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Ashwath KrishnanAshwath Krishnan
Ashwath Krishnan
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Ashwath Krishnan
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testing
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Diff for: ‎Dashboard/backup.py renamed to ‎Dashboard/app-new.py

+108-48
Original file line numberDiff line numberDiff line change
@@ -1,70 +1,123 @@
1+
# import datetime
12
# import pandas as pd
23
# import seaborn as sns
34
# from pathlib import Path
4-
# from shiny import App, ui
55
# import matplotlib.pyplot as plt
6+
# from pymongo import MongoClient
7+
# from shiny import App, ui, reactive, render_ui, render
68
# from maidr.widget.shiny import render_maidr
79

8-
# file_path = Path(__file__).parent / "healthdata.csv"
9-
# df = pd.read_csv(file_path)
1010

1111
# def parse_date(date_str):
12+
# """Try parsing the date in two possible formats."""
1213
# try:
13-
# return pd.to_datetime(date_str, format="%b %d, %Y") # Format: "Jun 13, 2024"
14+
# return pd.to_datetime(date_str, format="%b %d, %Y") # e.g., "Jun 13, 2024"
1415
# except ValueError:
1516
# try:
1617
# return pd.to_datetime(date_str, format="%d %b %Y", dayfirst=True)
1718
# except ValueError:
18-
# return pd.NaT # Handle invalid dates
19+
# return pd.NaT # Return NaT for invalid dates
1920

20-
# df['parsed_date'] = df['date'].apply(parse_date)
21-
# df = df.dropna(subset=['parsed_date']) # Remove rows with invalid parsed dates
21+
# def fetch_data():
22+
# """Connect to Azure Cosmos DB, flatten the nested documents, and return a DataFrame."""
23+
# connection_string = (
24+
# "mongodb://pcha:DBQWfFLdaAwofXET3QyLDt1ndCAbJdGwoq8iF4u79P2A0QArmODzkbENAMqtobHZOhDn765q2dlmACDbeuMcHg=="
25+
# "@pcha.mongo.cosmos.azure.com:10255/?ssl=true&retrywrites=false&replicaSet=globaldb"
26+
# "&maxIdleTimeMS=120000&appName=@pcha@"
27+
# )
28+
# client = MongoClient(connection_string, tlsAllowInvalidCertificates=True)
29+
# db = client['pcha_db']
30+
# collection = db['health_data']
31+
# cursor = collection.find()
2232

23-
# # min_date = df['parsed_date'].min()
24-
# # max_date = df['parsed_date'].max()
33+
# flattened_data = []
34+
# for document in cursor:
35+
# _id = document["_id"]
36+
# user_id = document["user_id"]
37+
# for date_entry in document["dates"]:
38+
# flattened_entry = {"_id": _id, "user_id": user_id, "date": date_entry["date"]}
39+
# # Unpack nested data values
40+
# for key, value in date_entry["data"].items():
41+
# flattened_entry[key] = value["value"]
42+
# flattened_data.append(flattened_entry)
43+
44+
# df = pd.DataFrame(flattened_data)
45+
# # Parse dates and drop rows with invalid dates
46+
# df['parsed_date'] = df['date'].apply(parse_date)
47+
# df = df.dropna(subset=['parsed_date'])
48+
# return df
49+
50+
# # Fetch the initial data when the app starts.
51+
# initial_df = fetch_data()
52+
# users = initial_df["user_id"].unique().tolist()
2553

26-
# users = df["user_id"].unique().tolist()
54+
# # -------------------------------------------------------
55+
# # 2. Define the UI
56+
# # -------------------------------------------------------
2757

