Connect basiq api data from backend endpoints to front end. Update dashboard and breakdown pages

frontend/classes/api.py

@@ -64,5 +64,43 @@ def refresh_token_auth(self, email: str, tokens: dict) -> dict:
         except Exception as e:
             raise Exception(f"There was an issue getting a refresh token")
 
+    def basiq_api_transaction_data(self) -> dict:
+        try:
 
-
+            url = self.base_url+"/basiq/transactions"
+
+            payload = {}
+            headers = {
+                "accepts": "application/json",
+                "Authorization": "eyJraWQiOiJlcXhraW1yN21mRkwySmpjMmx1ZCtuT0JlNk5DVU00aHNMaEtta1p6MjVRPSIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiJlN2NlM2ZhYy0zOGNjLTRiNjAtOTc4MC1jMDBiMTZjYmI3OTYiLCJpc3MiOiJodHRwczpcL1wvY29nbml0by1pZHAuYXAtc291dGhlYXN0LTIuYW1hem9uYXdzLmNvbVwvYXAtc291dGhlYXN0LTJfNzNlY0FOdWRNIiwiY29nbml0bzp1c2VybmFtZSI6ImU3Y2UzZmFjLTM4Y2MtNGI2MC05NzgwLWMwMGIxNmNiYjc5NiIsIm9yaWdpbl9qdGkiOiI2ODFmNjBmNy01YjIzLTQzYzItYjA0OC03OWFkNmQyYTgyNzUiLCJhdWQiOiI1N2kzOW9xcG1wdGIxZmxxZmoxdXUzaWJwYSIsImV2ZW50X2lkIjoiYTJlNTc0OTAtMDQzZS00ZGI4LTk0MzktNGI0ZGNlZGJlMWI1IiwidG9rZW5fdXNlIjoiaWQiLCJhdXRoX3RpbWUiOjE2ODQzNTkzNTYsIm5hbWUiOiJkYW5ubm5vcnIiLCJleHAiOjE2ODQzNjI5NTYsImlhdCI6MTY4NDM1OTM1NiwianRpIjoiNGEwM2JlNmItNTM5NC00MWRlLWFiMGEtMTQwOWNhMzk0MzZlIiwiZW1haWwiOiJkYW5AZW1haWwuY29tbSJ9.ZVlGgqM_Bxz1IdF65eN097rh0KBUeVxkw2zm806uwqpAeiGoxGXjJYVLx3kIwcSk50dhqNCu5_MZqt9lIfHNobFGs7Xjz-g2GVEPDttMELqllmCGier9m8QjhAmE3VJ3TwXbR1-oN1IxPkJe4slxKiFKN4OMqdZpAYETsfX0IJc2WQFrC4A00LYmgTLD4Uk0lxToGStsqK3UOp4ZZhmzRUfbKYgOcfNIGvpuUqWmHFjqtxgSG2g5s7Wa1QCloLmIadL3GEOqw8ulTS_BoPNYusy2VW63VSJX2VjO3DPYY41ViBBnaE7Ih7TuuTNiE4mVetBN8XZJ5bLdc4UuMvtFhw"
+                }    
+
+            response = requests.request("GET", url, headers=headers, data=payload)
+
+            if response.status_code > 299:
+                raise Exception("HTTP {response.statuscode} Bad Status Code")
+            #print(response.json())
+            return json.loads(response.text)
+
+        except Exception as e:
+            raise Exception("Error getting dashboard data failed")
+
+    def get_accounts_for_user(self) -> dict:
+        basiq_id="11103cba-4a08-4397-84a5-22ac125ed2f6"
+        try:
+            url = self.base_url + "/basiq/balance"
+
+            payload = {}
+            headers = {
+                "accepts": "application/json",
+                "Authorization": "eyJraWQiOiJlcXhraW1yN21mRkwySmpjMmx1ZCtuT0JlNk5DVU00aHNMaEtta1p6MjVRPSIsImFsZyI6IlJTMjU2In0.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.ZVlGgqM_Bxz1IdF65eN097rh0KBUeVxkw2zm806uwqpAeiGoxGXjJYVLx3kIwcSk50dhqNCu5_MZqt9lIfHNobFGs7Xjz-g2GVEPDttMELqllmCGier9m8QjhAmE3VJ3TwXbR1-oN1IxPkJe4slxKiFKN4OMqdZpAYETsfX0IJc2WQFrC4A00LYmgTLD4Uk0lxToGStsqK3UOp4ZZhmzRUfbKYgOcfNIGvpuUqWmHFjqtxgSG2g5s7Wa1QCloLmIadL3GEOqw8ulTS_BoPNYusy2VW63VSJX2VjO3DPYY41ViBBnaE7Ih7TuuTNiE4mVetBN8XZJ5bLdc4UuMvtFhw"
+                }    
+
+            response = requests.request("GET", url, headers=headers)
+            if response.status_code > 299:
+                raise Exception("HTTP {response.statuscode} Bad Status Code")
+            #print(response.json())
+            return json.loads(response.text)
+
+        except Exception as e:
+            raise Exception("Error getting account details failed")

