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testing
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Ashwath Krishnan authored and Ashwath Krishnan committed Feb 6, 2025
1 parent 61cb75d commit a1db6b4
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Showing 2 changed files with 156 additions and 156 deletions.
156 changes: 108 additions & 48 deletions Dashboard/backup.py → Dashboard/app-new.py
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Original file line number Diff line number Diff line change
@@ -1,70 +1,123 @@
# import datetime
# import pandas as pd
# import seaborn as sns
# from pathlib import Path
# from shiny import App, ui
# import matplotlib.pyplot as plt
# from pymongo import MongoClient
# from shiny import App, ui, reactive, render_ui, render
# from maidr.widget.shiny import render_maidr

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

# def parse_date(date_str):
# """Try parsing the date in two possible formats."""
# try:
# return pd.to_datetime(date_str, format="%b %d, %Y") # Format: "Jun 13, 2024"
# return pd.to_datetime(date_str, format="%b %d, %Y") # e.g., "Jun 13, 2024"
# except ValueError:
# try:
# return pd.to_datetime(date_str, format="%d %b %Y", dayfirst=True)
# except ValueError:
# return pd.NaT # Handle invalid dates
# return pd.NaT # Return NaT for invalid dates

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

# # min_date = df['parsed_date'].min()
# # max_date = df['parsed_date'].max()
# flattened_data = []
# for document in cursor:
# _id = document["_id"]
# user_id = document["user_id"]
# for date_entry in document["dates"]:
# flattened_entry = {"_id": _id, "user_id": user_id, "date": date_entry["date"]}
# # Unpack nested data values
# for key, value in date_entry["data"].items():
# flattened_entry[key] = value["value"]
# flattened_data.append(flattened_entry)

# df = pd.DataFrame(flattened_data)
# # Parse dates and drop rows with invalid dates
# df['parsed_date'] = df['date'].apply(parse_date)
# df = df.dropna(subset=['parsed_date'])
# return df

# # Fetch the initial data when the app starts.
# initial_df = fetch_data()
# users = initial_df["user_id"].unique().tolist()

# users = df["user_id"].unique().tolist()
# # -------------------------------------------------------
# # 2. Define the UI
# # -------------------------------------------------------

# # Define the UI
# app_ui = ui.page_fluid(
# ui.h2("PCHA Health Dashboard"),
# ui.page_sidebar(
# ui.page_sidebar(
# ui.sidebar(
# # Render the user selection dynamically.
# ui.input_select("user_id", label="Select User", choices=users),
# # Button to refresh the data.
# ui.input_action_button("refresh_data", "Refresh Data"),
# # ui.input_action_button("reset_filter", "Reset Filters"),
# # Additional inputs (e.g., for date filtering)
# ui.input_text("start_day", label="Start Day", placeholder="DD"),
# ui.input_text("start_month", label="Start Month", placeholder="MM"),
# ui.input_text("start_year", label="Start Year", placeholder="YYYY"),
# ui.input_text("end_day", label="End Day", placeholder="DD"),
# ui.input_text("end_month", label="End Month", placeholder="MM"),
# ui.input_text("end_year", label="End Year", placeholder="YYYY"),
# bg="#f8f8f8"
# ),
# ui.navset_card_tab(
# ui.nav_panel("Step Count", ui.card(ui.output_ui("plot_stepCount"))),
# ui.nav_panel("Distance Walking/Running", ui.card(ui.output_ui("plot_distance"))),
# ui.nav_panel("Exercise Time", ui.card(ui.output_ui("plot_exerciseTime"))),
# ui.nav_panel("Basal Energy Burned", ui.card(ui.output_ui("plot_basalEnergy")),),
# ui.nav_panel("Active Energy Burned", ui.card(ui.output_ui("plot_activeEnergy"))),
# ),
# )
# bg="#f8f8f8"
# ),
# ui.navset_card_tab(
# ui.nav_panel("Step Count", ui.card(ui.output_ui("plot_stepCount"))),
# ui.nav_panel("Distance Walking/Running", ui.card(ui.output_ui("plot_distance"))),
# ui.nav_panel("Exercise Time", ui.card(ui.output_ui("plot_exerciseTime"))),
# ui.nav_panel("Basal Energy Burned", ui.card(ui.output_ui("plot_basalEnergy")),),
# ui.nav_panel("Active Energy Burned", ui.card(ui.output_ui("plot_activeEnergy"))),
# ),
# )
# )

# def server(inp, _, __):

# # -------------------------------------------------------
# # 3. Define Server Logic
# # -------------------------------------------------------

# def server(input, output, session):
# data_store = reactive.Value(initial_df)

# @reactive.Effect
# def refresh_data_effect():
# _ = input.refresh_data()
# new_df = fetch_data()
# data_store.set(new_df)
# print("Data refreshed at", datetime.datetime.now())

# # @reactive.Effect
# # def reset_filter_effect():
# # _ = input.reset_filter()
# # df = fetch_data()
# # start_date = df['parsed_date'].min()
# # end_date = df['parsed_date'].max()


# @render_maidr
# def plot_stepCount():
# user_data = df[df["user_id"] == inp.user_id()]
# user_id = inp.user_id() # Selected user

# # Fetch start and end dates. If null, default to min and max dates
# df_latest = data_store.get()
# user_id = input.user_id()
# df = df_latest[df_latest["user_id"] == user_id]
# try:
# start_date = pd.to_datetime(f"{inp.start_year()}-{inp.start_month()}-{inp.start_day()}")
# start_date = pd.to_datetime(f"{input.start_year()}-{input.start_month()}-{input.start_day()}")
# except ValueError:
# start_date = df['parsed_date'].min() # Default to earliest date in the dataset

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

Expand All @@ -74,6 +127,7 @@
# (df["parsed_date"] >= start_date) &
# (df["parsed_date"] <= end_date)
# ]
# user_data = user_data.sort_values(by="parsed_date")

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

# @render_maidr
# def plot_distance():
# user_data = df[df["user_id"] == inp.user_id()]
# user_id = inp.user_id() # Selected user
# df_latest = data_store.get()
# user_id = input.user_id()
# df = df_latest[df_latest["user_id"] == user_id]

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

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

Expand All @@ -117,17 +172,18 @@

# @render_maidr
# def plot_basalEnergy():
# user_data = df[df["user_id"] == inp.user_id()]
# user_id = inp.user_id() # Selected user
# df_latest = data_store.get()
# user_id = input.user_id()
# df = df_latest[df_latest["user_id"] == user_id]

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

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

Expand All @@ -148,17 +204,18 @@

# @render_maidr
# def plot_activeEnergy():
# user_data = df[df["user_id"] == inp.user_id()]
# user_id = inp.user_id() # Selected user
# df_latest = data_store.get()
# user_id = input.user_id()
# df = df_latest[df_latest["user_id"] == user_id]

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

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

Expand All @@ -179,17 +236,18 @@

# @render_maidr
# def plot_exerciseTime():
# user_data = df[df["user_id"] == inp.user_id()]
# user_id = inp.user_id() # Selected user
# df_latest = data_store.get()
# user_id = input.user_id()
# df = df_latest[df_latest["user_id"] == user_id]

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

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

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

# # Create the app
# # -------------------------------------------------------
# # 4. Create and Run the App
# # -------------------------------------------------------

# app = App(app_ui, server)

# # Run the app
# if __name__ == "__main__":
# app.run()
# app.run()
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