specific language governing permissions and limitations under the License.
11 | 
12 | from .root import root_page
13 | from .Overview.Overview import Overview, data
14 | from .Analysis.Analysis import Analysis
15 | from .Predictions.Predictions import Predictions
16 | 


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/Complete Sales Dashboard/src/pages/root.py:
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 1 | # Copyright 2021-2024 Avaiga Private Limited
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 4 | # the License. You may obtain a copy of the License at
 5 | #
 6 | #        http://www.apache.org/licenses/LICENSE-2.0
 7 | #
 8 | # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 9 | # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
10 | # specific language governing permissions and limitations under the License.
11 | 
12 | """
13 | The root page of the application.
14 | Page content is imported from the root.md file.
15 | 
16 | Please refer to https://docs.taipy.io/en/latest/manuals/gui/pages for more details.
17 | """
18 | 
19 | from taipy.gui import Markdown
20 | 
21 | root_page = Markdown("pages/root.md")
22 | 


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/Complete Sales Dashboard/src/pages/Predictions/Predictions.md:
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 1 | <|layout|columns=2 9|gap=50px|
 2 | <sidebar|sidebar|
 3 | Create and select **scenarios**
 4 | 
 5 | <|{selected_scenario}|scenario_selector|>
 6 | |sidebar>
 7 | 
 8 | <scenario|part|render={selected_scenario}|
 9 | # **Prediction**{: .color-primary} page
10 | 
11 | <|1 1|layout|
12 | <date|
13 | #### Level
14 | 
15 | A parameter to choose how pessimistic or optimistic your predictions will be.
16 | 
17 | <|{selected_level}|slider|on_change=on_change_params|not continuous|min=70|max=150|>
18 | |date>
19 | 
20 | <country|
21 | #### **Holiday**{: .color-primary}
22 | 
23 | Choose if there is an holiday coming
24 | 
25 | <|{selected_holiday}|toggle|label=Holiday|on_change=on_change_params|>
26 | |country>
27 | |>
28 | 
29 | Run your scenario
30 | 
31 | <|{selected_scenario}|scenario|on_submission_change=on_submission_change|not expanded|>
32 | 
33 | ---------------------------------------
34 | 
35 | ## **Predictions**{: .color-primary} and explorer of data nodes
36 | 
37 | <|Data Nodes|expandable|
38 | <|1 5|layout|
39 | <|{selected_data_node}|data_node_selector|> 
40 | 
41 | <|{selected_data_node}|data_node|>
42 | |>
43 | |>
44 | 
45 | |scenario>
46 | |>
47 | 


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/Complete Sales Dashboard/src/main.py:
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 1 | # Copyright 2021-2024 Avaiga Private Limited
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 4 | # the License. You may obtain a copy of the License at
 5 | #
 6 | #        http://www.apache.org/licenses/LICENSE-2.0
 7 | #
 8 | # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 9 | # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
10 | # specific language governing permissions and limitations under the License.
11 | import taipy as tp
12 | from taipy.gui import Gui
13 | 
14 | from configuration import scenario_cfg
15 | 
16 | from taipy import Core
17 | from pages import *
18 | 
19 | 
20 | pages = {
21 |     "/": root_page,
22 |     "Overview": Overview,
23 | 	"Analysis": Analysis,
24 | 	"Predictions": Predictions
25 | }
26 | 
27 | 
28 | if __name__ == "__main__":
29 |     core = Core()
30 |     core.run()
31 |     # #############################################################################
32 |     # PLACEHOLDER: Create and submit your scenario here                           #
33 |     #                                                                             #
34 |     # Example:                                                                    #
35 |     # from configuration import scenario_config                                   #
36 |     # scenario = tp.create_scenario(scenario_config)                              #
37 |     # scenario.submit()                                                           #
38 |     # Comment, remove or replace the previous lines with your own use case        #
39 |     # #############################################################################
40 | 
41 |     gui = Gui(pages=pages)
42 |     gui.run(title="Sales", use_reloader=True, port=2452)
43 |     


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/Complete Sales Dashboard/src/pages/Predictions/Predictions.py:
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 1 | # Copyright 2021-2024 Avaiga Private Limited
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 4 | # the License. You may obtain a copy of the License at
 5 | #
 6 | #        http://www.apache.org/licenses/LICENSE-2.0
 7 | #
 8 | # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 9 | # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
10 | # specific language governing permissions and limitations under the License.
