Update app.py

app.py

@@ -1,284 +1,13 @@
-#%%
-from matplotlib.pyplot import title
-import tensorflow as tf
-from tensorflow import keras
-from huggingface_hub import from_pretrained_keras
-import pandas as pd
-import matplotlib.pyplot as plt
+    
 import streamlit as st
-from zipfile import ZipFile
-import os
 
-if ("0" != "mycustom"):
-	import datetime
-	from io import StringIO
-
-import warnings
-warnings.filterwarnings("ignore")
-
-if ("0" == "mycustom"):
-	os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-	os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-if ("0" == "mycustom"):
-	uri = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip"
-	zip_path = keras.utils.get_file(origin=uri, fname="jena_climate_2009_2016.csv.zip")
-	zip_file = ZipFile(zip_path)
-	zip_file.extractall()
-	csv_path = "jena_climate_2009_2016.csv"
-	df = pd.read_csv(csv_path)
-
-if ("0" == "mycustom"):
-	mybacklogmax = 10000
-	df = df.head(n=mybacklogmax)
-	st.dataframe(df)
-
-if ("0" != "mycustom"):
-	myfields = [0, 1, 5, 7, 8, 10, 11]
-	myfields = [1]
-	mytitles = ["Date Time","p (mbar)","T (degC)","Tpot (K)","Tdew (degC)","rh (%)","VPmax (mbar)","VPact (mbar)","VPdef (mbar)","sh (g/kg)","H2OC (mmol/mol)","rho (g/m**3)","wv (m/s)","max. wv (m/s)","wd (deg)"]
-
-	mybacklogmax = 10
-
-	atoday = datetime.date.today()
-
-	ayear = int(atoday.strftime("%Y"))-0
-	amonth = int(atoday.strftime("%m"))
-	amonthday = int(atoday.strftime("%d"))
-
-	csvString = ""
-	csvString += (",").join(mytitles)
-	adf = pd.DataFrame(columns=mytitles)
-	for i in range((ayear-mybacklogmax),ayear,1):
-		alink = ("https://data.weather.gov.hk/weatherAPI/opendata/opendata.php?dataType=CLMTEMP&year={}&rformat=csv&station=HKO").format(str(i))
-		df = pd.read_csv(alink, skiprows=[0,1,2], skipfooter=3, engine='python', on_bad_lines='skip')
-
-		df = df.reset_index()  # make sure indexes pair with number of rows
-		for index, row in df.iterrows():
-			if (row[2]!=amonth) or (row[3]!=amonthday):
-				continue
-
-			adate = ("{:02d}.{:02d}.{} 00:00:00").format(row[3], row[2], row[1])
-			csvString += '\n'+(",").join([adate,"",str(row[4]),"","","","","","","","","","","",""])
-			st.write(row[0],adate)
-			adf = adf.append({"Date Time":adate,"T (degC)":(row[4]),}, ignore_index=True)
-			break
-	adf = pd.read_csv(StringIO(csvString), sep=",")
-	df = adf
-	st.dataframe(df)
-
-#%%
-
-title = "Timeseries forecasting for weather prediction"
-
-st.title('Timeseries forecasting for weather prediction')
-
-st.write("Demonstrates how to do timeseries forecasting using a [LSTM model.](https://keras.io/api/layers/recurrent_layers/lstm/#lstm-class)This space demonstration is forecasting for weather prediction. *n* observation is selected from validation dataset." )
-st.write("Keras example authors: [Prabhanshu Attri, Yashika Sharma, Kristi Takach, Falak Shah](https://keras.io/examples/timeseries/timeseries_weather_forecasting/)")
-
-
-# %% model 
-
-titles = [
-    "Pressure",
-    "Temperature",
-    "Temperature in Kelvin",
-    "Temperature (dew point)",
-    "Relative Humidity",
-    "Saturation vapor pressure",
-    "Vapor pressure",
-    "Vapor pressure deficit",
-    "Specific humidity",
-    "Water vapor concentration",
-    "Airtight",
-    "Wind speed",
-    "Maximum wind speed",
-    "Wind direction in degrees",
-]
-
-feature_keys = [
-    "p (mbar)",
-    "T (degC)",
-    "Tpot (K)",
-    "Tdew (degC)",
-    "rh (%)",
-    "VPmax (mbar)",
-    "VPact (mbar)",
-    "VPdef (mbar)",
-    "sh (g/kg)",
-    "H2OC (mmol/mol)",
-    "rho (g/m**3)",
-    "wv (m/s)",
-    "max. wv (m/s)",
-    "wd (deg)",
+astations = [
+	["hongkongobservatory","HKO",22.3022566,114.1722662,"Hong Kong Observatory"],
+	["kingspark","KP",22.3115408,114.1685675,"Observatory Meteorological Station, King's Park"],
 ]
 