frontend/environment/.env

@@ -2,4 +2,4 @@ HOST=0.0.0.0
 PORT=8080
 DEBUG=True
 DEV_TOOLS_PROPS_CHECK=False
-BASE_API_URL="https://tz2pv43ci1.execute-api.ap-southeast-2.amazonaws.com/prod"
+BASE_API_URL="https://9u48mrkt22.execute-api.ap-southeast-2.amazonaws.com/prod"

frontend/environment/.env.development

@@ -2,4 +2,4 @@ HOST=127.0.0.1
 PORT=8080
 DEBUG=True
 DEV_TOOLS_PROPS_CHECK=True
-BASE_API_URL="https://tz2pv43ci1.execute-api.ap-southeast-2.amazonaws.com/prod"
+BASE_API_URL="https://pu0s16t327.execute-api.ap-southeast-2.amazonaws.com/prod"

frontend/pages/breakdown/breakdown.py

@@ -7,15 +7,13 @@
 from app import app
 from pages.breakdown import breakdown_callbacks
 
-
-df = pd.read_csv('pages/breakdown/dummies.csv')
+dfIncoming, dfOutgoing = breakdown_callbacks.get_incoming_outgoing_transactions()
 
 layout = dbc.Container([
     dbc.Row([
         dbc.Col(html.H1("YOUR FINANCIAL DASHBOARD",
                         className='text-center text-primary mb-4',id="title"),
-                width=12)
-
+                width=12)   
     ]),
 
     dbc.Row([
@@ -25,7 +23,7 @@
 
         dbc.Col([
             dcc.Dropdown(id = 'yearly_spending_dd', multi = False, placeholder = 'Select a year',
-                        options = [{'label':x, 'value':x} for x in sorted(df.iloc[:,3].unique())]),
+                        options = [{'label':x, 'value':x} for x in sorted(dfOutgoing.iloc[:,0].unique())]),
 
             dcc.Graph(id = 'yearly_spending_graph', figure={})
         ], width = {'size':6}),
@@ -35,15 +33,14 @@
 
     ]),
 
-
     dbc.Row([
 
         dbc.Col(html.H2("Yearly Saving",
                         className='text-left text-primary mb-4 '), width = 12),
 
         dbc.Col([
             dcc.Dropdown(id = 'yearly_saving_dd', multi = False, placeholder = 'Select a year',
-                        options = [{'label':x, 'value':x} for x in sorted(df.iloc[:,3].unique())]),
+                        options = [{'label':x, 'value':x} for x in sorted(dfIncoming.iloc[:,0].unique())]),
 
             dcc.Graph(id = 'yearly_saving_graph', figure={})
         ], width = {'size':6}),
@@ -53,4 +50,3 @@
 
     ])
 ], fluid = False)
-

frontend/pages/breakdown/breakdown_callbacks.py

@@ -3,28 +3,115 @@
 from app import app
 import pandas as pd
 import plotly.express as px
-
-df = pd.read_csv('pages/breakdown/dummies.csv')
+import json
 
 @app.callback(
     Output('yearly_spending_graph', 'figure'),
     Input('yearly_spending_dd', 'value')
 )
 def update_graph(year):
-    dff = df[df.iloc[:,3]==year]
-    figln = px.line(dff, x='Month', y='Spending')
+    dfIncoming, dfOutgoing = get_incoming_outgoing_transactions()
+    dff = dfOutgoing[dfOutgoing.iloc[:,0]==year]
+    figln = px.line(dff, x='month', y='amount')
     return figln
 
-
 @app.callback(
     Output('yearly_saving_graph', 'figure'),
     Input('yearly_saving_dd', 'value')
 )
 def update_graph(year):
-    dff = df[df.iloc[:,3]==year]
-    figln = px.line(dff, x='Month', y='Saving')
+    dfIncoming, dfOutgoing = get_incoming_outgoing_transactions()
+    dff = dfIncoming[dfIncoming.iloc[:,0]==year]
+    figln = px.line(dff, x='month', y='amount')
     return figln
 