11 | 
12 | """
13 | A page of the application.
14 | Page content is imported from the Predictions.md file.
15 | 
16 | Please refer to https://docs.taipy.io/en/latest/manuals/gui/pages for more details.
17 | """
18 | 
19 | from taipy.gui import Markdown, notify
20 | 
21 | 
22 | selected_data_node = None
23 | selected_scenario = None
24 | selected_holiday = False
25 | selected_level = 100
26 | 
27 | def on_submission_change(state, submitable, details):
28 |     if details['submission_status'] == 'COMPLETED':
29 |         notify(state, "success", "Predictions ready!")
30 |         print("Predictions ready!")
31 |     elif details['submission_status'] == 'FAILED':
32 |         notify(state, "error", "Submission failed!")
33 |         print("Submission failed!")
34 |     else:
35 |         notify(state, "info", "In progress...")
36 |         print("In progress...")
37 | 
38 | 
39 | def on_change_params(state): 
40 |     state.selected_scenario.level.write(state.selected_level/100)
41 |     state.selected_scenario.holiday.write(state.selected_holiday)
42 |     notify(state, "success", "Scenario parameters changed!")
43 | 
44 | def on_change(state, var_name, var_value):
45 |     if var_name == 'selected_scenario' and var_value:
46 |         state.selected_level = state.selected_scenario.level.read()*100
47 |         state.selected_holiday = state.selected_scenario.holiday.read()
48 | 
49 | Predictions = Markdown("pages/Predictions/Predictions.md")


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/Complete Sales Dashboard/src/configuration/config.toml:
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 1 | [TAIPY]
 2 | 
 3 | [DATA_NODE.initial_data]
 4 | storage_type = "csv"
 5 | scope = "GLOBAL:SCOPE"
 6 | path = "data/modified_supermarkt_sales_plus.csv"
 7 | 
 8 | [DATA_NODE.holiday]
 9 | default_data = "False:bool"
10 | 
11 | [DATA_NODE.level]
12 | default_data = "1:int"
13 | 
14 | [DATA_NODE.date]
15 | 
16 | [DATA_NODE.final_data]
17 | 
18 | [DATA_NODE.model_arima]
19 | 
20 | [DATA_NODE.model_xgboost]
21 | 
22 | [DATA_NODE.predictions_arima]
23 | 
24 | [DATA_NODE.predictions_xgboost]
25 | 
26 | [DATA_NODE.result]
27 | 
28 | [TASK.task_preprocess_data]
29 | function = "algorithms.algorithms.preprocess:function"
30 | inputs = [ "initial_data:SECTION", "holiday:SECTION", "level:SECTION",]
31 | outputs = [ "final_data:SECTION", "date:SECTION",]
32 | skippable = "False:bool"
33 | 
34 | [TASK.task_train]
35 | function = "algorithms.algorithms.train_arima:function"
36 | inputs = [ "final_data:SECTION",]
37 | outputs = [ "model_arima:SECTION",]
38 | skippable = "False:bool"
39 | 
40 | [TASK.task_forecast]
41 | function = "algorithms.algorithms.forecast:function"
42 | inputs = [ "model_arima:SECTION",]
43 | outputs = [ "predictions_arima:SECTION",]
44 | skippable = "False:bool"
45 | 
46 | [TASK.task_train_xgboost]
47 | function = "algorithms.algorithms.train_xgboost:function"
48 | inputs = [ "final_data:SECTION",]
49 | outputs = [ "model_xgboost:SECTION",]
50 | skippable = "False:bool"
51 | 
52 | [TASK.task_forecast_xgboost]
53 | function = "algorithms.algorithms.forecast_xgboost:function"
54 | inputs = [ "model_xgboost:SECTION", "date:SECTION",]
55 | outputs = [ "predictions_xgboost:SECTION",]
56 | skippable = "False:bool"
57 | 
58 | [TASK.task_result]
59 | function = "algorithms.algorithms.concat:function"
60 | inputs = [ "final_data:SECTION", "predictions_arima:SECTION", "predictions_xgboost:SECTION",]