28-
# # Define the UI
2958
# app_ui = ui.page_fluid(
3059
# ui.h2("PCHA Health Dashboard"),
31-
# ui.page_sidebar(
60+
# ui.page_sidebar(
3261
# ui.sidebar(
62+
# # Render the user selection dynamically.
3363
# ui.input_select("user_id", label="Select User", choices=users),
64+
# # Button to refresh the data.
3465
# ui.input_action_button("refresh_data", "Refresh Data"),
66+
# # ui.input_action_button("reset_filter", "Reset Filters"),
67+
# # Additional inputs (e.g., for date filtering)
3568
# ui.input_text("start_day", label="Start Day", placeholder="DD"),
3669
# ui.input_text("start_month", label="Start Month", placeholder="MM"),
3770
# ui.input_text("start_year", label="Start Year", placeholder="YYYY"),
3871
# ui.input_text("end_day", label="End Day", placeholder="DD"),
3972
# ui.input_text("end_month", label="End Month", placeholder="MM"),
4073
# ui.input_text("end_year", label="End Year", placeholder="YYYY"),
41-
# bg="#f8f8f8"
42-
# ),
43-
# ui.navset_card_tab(
44-
# ui.nav_panel("Step Count", ui.card(ui.output_ui("plot_stepCount"))),
45-
# ui.nav_panel("Distance Walking/Running", ui.card(ui.output_ui("plot_distance"))),
46-
# ui.nav_panel("Exercise Time", ui.card(ui.output_ui("plot_exerciseTime"))),
47-
# ui.nav_panel("Basal Energy Burned", ui.card(ui.output_ui("plot_basalEnergy")),),
48-
# ui.nav_panel("Active Energy Burned", ui.card(ui.output_ui("plot_activeEnergy"))),
49-
# ),
50-
# )
74+
# bg="#f8f8f8"
75+
# ),
76+
# ui.navset_card_tab(
77+
# ui.nav_panel("Step Count", ui.card(ui.output_ui("plot_stepCount"))),
78+
# ui.nav_panel("Distance Walking/Running", ui.card(ui.output_ui("plot_distance"))),
79+
# ui.nav_panel("Exercise Time", ui.card(ui.output_ui("plot_exerciseTime"))),
80+
# ui.nav_panel("Basal Energy Burned", ui.card(ui.output_ui("plot_basalEnergy")),),
81+
# ui.nav_panel("Active Energy Burned", ui.card(ui.output_ui("plot_activeEnergy"))),
82+
# ),
83+
# )
5184
# )
5285

53-
# def server(inp, _, __):
86+
87+
# # -------------------------------------------------------
88+
# # 3. Define Server Logic
89+
# # -------------------------------------------------------
90+
91+
# def server(input, output, session):
92+
# data_store = reactive.Value(initial_df)
93+
94+
# @reactive.Effect
95+
# def refresh_data_effect():
96+
# _ = input.refresh_data()
97+
# new_df = fetch_data()
98+
# data_store.set(new_df)
99+
# print("Data refreshed at", datetime.datetime.now())
100+
101+
# # @reactive.Effect
102+
# # def reset_filter_effect():
103+
# # _ = input.reset_filter()
104+
# # df = fetch_data()
105+
# # start_date = df['parsed_date'].min()
106+
# # end_date = df['parsed_date'].max()
107+
54108

55109
# @render_maidr
56110
# def plot_stepCount():
57-
# user_data = df[df["user_id"] == inp.user_id()]
58-
# user_id = inp.user_id() # Selected user
59-
60-
# # Fetch start and end dates. If null, default to min and max dates
111+
# df_latest = data_store.get()
112+
# user_id = input.user_id()
113+
# df = df_latest[df_latest["user_id"] == user_id]
61114
# try:
62-
# start_date = pd.to_datetime(f"{inp.start_year()}-{inp.start_month()}-{inp.start_day()}")
115+
# start_date = pd.to_datetime(f"{input.start_year()}-{input.start_month()}-{input.start_day()}")
63116
# except ValueError:
64117
# start_date = df['parsed_date'].min() # Default to earliest date in the dataset
65118

66119
# try:
67-
# end_date = pd.to_datetime(f"{inp.end_year()}-{inp.end_month()}-{inp.end_day()}")
120+
# end_date = pd.to_datetime(f"{input.end_year()}-{input.end_month()}-{input.end_day()}")
68121
# except ValueError:
69122
# end_date = df['parsed_date'].max()
70123

@@ -74,6 +127,7 @@
74127
# (df["parsed_date"] >= start_date) &
75128
# (df["parsed_date"] <= end_date)
76129
# ]
130+
# user_data = user_data.sort_values(by="parsed_date")
77131

78132
# fig, ax = plt.subplots(figsize=(10, 8))
79133
# s_plot = sns.lineplot(data=user_data, x="date", y="stepCount", marker="o", ax=ax)
@@ -85,17 +139,18 @@
85139

86140
# @render_maidr
87141
# def plot_distance():
88-
# user_data = df[df["user_id"] == inp.user_id()]
89-
# user_id = inp.user_id() # Selected user
142+
# df_latest = data_store.get()
143+
# user_id = input.user_id()
144+
# df = df_latest[df_latest["user_id"] == user_id]
90145

91146
# # Fetch start and end dates. If null, default to min and max dates
92147
# try:
93-
# start_date = pd.to_datetime(f"{inp.start_year()}-{inp.start_month()}-{inp.start_day()}")
148+
# start_date = pd.to_datetime(f"{input.start_year()}-{input.start_month()}-{input.start_day()}")
94149
# except ValueError:
95150
# start_date = df['parsed_date'].min() # Default to earliest date in the dataset
96151