-date_time_key = "Date Time"
-split_fraction = 0.715
-train_split = int(split_fraction * int(df.shape[0]))
-step = 6
-
-past = 720
-future = 72
-learning_rate = 0.001
-batch_size = 256
-epochs = 10
-
-if ("0" != "mycustom"):
-	past = 0
-	future = 1
-	batch_size = 1
-	step = 1
-
-def normalize(data, train_split):
-    data_mean = data[:train_split].mean(axis=0)
-    data_std = data[:train_split].std(axis=0)
-    return (data - data_mean) / data_std
-
-print(
-    "The selected parameters are:",
-    ", ".join([titles[i] for i in [0, 1, 5, 7, 8, 10, 11]]),
-)
-selected_features = [feature_keys[i] for i in [0, 1, 5, 7, 8, 10, 11]]
-
-if ("0" != "mycustom"):
-	print(
-	    "The selected parameters are:",
-	    ", ".join([titles[i] for i in myfields]),
-	)
-	selected_features = [feature_keys[i] for i in myfields]
-
-features = df[selected_features]
-features.index = df[date_time_key]
-features.head()
-
-features = normalize(features.values, train_split)
-features = pd.DataFrame(features)
-features.head()
-
-train_data = features.loc[0 : train_split - 1]
-val_data = features.loc[train_split:]
-
-
-split_fraction = 0.715
-train_split = int(split_fraction * int(df.shape[0]))
-step = 6
-
-past = 720
-future = 72
-learning_rate = 0.001
-batch_size = 256
-epochs = 10
-
-if ("0" != "mycustom"):
-	past = 0
-	future = 1
-	batch_size = 1
-	step = 1
-
-def normalize(data, train_split):
-    data_mean = data[:train_split].mean(axis=0)
-    data_std = data[:train_split].std(axis=0)
-    return (data - data_mean) / data_std
-print(
-    "The selected parameters are:",
-    ", ".join([titles[i] for i in [0, 1, 5, 7, 8, 10, 11]]),
-)
-selected_features = [feature_keys[i] for i in [0, 1, 5, 7, 8, 10, 11]]
-
-if ("0" != "mycustom"):
-	print(
-	    "The selected parameters are:",
-	    ", ".join([titles[i] for i in myfields]),
-	)
-	selected_features = [feature_keys[i] for i in myfields]
-
-features = df[selected_features]
-features.index = df[date_time_key]
-features.head()
-
-features = normalize(features.values, train_split)
-features = pd.DataFrame(features)
-features.head()
-
-train_data = features.loc[0 : train_split - 1]
-val_data = features.loc[train_split:]
-start = past + future
-end = start + train_split
-
-if ("0" == "mycustom"):
-	x_train = train_data[[i for i in range(7)]].values
-	y_train = features.iloc[start:end][[1]]
-if ("0" != "mycustom"):
-	x_train = train_data[[i for i in range(len(myfields))]].values
-	y_train = features.iloc[start:end][[0]]
-
-sequence_length = int(past / step)
-x_end = len(val_data) - past - future
-
-label_start = train_split + past + future
-st.write(label_start)
-
-if ("0" == "mycustom"):
-	x_val = val_data.iloc[:x_end][[i for i in range(7)]].values
-	y_val = features.iloc[label_start:][[1]]
-if ("0" != "mycustom"):
-	x_val = val_data.iloc[:x_end][[i for i in range(len(myfields))]].values
-	y_val = features.iloc[label_start:][[0]]
-
-dataset_val = keras.preprocessing.timeseries_dataset_from_array(
-    x_val,
-    y_val,
-    sequence_length=sequence_length,
-    sampling_rate=step,
-    batch_size=batch_size,
-)
-#%%
-model = from_pretrained_keras("keras-io/timeseries_forecasting_for_weather")
-
-#%% 
-st.set_option('deprecation.showPyplotGlobalUse', False)
-def plot():
-	n = st.sidebar.slider("Step", min_value = 1, max_value=5, value = 1)
-	def show_plot(plot_data, delta, title):
-		labels = ["History", "True Future", "Model Prediction"]
-		marker = [".-", "rx", "go"]
-		time_steps = list(range(-(plot_data[0].shape[0]), 0))
-		if delta:
-			future = delta
-		else:
-			future = 0
-
-		plt.title(title)
-		for i, val in enumerate(plot_data):
-			if i:
-				plt.plot(future, plot_data[i], marker[i], markersize=10, label=labels[i])
-			else:
-				plt.plot(time_steps, plot_data[i].flatten(), marker[i], label=labels[i])
-		plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05),
-			ncol=3, fancybox=True, shadow=True)
-		plt.xlim([time_steps[0], (future + 5) * 2])
-		plt.xlabel("Time-Step")
-		plt.show()
-		return
-
-
-	for x, y in dataset_val.take(n):
-		if ("0" == "mycustom"):
-			show_plot(
-				[x[0][:, 1].numpy(), y[0].numpy(), model.predict(x)[0]],
-				12,
-				f"{n} Step Prediction",
-			)
-		if ("0" != "mycustom"):
-			show_plot(
-				[x[0][:, 0].numpy(), y[0].numpy(), model.predict(x)[0]],
-				12,
-				f"{n} Step Prediction",
-			)
-
-
-fig = plot()
-st.pyplot(fig)
-
-# %%
+acontainer1 = st.empty()
+aoption = acontainer1.selectbox(
+    'Which station?',
+    astations[1])
+acontainer2.write(aoption)