61 | outputs = [ "result:SECTION",]
62 | skippable = "False:bool"
63 | 
64 | [SCENARIO.scenario]
65 | tasks = [ "task_preprocess_data:SECTION", "task_train:SECTION", "task_forecast:SECTION", "task_train_xgboost:SECTION", "task_forecast_xgboost:SECTION", "task_result:SECTION",]
66 | additional_data_nodes = []
67 | 
68 | [SCENARIO.scenario.comparators]
69 | 
70 | [SCENARIO.scenario.sequences]
71 | 


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/Complete Sales Dashboard/src/pages/Overview/Overview.py:
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 1 | import pandas as pd
 2 | import plotly.express as px
 3 | import taipy.gui.builder as tgb
 4 | 
 5 | # Load the dataset
 6 | data = pd.read_csv('data/modified_supermarkt_sales_plus.csv')
 7 | 
 8 | def create_pie_figure(data, group_by):
 9 |     grouped_data = data.groupby(group_by)['Total'].sum().reset_index()
10 |     grouped_data['Total'] = grouped_data['Total'].round(2)
11 |     fig = px.pie(grouped_data, names=group_by, values='Total', title=f"Sales Performance by {group_by}", hole=0.3)
12 |     return fig
13 | 
14 | def create_bar_figure(data, group_by):
15 |     sales_over_time = data.groupby(group_by)['Total'].sum().reset_index()
16 |     fig = px.bar(sales_over_time, x=group_by, y='Total', title='Sales Trends Over Time', color='Total')
17 |     return fig
18 | 
19 | import os
20 | 
21 | def create_sales_by_city_map(data):
22 |     mapbox_access_token = os.environ.get('MAPBOX_ACCESS_TOKEN')
23 |     px.set_mapbox_access_token(mapbox_access_token)
24 |     city_sales = data.groupby('City').agg({'Total': 'sum', 'Latitude': 'mean', 'Longitude': 'mean'}).reset_index()
25 |     fig = px.scatter_mapbox(city_sales, lat="Latitude", lon="Longitude", size="Total", color="Total", text="City",
26 |                             zoom=5, center={"lat": 18.7, "lon": 98.9}, mapbox_style="dark", title='Total Sales by City', size_max=50)
27 |     fig.update_layout(title={'text': "Total Sales by City", 'y': 0.9, 'x': 0.5, 'xanchor': 'center', 'yanchor': 'top'},
28 |                       legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
29 |                       margin={"r": 0, "t": 0, "l": 0, "b": 0})
30 |     return fig
31 | 
32 | fig_product_line = create_pie_figure(data, 'Product_line')
33 | fig_city = create_pie_figure(data, 'City')
34 | fig_customer_type = create_pie_figure(data, 'Customer_type')
35 | 
36 | with tgb.Page() as Overview:
37 |     # Sales by City Map
38 |     tgb.chart(figure="{create_sales_by_city_map(data)}", height="600px")
39 |     
40 |     with tgb.layout(columns="1 1 1"):
41 |         tgb.chart(figure="{fig_product_line}")
42 |         tgb.chart(figure="{fig_city}")
43 |         tgb.chart(figure="{fig_customer_type}")
44 | 
45 |     tgb.chart(figure="{create_bar_figure(data, 'Time')}")
46 |         
47 |     tgb.chart(figure="{create_bar_figure(data, 'Date')}")


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/Complete Sales Dashboard/src/data/data_augmentation.py:
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 1 | import numpy as np
 2 | import pandas as pd 
 3 | import string
 4 | import random
 5 | 
 6 | 
 7 | # Seed for reproducibility
 8 | np.random.seed(42)
 9 | 
10 | data = pd.read_csv('data/supermarkt_sales_clean.csv')
11 | 
12 | def generate_invoice_id(n):
13 |     """Generate n unique Invoice IDs in the format 'XXX-XX-XXXX'."""