97152
# try:
98-
# end_date = pd.to_datetime(f"{inp.end_year()}-{inp.end_month()}-{inp.end_day()}")
153+
# end_date = pd.to_datetime(f"{input.end_year()}-{input.end_month()}-{input.end_day()}")
99154
# except ValueError:
100155
# end_date = df['parsed_date'].max()
101156

@@ -117,17 +172,18 @@
117172

118173
# @render_maidr
119174
# def plot_basalEnergy():
120-
# user_data = df[df["user_id"] == inp.user_id()]
121-
# user_id = inp.user_id() # Selected user
175+
# df_latest = data_store.get()
176+
# user_id = input.user_id()
177+
# df = df_latest[df_latest["user_id"] == user_id]
122178

123179
# # Fetch start and end dates. If null, default to min and max dates
124180
# try:
125-
# start_date = pd.to_datetime(f"{inp.start_year()}-{inp.start_month()}-{inp.start_day()}")
181+
# start_date = pd.to_datetime(f"{input.start_year()}-{input.start_month()}-{input.start_day()}")
126182
# except ValueError:
127183
# start_date = df['parsed_date'].min() # Default to earliest date in the dataset
128184

129185
# try:
130-
# end_date = pd.to_datetime(f"{inp.end_year()}-{inp.end_month()}-{inp.end_day()}")
186+
# end_date = pd.to_datetime(f"{input.end_year()}-{input.end_month()}-{input.end_day()}")
131187
# except ValueError:
132188
# end_date = df['parsed_date'].max()
133189

@@ -148,17 +204,18 @@
148204

149205
# @render_maidr
150206
# def plot_activeEnergy():
151-
# user_data = df[df["user_id"] == inp.user_id()]
152-
# user_id = inp.user_id() # Selected user
207+
# df_latest = data_store.get()
208+
# user_id = input.user_id()
209+
# df = df_latest[df_latest["user_id"] == user_id]
153210

154211
# # Fetch start and end dates. If null, default to min and max dates
155212
# try:
156-
# start_date = pd.to_datetime(f"{inp.start_year()}-{inp.start_month()}-{inp.start_day()}")
213+
# start_date = pd.to_datetime(f"{input.start_year()}-{input.start_month()}-{input.start_day()}")
157214
# except ValueError:
158215
# start_date = df['parsed_date'].min() # Default to earliest date in the dataset
159216

160217
# try:
161-
# end_date = pd.to_datetime(f"{inp.end_year()}-{inp.end_month()}-{inp.end_day()}")
218+
# end_date = pd.to_datetime(f"{input.end_year()}-{input.end_month()}-{input.end_day()}")
162219
# except ValueError:
163220
# end_date = df['parsed_date'].max()
164221

@@ -179,17 +236,18 @@
179236

180237
# @render_maidr
181238
# def plot_exerciseTime():
182-
# user_data = df[df["user_id"] == inp.user_id()]
183-
# user_id = inp.user_id() # Selected user
239+
# df_latest = data_store.get()
240+
# user_id = input.user_id()
241+
# df = df_latest[df_latest["user_id"] == user_id]
184242

185243
# # Fetch start and end dates. If null, default to min and max dates
186244
# try:
187-
# start_date = pd.to_datetime(f"{inp.start_year()}-{inp.start_month()}-{inp.start_day()}")
245+
# start_date = pd.to_datetime(f"{input.start_year()}-{input.start_month()}-{input.start_day()}")
188246
# except ValueError:
189247
# start_date = df['parsed_date'].min() # Default to earliest date in the dataset
190248

191249
# try:
192-
# end_date = pd.to_datetime(f"{inp.end_year()}-{inp.end_month()}-{inp.end_day()}")
250+
# end_date = pd.to_datetime(f"{input.end_year()}-{input.end_month()}-{input.end_day()}")
193251
# except ValueError:
194252
# end_date = df['parsed_date'].max()
195253

@@ -209,9 +267,11 @@
209267
# ax.tick_params(axis="x", rotation=90, labelsize=5)
210268
# return s_plot
211269

212-
# # Create the app
270+
# # -------------------------------------------------------
271+
# # 4. Create and Run the App
272+
# # -------------------------------------------------------
273+
213274
# app = App(app_ui, server)
214275

215-
# # Run the app
216276
# if __name__ == "__main__":
217-
# app.run()
277+
# app.run()

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