time series for gradio

.gitignore

@@ -1,3 +1,4 @@
 .venv
 .env
-.cache.sqlite
+.cache.sqlite
+__pycache__/

app.py

@@ -1,8 +1,20 @@
 import gradio as gr
+import pandas as pd
+import numpy as np
+import random
 
-def greet(name):
-    return "Hello " + name + "!!"
+from datetime import datetime, timedelta
+now = datetime.now()
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+df = pd.DataFrame({
+    'time': [now - timedelta(minutes=5*i) for i in range(25)],
+    'price': np.random.randint(100, 1000, 25),
+    'origin': [random.choice(["DFW", "DAL", "HOU"]) for _ in range(25)],
+    'destination': [random.choice(["JFK", "LGA", "EWR"]) for _ in range(25)],
+})
 
+with gr.Blocks() as demo:
+    gr.LinePlot(df, x="time", y="price")
+    gr.ScatterPlot(df, x="time", y="price", color="origin")
+
+demo.launch()

debug.ipynb

@@ -0,0 +1,22 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

infer.py

@@ -14,6 +14,11 @@ project_name = os.getenv('HOPSWORKS_PROJECT')
 project = hopsworks.login(project=project_name, api_key_value=api_key)
 fs = project.get_feature_store() 
 secrets = util.secrets_api(project.name)
+location_str = secrets.get_secret("SENSOR_LOCATION_JSON").value
+location = json.loads(location_str)
+country=location['country']
+city=location['city']
+street=location['street']
 
 AQI_API_KEY = secrets.get_secret("AQI_API_KEY").value
 location_str = secrets.get_secret("SENSOR_LOCATION_JSON").value
@@ -26,7 +31,7 @@ feature_view = fs.get_feature_view(
     version=1,
 )
 
-# Retreive model
+### Retreive model
 
 mr = project.get_model_registry()
 
@@ -39,7 +44,7 @@ saved_model_dir = retrieved_model.download()
 retrieved_xgboost_model = XGBRegressor()
 retrieved_xgboost_model.load_model(saved_model_dir + "/model.json")
 
-# Retrieve features 
+### Retrieve features 
 
 weather_fg = fs.get_feature_group(
     name='weather',
@@ -48,5 +53,50 @@ weather_fg = fs.get_feature_group(
 
 today_timestamp = pd.to_datetime(today)
 batch_data = weather_fg.filter(weather_fg.date >= today_timestamp ).read()
+
+
+### Predict and upload
+
 batch_data['predicted_pm25'] = retrieved_xgboost_model.predict(
     batch_data[['temperature_2m_mean', 'precipitation_sum', 'wind_speed_10m_max', 'wind_direction_10m_dominant']])
+
+batch_data['street'] = street
+batch_data['city'] = city
+batch_data['country'] = country
+# Fill in the number of days before the date on which you made the forecast (base_date)
+batch_data['days_before_forecast_day'] = range(1, len(batch_data)+1)
+batch_data = batch_data.sort_values(by=['date'])
+#batch_data['date'] = batch_data['date'].dt.tz_convert(None).astype('datetime64[ns]')
+
+plt = util.plot_air_quality_forecast(city, street, batch_data, file_path="./img/pm25_forecast.png")
+
+monitor_fg = fs.get_or_create_feature_group(
+    name='aq_predictions',
+    description='Air Quality prediction monitoring',
+    version=1,
+    primary_key=['city','street','date','days_before_forecast_day'],
+    event_time="date"
+)
+
+print(f"Batch data: {batch_data}")
+
+monitor_fg.insert(batch_data, write_options={"wait_for_job": True})
+monitoring_df = monitor_fg.filter(monitor_fg.days_before_forecast_day == 1).read()
+
+# Hindcast monitoring
+
+air_quality_fg = fs.get_feature_group(
+    name='air_quality',
+    version=1,
+)
+air_quality_df = air_quality_fg.read()
+
+outcome_df = air_quality_df[['date', 'pm25']]
+preds_df =  monitoring_df[['date', 'predicted_pm25']]
+hindcast_df = pd.merge(preds_df, outcome_df, on="date")
+hindcast_df = hindcast_df.sort_values(by=['date'])
+
+if len(hindcast_df) == 0:
+    hindcast_df = util.backfill_predictions_for_monitoring(weather_fg, air_quality_df, monitor_fg, retrieved_xgboost_model)
+
+plt = util.plot_air_quality_forecast(city, street, hindcast_df, file_path="./img/pm25_hindcast_1day.png", hindcast=True)

scheduler.py

@@ -0,0 +1,7 @@
+import modal
+
+app = modal.App('scheduler')
+
+@app.function(schedule=modal.Period(seconds=15))
+def update():
+    print('Updating...')