14 |     invoice_ids = set()
15 |     while len(invoice_ids) < n:
16 |         # Generate parts of the Invoice ID
17 |         part1 = ''.join(random.choices(string.ascii_uppercase, k=3))
18 |         part2 = ''.join(random.choices(string.digits, k=2))
19 |         part3 = ''.join(random.choices(string.ascii_uppercase + string.digits, k=4))
20 |         
21 |         # Combine parts into one ID
22 |         invoice_id = f"{part1}-{part2}-{part3}"
23 |         
24 |         # Add to the set of Invoice IDs
25 |         invoice_ids.add(invoice_id)
26 |     
27 |     return list(invoice_ids)
28 | 
29 | num_entries = 5_000
30 | new_branches = np.random.choice(['A', 'B', 'C', 'D', 'E', 'F'], size=num_entries)
31 | new_invoice = generate_invoice_id(num_entries)
32 | new_products = np.random.choice(list(data['Product line'].unique()), size=num_entries)
33 | new_customer_types = np.random.choice(['Member', 'Normal'], size=num_entries)
34 | new_genders = np.random.choice(['Male', 'Female'], size=num_entries)
35 | new_unit_prices = np.random.uniform(10, 100, size=num_entries)
36 | new_quantities = np.random.randint(1, 10, size=num_entries)
37 | new_tax = new_unit_prices * new_quantities * 0.05
38 | new_totals = new_unit_prices * new_quantities + new_tax
39 | new_cogs = new_unit_prices * new_quantities
40 | new_gross_margin_percentage = np.full(shape=num_entries, fill_value=4.761905)
41 | new_gross_income = new_tax
42 | new_ratings = np.random.uniform(1, 10, size=num_entries)
43 | new_dates = np.random.choice(pd.date_range(start='2021-01-01', end='2021-12-31'), size=num_entries)
44 | new_times = [f"{np.random.randint(10, 19)}:{np.random.randint(0, 59):02d}:00" for _ in range(num_entries)]
45 | new_payments = np.random.choice(['Cash', 'Ewallet', 'Credit card'], size=num_entries)
46 | 
47 | 
48 | cities_latitudes = {
49 |     'Bangkok': (13.736717, 100.523186),
50 |     'Chiang Mai': (18.796143, 98.979263),
51 |     'Vientiane': (17.974855, 102.630867),
52 |     'Luang Prabang': (19.889271, 102.133453),
53 | }
54 | 
55 | # Select a city for each new entry and retrieve its latitude
56 | new_cities = np.random.choice(list(cities_latitudes.keys()), size=num_entries)
57 | new_latitudes = np.array([cities_latitudes[city][0] for city in new_cities])
58 | new_longitudes = np.array([cities_latitudes[city][1] for city in new_cities])
59 | 
60 | # Create DataFrame for additional data
61 | additional_data = pd.DataFrame({
62 |     'Invoice ID': new_invoice,
63 |     'Branch': new_branches,
64 |     'City': new_cities,
65 |     'Customer_type': new_customer_types,
66 |     'Gender': new_genders,
67 |     'Product_line': new_products,
68 |     'Unit price': new_unit_prices,
69 |     'Quantity': new_quantities,
70 |     'Tax 5%': new_tax,
71 |     'Total': new_totals,
72 |     'Date': new_dates,
73 |     'Time': new_times,
74 |     'Payment': new_payments,
75 |     'cogs': new_cogs,
76 |     'gross_margin_percentage': new_gross_margin_percentage,
77 |     'gross_income': new_gross_income,
78 |     'Rating': new_ratings,
79 |     'Latitude': new_latitudes,
80 |     'Longitude': new_longitudes
81 | })
82 | 
83 | # Concatenate the additional data with the existing dataset
84 | augmented_data = pd.concat([data, additional_data]).reset_index(drop=True)
85 | 
86 | 
87 | augmented_data.to_csv('data/modified_supermarkt_sales_plus.csv')


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/Complete Sales Dashboard/src/pages/Analysis/Analysis.py:
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 1 | # Copyright 2021-2024 Avaiga Private Limited
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 4 | # the License. You may obtain a copy of the License at
 5 | #
 6 | #        http://www.apache.org/licenses/LICENSE-2.0
 7 | #
 8 | # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 9 | # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
10 | # specific language governing permissions and limitations under the License.
11 | 
12 | """
13 | A page of the application.
14 | Page content is imported from the Analysis.md file.
15 | 
16 | Please refer to https://docs.taipy.io/en/latest/manuals/gui/pages for more details.
