Update app.py

app.py

@@ -1,58 +1,122 @@
 import pandas as pd
+
+import os
+
 import panel as pn
 import hvplot as hv
-from datetime import datetime
-from datetime import date,timedelta
-pn.extension('bokeh', template='bootstrap')
+
+pn.extension("bokeh", template="bootstrap")
 import hvplot.pandas
+
+from datetime import datetime
+from datetime import date, timedelta
+
 from DescriptionDict import DescriptionDict
 
-@pn.cache
-def get_df(todaydate):
-    todaydate2 = datetime.today().strftime("%Y-%m-%d")
-    # todaydate2 =todaydate.strftime("%Y-%m-%d")
-    url = f"https://raw.githubusercontent.com/alirezax2/GurusFocusCrawl/main/gurufocus/GuruFocus_merged_{todaydate2}.csv"
-    df = pd.read_csv(url)
-    
-    return df
+# from dotenv import load_dotenv
+
+# from datasets import load_dataset
+from utils import load_hf_dataset  # ,upload_to_hf_dataset, download_from_hf_dataset
+
+# Load environment variables from .env file
+# load_dotenv()
+
+# Get the Hugging Face API token from the environment; either set in .env file or in the environment directly in GitHub
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Get the name of the  dataset for TradingView, GuruFocus to read from
+dataset_name_TradingView_input = os.getenv("dataset_name_TradingView_input")
+dataset_name_GuruFocus_input = os.getenv("dataset_name_GuruFocus_input")
+
+
+@pn.cache()
+def get_tradingview(current_datetime):
+    # Load lastest TradingView DataSet from HuggingFace Dataset which is always america.csv
+    # download_from_hf_dataset("america.csv", dataset_name_TradingView_input, HF_TOKEN)
+    return load_hf_dataset("america.csv", HF_TOKEN, dataset_name_TradingView_input)
+
+
+@pn.cache()
+def get_gurufocus(current_datetime):
+    # Load lastest GuruFocus DataSet from HuggingFace Dataset which is always GuruFocus_merged.csv
+    # daily_gurufocus_DF = load_dataset(dataset_name_GuruFocus_output , data_files="GuruFocus_merged.csv", split="train" , token=HF_TOKEN ).to_pandas()
+    return load_hf_dataset(
+        "GuruFocus_merged.csv", HF_TOKEN, dataset_name_GuruFocus_input
+    )
+
 
 todaydate = pn.widgets.DatePicker(
-    name ="End Date",# value=datetime(2000, 1, 1),
-    description='Select a Date',
-    end= date.today() #date(2023, 9, 1)
+    name="End Date",  # value=datetime(2000, 1, 1),
+    description="Select a Date",
+    end=date.today(),  # date(2023, 9, 1)
+)
+
+todaydate2 = datetime.today().strftime("%Y-%m-%d")
+df = get_gurufocus(todaydate2)
+selectedcol = pn.widgets.Select(
+    name="Select Ratio", value="PEG Ratio", options=list(df.columns)
+)
+selecteditem = pn.widgets.Select(
+    name="Select Item", value="Sector", options=["Industry", "Sector"]
+)
+selectedmethod = pn.widgets.Select(
+    name="Select Method", value="Mean", options=["Mean", "Min", "Max"]
 )
 
-todaydate2 = datetime.today().strftime("%Y-%m-%d")   
-df = get_df(todaydate2)
-selectedcol = pn.widgets.Select(name='Select Ratio', value = 'PEG Ratio', options=list(df.columns))
-selecteditem = pn.widgets.Select(name='Select Item', value='Sector' , options=['Industry' , 'Sector'])
-selectedmethod = pn.widgets.Select(name='Select Method', value= 'Mean' , options=['Mean', 'Min' , 'Max'])
+ticker = pn.widgets.AutocompleteInput(
+    name="Ticker", options=list(df.Ticker), placeholder="Write Ticker here همین جا"
+)
 
-ticker = pn.widgets.AutocompleteInput(name='Ticker', options=list(df.Ticker) , placeholder='Write Ticker here همین جا')
 ticker.value = "AAPL"
 
-selectedhover = 'Ticker'
-hv.extension('bokeh')
+selectedhover = "Ticker"
+hv.extension("bokeh")
+
 
-def create_plot(selectedcol,selecteditem,todaydate,selectedmethod,ticker):
-    df = get_df(todaydate)
-    if selectedmethod=='Mean':
+def create_plot(selectedcol, selecteditem, todaydate, selectedmethod, ticker):
+    df = get_gurufocus(todaydate)
+    if selectedmethod == "Mean":
         group_them = df.groupby(selecteditem)[selectedcol].mean()
-    if selectedmethod=='Min':
+    if selectedmethod == "Min":
         group_them = df.groupby(selecteditem)[selectedcol].min()
-    if selectedmethod=='Max':
+    if selectedmethod == "Max":
         group_them = df.groupby(selecteditem)[selectedcol].max()
-    df2 = df.merge(group_them, left_on=selecteditem, right_index=True, suffixes=('', f'_{selecteditem}_{selectedmethod}'))
-    df3 = df.query('Ticker == @ticker')
-    return df2.hvplot.bar(x=selecteditem, y=f'{selectedcol}_{selecteditem}_{selectedmethod}', hover_cols=selectedhover, height=800, width=1800).opts(xrotation=90, fontsize={'xticks': 10}).opts(show_grid=True) * df3.hvplot.bar(x=selecteditem, y=f'{selectedcol}',hover_cols=selectedhover,)
+    df2 = df.merge(
+        group_them,
+        left_on=selecteditem,
+        right_index=True,
+        suffixes=("", f"_{selecteditem}_{selectedmethod}"),
+    )
+    df3 = df.query("Ticker == @ticker")
+    return df2.hvplot.bar(
+        x=selecteditem,
+        y=f"{selectedcol}_{selecteditem}_{selectedmethod}",
+        hover_cols=selectedhover,
+        height=800,
+        width=1800,
+    ).opts(xrotation=90, fontsize={"xticks": 10}).opts(show_grid=True) * df3.hvplot.bar(
+        x=selecteditem,
+        y=f"{selectedcol}",
+        hover_cols=selectedhover,
+    )
+
 
 def create_alert(selectedcol):
     text = f"### {selectedcol} \n {DescriptionDict[selectedcol]}"
     return pn.pane.Alert(text, alert_type="warning")
-    
-
-bound_plot = pn.bind(create_plot, selectedcol=selectedcol , selecteditem=selecteditem, todaydate=todaydate, selectedmethod=selectedmethod,ticker=ticker)
-bound_alert = pn.bind(create_alert,selectedcol=selectedcol)
-pn.Column(pn.Row(selectedcol, selecteditem, todaydate,selectedmethod,ticker), bound_plot, bound_alert).servable(title="Financial Sector Ratios Navigator")
 
 
+bound_plot = pn.bind(
+    create_plot,
+    selectedcol=selectedcol,
+    selecteditem=selecteditem,
+    todaydate=todaydate,
+    selectedmethod=selectedmethod,
+    ticker=ticker,
+)
+bound_alert = pn.bind(create_alert, selectedcol=selectedcol)
+pn.Column(
+    pn.Row(selectedcol, selecteditem, todaydate, selectedmethod, ticker),
+    bound_plot,
+    bound_alert,
+).servable(title="Financial Sector Ratios Navigator")