-# def callback(n):
-#     api = API()
-#     return f"{n}"
+def get_transaction_data():
+    api = API()
+    transaction_result = api.basiq_api_transaction_data()
+    data = transaction_result["data"]
+
+    accounts = get_accounts()
+
+    df = pd.json_normalize(data)
+    accounts_df = pd.json_normalize(accounts)
+
+    processedData = df.filter(["id", "status", "amount", "direction", "account", "class", "postDate", "subClass.title"], axis=1)
+    monthsForProcessing = ["Jan", "Feb", "Mar", "Apr", "May", "June", "July", "Aug", "Sep", "Oct", "Nov", "Dec"]
+
+    #Renamed 'subClass.title' as 'category'. This is not the category I was looking to use but it could do the job
+    processedData.rename(columns = {'subClass.title':'category'}, inplace = True)
+
+    #Processing the date value that is returned. It will be split from a string in to 3 arrays listed below
+    year = []
+    month = []
+    day = []
+
+    #Account name included to give something human readable to the account id number
+    accountName = []
+    #Here I am transforming the data in the postDate and account columns. 
+    for column in df[["postDate", "account"]]:
+        for val in df[column]:
+            if column == "postDate":
+
+                #postDate has the initial string value split, giving us a date formatted to yyy-mm-dd at the 0th index. This is then
+                #split at the '-' into separate years, months and dates and added in to the appropriate array above 
+
+                initialSplit = val.split('T')
+                yearMonthDay = initialSplit[0].split("-")
+                year.append(yearMonthDay[0])
+                month.append(monthsForProcessing[int(yearMonthDay[1]) - 1])
+                day.append(yearMonthDay[2])
+            if column == "account":
+
+                #If the account id matches the account id in the 'Accounts' dataframe (created 2 cells above) then the account name
+                #is added to the accountName array, to be added in to the dataframe 
+                for index, accountNumber in enumerate(accounts_df["id"]):
+                    if val == accountNumber:
+                        accountName.append(accounts_df["name"][index])
+                        break
+
+    #All of the new values are added to the existing dataframe
+    processedData["year"] = year
+    processedData["month"] = month
+    processedData["day"] = day
+    # processedData["accountName"] = accountName
+    processedData = processedData.drop('postDate', axis=1)
+    processedData["category"].fillna("Unknown", inplace = True)
+
+    return processedData
+
+def get_accounts():
+    api = API()
+    account_details = api.get_accounts_for_user()
+    data = account_details["data"]
+
+    return data
+
+def get_incoming_outgoing_transactions():
+    processedData = get_transaction_data()
+
+    outgoingTransactions = processedData[processedData.amount.astype('float64') < 0]
+
+    #transforming values to absolute value to prevent the issue mentioned above
+    for index, row in outgoingTransactions.iterrows():
+        row["amount"] = abs(float(row["amount"]))
+
+    incomingTransactions = processedData[processedData.amount.astype('float64') > 0]
+
+    for index, row in incomingTransactions.iterrows():
+        row["amount"] = abs(float(row["amount"]))
+
+    monthlySpending = outgoingTransactions.groupby(['year', 'month']).sum().reset_index().filter(['year', 'month', 'amount'], axis=1)
+    monthlySaving = incomingTransactions.groupby(['year', 'month']).sum().reset_index().filter(['year', 'month', 'amount'], axis=1)
+
+    ordered_months = ["Jan", "Feb", "Mar", "Apr", "May", "June",
+      "July", "Aug", "Sep", "Oct", "Nov", "Dec"]
+
+    #Both incoming models are sorted by month
+    monthlySaving['to_sort']=monthlySaving['month'].apply(lambda x:ordered_months.index(x))
+    monthlySaving = monthlySaving.sort_values('to_sort')
+
+    monthlySpending['to_sort']=monthlySpending['month'].apply(lambda x:ordered_months.index(x))
+    monthlySpending = monthlySpending.sort_values('to_sort')
+
+    return monthlySaving, monthlySpending

frontend/pages/dashboard/dashboard.py

@@ -4,9 +4,20 @@
 from pages.dashboard import dashboard_callbacks
 import pandas as pd
 import dash_table
+import json
 
+df_outgoing_transactions = dashboard_callbacks.get_outgoing_transactions()
+df_absolute_spending_transaction_values = dashboard_callbacks.list_outgoing_transactions()
 
-df = pd.read_csv('pages/dashboard/dummies.csv')
+accounts = dashboard_callbacks.get_accounts()
+
+accounts_df = pd.json_normalize(accounts)
+
+columns = [{"name": i, "id": i} for i in df_absolute_spending_transaction_values.columns]
+columns[2]["name"] = "Amount"
+columns[6]["name"] = "Category"
+columns[7]["name"] = "Year"
+columns[8]["name"] = "Month"
 
 layout = dbc.Container([
 
@@ -16,24 +27,24 @@
 
         html.H3("YEARLY SPENDING CATEGORIES",
                         className='text-center text-primary mb-4'),
-
         dbc.Row([
 
                 dbc.Col(html.P('Chart shows categories of spending over the selected year.', className='text-right text-primary, mb-4'),
                     width = {'size': 3,'offset' : 0}),
                 dbc.Col([
                         dcc.Dropdown(id = 'piechart_dd', multi = False, placeholder = 'Select a year',
-                        options = [{'label':x, 'value':x} for x in sorted(df.iloc[:,3].unique())]),
+                        options = [{'label':x, 'value':x} for x in sorted(df_absolute_spending_transaction_values.iloc[:,7].unique())]),
 
                         dcc.Graph(id = 'piechart', figure={})
                 ])
             ]
         ),
-    
+
         dbc.Row(
             dash_table.DataTable(
-                data = df.to_dict('records'), 
-                columns = [{"name": i, "id": i} for i in df.columns],
+                data = df_outgoing_transactions.to_dict('records'), 
+                columns = columns,
+                hidden_columns = ["index", "transactionNumber", "id", "status", "direction", "account", "class", "day"],
                 style_cell = {'textAlign':'left'},
                     style_header={
                     'backgroundColor': '#4f8fe3',