17 | """
18 | from taipy.gui import Markdown
19 | import pandas as pd
20 | import plotly.express as px
21 | import taipy.gui.builder as tgb
22 | 
23 | # Assuming data is loaded from 'data/modified_supermarkt_sales_plus.csv'
24 | data = pd.read_csv('data/modified_supermarkt_sales_plus.csv')
25 | 
26 | data['Date'] = pd.to_datetime(data['Date'])
27 | data['Month_Year'] = data['Date'].dt.to_period('M').dt.to_timestamp()
28 |     
29 | 
30 | def create_perc_fig(df, group_column):
31 |     # Group, sum, and convert to percentage
32 |     df = df.groupby(['Month_Year', group_column])['Total'].sum().unstack(fill_value=0)
33 |     df = df.div(df.sum(axis=1), axis=0).reset_index().melt(id_vars='Month_Year', var_name=group_column, value_name='Percentage')
34 |     df['Percentage'] = (df.loc[:, 'Percentage'].round(3) * 100)
35 |     
36 |     # Create and return the plot
37 |     fig = px.bar(df, x='Month_Year', y='Percentage', color=group_column, title=f"Evolution of Sales by {group_column} over Time", labels={'Percentage': '% of Total'}, text_auto=True)
38 |     return fig
39 | 
40 | fig_product_line = create_perc_fig(data, 'Product_line')
41 | fig_city = create_perc_fig(data, 'City')
42 | fig_gender = create_perc_fig(data, 'Gender')
43 | fig_customer_type = create_perc_fig(data, 'Customer_type')
44 | 
45 | import time
46 | 
47 | def on_change(state, var_name, var_value):
48 |     if var_name in ['city', 'customer_type', 'gender']:
49 |         data = state.data.loc[
50 |             state.data["City"].isin(state.city)
51 |             & state.data["Customer_type"].isin(state.customer_type)
52 |             & state.data["Gender"].isin(state.gender), :
53 |         ]
54 | 
55 |         state.fig_product_line = create_perc_fig(data, 'Product_line')
56 |         state.fig_city = create_perc_fig(data, 'City')
57 |         state.fig_gender = create_perc_fig(data, 'Gender')
58 |         state.fig_customer_type = create_perc_fig(data, 'Customer_type')
59 | 
60 | 
61 | customer_type = ["Normal", "Member"]
62 | gender = ["Male", "Female"]
63 | city = ["Bangkok", "Chiang Mai", "Vientiane", "Luang Prabang"]
64 | 
65 | with tgb.Page() as Analysis:   
66 |     with tgb.layout(columns="1 1 1"):
67 |         tgb.selector(value="{customer_type}", lov=customer_type, multiple=True, dropdown=True, class_name="fullwidth", label="Customer Type")
68 |         tgb.selector(value="{gender}", lov=gender, multiple=True, dropdown=True, class_name="fullwidth", label="Gender")
69 |         tgb.selector(value="{city}", lov=city,  multiple=True, dropdown=True, class_name="fullwidth", label="City")
70 | 
71 | 
72 |     with tgb.layout(columns="1 1"):
73 |         tgb.chart(figure="{fig_customer_type}")
74 |         tgb.chart(figure="{fig_city}")
75 |         tgb.chart(figure="{fig_gender}")
76 |         tgb.chart(figure="{fig_product_line}")
77 |         
78 | 
79 | 


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/Complete Sales Dashboard/src/algorithms/algorithms.py:
--------------------------------------------------------------------------------
 1 | # Copyright 2021-2024 Avaiga Private Limited
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 4 | # the License. You may obtain a copy of the License at
 5 | #
 6 | #        http://www.apache.org/licenses/LICENSE-2.0
 7 | #
 8 | # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 9 | # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
10 | # specific language governing permissions and limitations under the License.
11 | 
12 | """
13 | This file is designed to contain the various Python functions used to configure tasks.
14 | 
15 | The functions will be imported by the __init__.py file in this folder.
16 | """
17 | 
18 | import pandas as pd
19 | from sklearn.ensemble import GradientBoostingRegressor
20 | import datetime as dt
21 | import numpy as np
22 | from pmdarima import auto_arima
23 | 
24 | 
25 | def add_features(data):
26 |     dates = pd.to_datetime(data["Date"])
27 |     data["Months"] = (dates.dt.month - 6)/12
28 |     data["Days"] = (dates.dt.isocalendar().day - 15)/30
29 |     data["Week"] = (dates.dt.isocalendar().week - 26)/52
30 |     data["Day of week"] = (dates.dt.dayofweek - 3.5)/7
31 |     # Number of days after 30 December 2020
32 |     data["Index"] = (dates - dt.datetime(2020, 12, 30)).dt.days
33 |     return data
34 | 
35 | def preprocess(initial_data, holiday, level):
36 |     data = initial_data.groupby(['Date'])\
37 |                        .sum()\
38 |                        .dropna()\
39 |                        .reset_index()
40 | 
41 |     data['Date'] = pd.to_datetime(data['Date'])
42 |     final_data = data[['Date','Total']]
43 |     final_data = add_features(final_data)
44 | 
45 |     final_data['Total'] = final_data['Total']*level
46 |     if holiday:
47 |         final_data['Total'] *= 0.8
48 | 
49 |     date = final_data['Date'].max()
50 |     return final_data, date
51 | 
52 | def train_arima(train_data):
53 |     model = auto_arima(train_data['Total'],
54 |                        start_p=1, start_q=1,
55 |                        max_p=5, max_q=5,
56 |                        start_P=0, seasonal=False,
57 |                        d=1, D=1, trace=True,
58 |                        error_action='ignore',  
59 |                        suppress_warnings=True)
60 |     model.fit(train_data['Total'])
61 |     return model
62 | 
63 | def forecast(model):
64 |     predictions = model.predict(n_periods=60)
65 |     return np.array(predictions)
66 | 
67 | def train_xgboost(train_data):    
68 |     y = train_data['Total']
69 |     X = train_data.drop(['Total','Date'], axis=1)
70 |     
71 |     model = GradientBoostingRegressor()
72 |     model.fit(X,y)
73 |     return model
74 | 
75 | def forecast_xgboost(model, date):
76 |     dates = pd.to_datetime([date + dt.timedelta(days=i)
77 |                             for i in range(60)])
78 |     X = add_features(pd.DataFrame({"Date":dates}))
79 |     X.drop('Date', axis=1, inplace=True)
80 |     predictions = model.predict(X)
81 |     return predictions
82 | 
83 | 
84 | def concat(final_data, predictions_arima, predictions_xgboost):
85 |     date = final_data['Date'].max()
86 | 
87 |     def  _convert_predictions(final_data, predictions, date, label='Predictions'):
88 |         dates = pd.to_datetime([date + dt.timedelta(days=i)
89 |                                 for i in range(len(predictions))])
90 |         final_data['Date'] = pd.to_datetime(final_data['Date'])
91 |         final_data = final_data[['Date','Total']]
92 |         predictions = pd.concat([pd.Series(dates, name="Date"),
93 |                                  pd.Series(predictions, name=label)], axis=1)
94 |         return final_data.merge(predictions, on="Date", how="outer")
95 | 
96 |     result_arima = _convert_predictions(final_data, predictions_arima, date, label='ARIMA')
97 |     result_xgboost = _convert_predictions(final_data, predictions_xgboost, date, label='Xgboost')
98 |     return result_arima.merge(result_xgboost, on=["Date", 'Total'], how="outer").sort_values(by='Date')
99 | 


--------------------------------------------------------------------------------
/Complete Sales Dashboard/src/configuration/config.py:
--------------------------------------------------------------------------------
 1 | # Copyright 2021-2024 Avaiga Private Limited
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 4 | # the License. You may obtain a copy of the License at
 5 | #
 6 | #        http://www.apache.org/licenses/LICENSE-2.0
 7 | #
 8 | # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
 9 | # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
10 | # specific language governing permissions and limitations under the License.
11 | 
12 | """
13 | Contain the application's configuration including the scenario configurations.
14 | 
15 | The configuration is run by the Core service.
16 | """
17 | 
18 | from algorithms import *
19 | 
20 | from taipy import Config
21 | 
22 | from taipy.config import Config, Scope
23 | import datetime as dt
24 | 
25 | 
26 | #Config.configure_job_executions(mode="standalone", nb_of_workers=2)
27 | 
28 | path_to_data = "data/modified_supermarkt_sales_plus.csv"
29 | 
30 | initial_data_cfg = Config.configure_data_node(id="initial_data",
31 |                                               storage_type="csv",
32 |                                               path=path_to_data,
33 |                                               scope=Scope.GLOBAL)
34 | 
35 | holiday_cfg = Config.configure_data_node(id="holiday", default_data=False)
36 | level_cfg = Config.configure_data_node(id="level", default_data=1)
37 | date_cfg = Config.configure_data_node(id="date")
38 | 
39 | final_data_cfg =  Config.configure_data_node(id="final_data")
40 | 
41 | 
42 | model_arima_cfg = Config.configure_data_node(id="model_arima")
43 | model_xgboost_cfg = Config.configure_data_node(id="model_xgboost")
44 | 
45 | predictions_arima_cfg = Config.configure_data_node(id="predictions_arima")
46 | predictions_xgboost_cfg = Config.configure_data_node(id="predictions_xgboost")
47 | 
48 | result_cfg = Config.configure_data_node(id="result")
49 | 
50 | 
51 | task_preprocess_cfg = Config.configure_task(id="task_preprocess_data",
52 |                                            function=preprocess,
53 |                                            input=[initial_data_cfg, holiday_cfg, level_cfg],
54 |                                            output=[final_data_cfg, date_cfg])
55 | 
56 | 
57 | task_train_arima_cfg = Config.configure_task(id="task_train",
58 |                                       function=train_arima,
59 |                                       input=final_data_cfg,
60 |                                       output=model_arima_cfg) 
61 | 
62 | task_forecast_arima_cfg = Config.configure_task(id="task_forecast",
63 |                                       function=forecast,
64 |                                       input=model_arima_cfg,
65 |                                       output=predictions_arima_cfg)
66 | 
67 | 
68 | task_train_xgboost_cfg = Config.configure_task(id="task_train_xgboost",
69 |                                       function=train_xgboost,
70 |                                       input=final_data_cfg,
71 |                                       output=model_xgboost_cfg)
72 | 
73 | task_forecast_xgboost_cfg = Config.configure_task(id="task_forecast_xgboost",
74 |                                       function=forecast_xgboost,
75 |                                       input=[model_xgboost_cfg, date_cfg],
76 |                                       output=predictions_xgboost_cfg)
77 | 
78 | task_result_cfg = Config.configure_task(id="task_result",
79 |                                       function=concat,
80 |                                       input=[final_data_cfg, 
81 |                                              predictions_arima_cfg, 
82 |                                              predictions_xgboost_cfg],
83 |                                       output=result_cfg)
84 | 
85 | 
86 | scenario_cfg = Config.configure_scenario(id='scenario', task_configs=[task_preprocess_cfg,
87 |                                                                       task_train_arima_cfg,
88 |                                                                       task_forecast_arima_cfg,
89 |                                                                       task_train_xgboost_cfg,
90 |                                                                       task_forecast_xgboost_cfg,
91 |                                                                       task_result_cfg])
92 | 
93 | Config.export('configuration/config.toml')


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/Complete Sales Dashboard/src_single_page_taipy/main.py:
--------------------------------------------------------------------------------
  1 | from taipy.gui import Gui
  2 | import pandas as pd
  3 | import plotly.express as px
  4 | import taipy.gui.builder as tgb
  5 | 
  6 | 
  7 | data = pd.read_csv('data/modified_supermarkt_sales_plus.csv')
  8 | 
  9 | 
 10 | def create_pie_figure(data, group_by: str):
 11 |     grouped_data = data.groupby(group_by)['Total'].sum().reset_index()
 12 |     grouped_data['Total'] = grouped_data['Total'].round(2)
 13 |     fig = px.pie(grouped_data, names=group_by, values='Total', title=f"Sales Performance by {group_by}", hole=0.3)
 14 |     return fig
 15 | 
 16 | def create_bar_figure(data, group_by: str):
 17 |     sales_over_time = data.groupby(group_by)['Total'].sum().reset_index()
 18 |     fig = px.bar(sales_over_time, x=group_by, y='Total', title='Sales Trends Over Time', color='Total')
 19 |     return fig
 20 | 
 21 | def create_sales_by_city_map(data):
 22 |     # mapbox_access_token = ...
 23 |     # px.set_mapbox_access_token(mapbox_access_token)
 24 |     city_sales = data.groupby('City').agg({'Total': 'sum', 'Latitude': 'mean', 'Longitude': 'mean'}).reset_index()
 25 |     fig = px.scatter_mapbox(city_sales, lat="Latitude", lon="Longitude", size="Total", color="Total", text="City",
 26 |                             zoom=5, center={"lat": 18.7, "lon": 98.9}, mapbox_style="dark", title='Total Sales by City', size_max=50)
 27 |     fig.update_layout(title={'text': "Total Sales by City", 'y': 0.9, 'x': 0.5, 'xanchor': 'center', 'yanchor': 'top'},
 28 |                       legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
 29 |                       margin={"r": 0, "t": 0, "l": 0, "b": 0})
 30 |     return fig
 31 | 
 32 | 
 33 | def create_perc_fig(df, group_column):
 34 |     # Group, sum, and convert to percentage
 35 |     df = df.groupby(['Month_Year', group_column])['Total'].sum().unstack(fill_value=0)
 36 |     df = df.div(df.sum(axis=1), axis=0).reset_index().melt(id_vars='Month_Year', var_name=group_column, value_name='Percentage')
 37 |     df['Percentage'] = (df.loc[:, 'Percentage'].round(3) * 100)
 38 |     
 39 |     # Create and return the plot
 40 |     fig = px.bar(df, x='Month_Year', y='Percentage', color=group_column, title=f"Evolution of Sales by {group_column} over Time", labels={'Percentage': '% of Total'}, text_auto=True)
 41 |     return fig
 42 | 
 43 | 
 44 | fig_map = create_sales_by_city_map(data)
 45 | 
 46 | fig_product_line = create_pie_figure(data, 'Product_line')
 47 | fig_city = create_pie_figure(data, 'City')
 48 | fig_customer_type = create_pie_figure(data, 'Customer_type')
 49 | 
 50 | fig_time = create_bar_figure(data, 'Time')
 51 | fig_date = create_bar_figure(data, 'Date')
 52 | 
 53 | 
 54 | 
 55 | 
 56 | city = ["Bangkok", "Chiang Mai", "Vientiane", "Luang Prabang", "Yangon", "Naypyitaw"]
 57 | 
 58 | filtered_data = data.loc[
 59 |     data["City"].isin(city)
 60 | ]
 61 | 
 62 | fig_product_line_perc = create_perc_fig(filtered_data, 'Product_line')
 63 | fig_city_perc = create_perc_fig(filtered_data, 'City')
 64 | fig_gender_perc = create_perc_fig(filtered_data, 'Gender')
 65 | fig_customer_type_perc = create_perc_fig(filtered_data, 'Customer_type')
 66 | 
 67 | 
 68 | def on_selector(state):
 69 |     filtered_data = state.data.loc[
 70 |         state.data["City"].isin(state.city)
 71 |     ]
 72 | 
 73 |     state.fig_product_line_perc = create_perc_fig(filtered_data, 'Product_line')
 74 |     state.fig_city_perc = create_perc_fig(filtered_data, 'City')
 75 |     state.fig_gender_perc = create_perc_fig(filtered_data, 'Gender')
 76 |     state.fig_customer_type_perc = create_perc_fig(filtered_data, 'Customer_type')
 77 | 
 78 | 
 79 | 
 80 | 
 81 | with tgb.Page() as page:
 82 |     tgb.text("Sales Insights", class_name="h1")
 83 | 
 84 |     with tgb.layout("1 1 1"):
 85 |         with tgb.part():
 86 |             tgb.text("Total Sales", class_name="h2")
 87 |             tgb.text("{int(data['Total'].sum())}", class_name="h3")
 88 | 
 89 |         with tgb.part():
 90 |             tgb.text("Average Sales", class_name="h2")
 91 |             tgb.text("{int(data['Total'].mean())}", class_name="h3")
 92 | 
 93 |         with tgb.part():
 94 |             tgb.text("Mean Rating", class_name="h2")
 95 |             tgb.text("{int(data['Rating'].mean())}", class_name="h3")
 96 | 
 97 |     tgb.chart(figure="{fig_map}")
 98 | 
 99 |     with tgb.layout("1 1 1"):
100 |         tgb.chart(figure="{fig_product_line}")
101 |         tgb.chart(figure="{fig_city}")
102 |         tgb.chart(figure="{fig_customer_type}")
103 |     
104 |     tgb.chart(figure="{fig_time}")
105 |     tgb.chart(figure="{fig_date}")
106 | 
107 |     tgb.text("Analysis", class_name="h2")
108 | 
109 |     tgb.selector(value="{city}", lov=["Bangkok", "Chiang Mai", "Vientiane", "Luang Prabang", "Yangon", "Naypyitaw"],
110 |                  dropdown=True,
111 |                  multiple=True,
112 |                  label="Select cities",
113 |                  class_name="fullwidth",
114 |                  on_change=on_selector)
115 | 
116 |     with tgb.layout("1 1"):
117 |         tgb.chart(figure="{fig_product_line_perc}")
118 |         tgb.chart(figure="{fig_city_perc}")
119 |         tgb.chart(figure="{fig_gender_perc}")
120 |         tgb.chart(figure="{fig_customer_type_perc}")
121 | 
122 |     tgb.table("{data}")
123 | 
124 | 
125 | if __name__ == "__main__":
126 |     gui = Gui(page)
127 |     gui.run(title="Sales", port=2452)
128 |     


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