:testing streamlit now

.gitignore

@@ -1,4 +1,5 @@
 .venv
+.venv_old
 .env
 .cache.sqlite
-__pycache__/
+__pycache__/

app.py

@@ -1,34 +1,37 @@
-import gradio as gr
+import streamlit as st
 import pandas as pd
 import numpy as np
-import random
-import os 
-import hopsworks
-# from gradio_datetimerange import DateTimeRange
 
-from datetime import datetime, timedelta
-now = datetime.now()
+st.title('Lahore Air Quality!')
+st.subheader('Particle matter, diameter < 2.5 micrometers (PM2.5)')
+
+### Load data
+
+import datetime
+import pandas as pd
+import hopsworks
+import datetime 
+from functions import util
+import os
+import pandas as pd
+import modal
 
-api_key = os.getenv('HOPSWORKS_API_KEY')
-project_name = os.getenv('HOPSWORKS_PROJECT')
+app = modal.App('scheduler')
+volume = modal.Volume.from_name("my-volume")
 
-project = hopsworks.login(project=project_name, api_key_value=api_key)
-fs = project.get_feature_store() 
+@app.function(schedule=modal.Period(seconds=10))
+def update():
+    print('Updating...')
+    
 
-air_quality_fg = fs.get_feature_group(
-    name='air_quality',
-    version=1,
-)
-air_quality_df = air_quality_fg.read()
-air_quality_df
 
-print(air_quality_df.info())
-print(air_quality_df)
 
-with gr.Blocks() as demo:
-    gr.Markdown("Helsingborg Air Quality Forecast")
-    # daterange = DateTimeRange(["now - 24h", "now"])
-    plot = gr.LinePlot(air_quality_df, x="date", y="pm25")
-    # daterange.bind([plot])
+if __name__ == "__main__":
+    if "df" not in st.session_state:
+        st.session_state.df = pd.DataFrame(np.random.randn(20, 2), columns=["x", "y"])
+    else:
+        st.session_state.df = pd.DataFrame(
+            np.random.randn(20, 3),
+            columns=['a', 'b', 'c'])
 
-demo.launch()   
+    st.line_chart(st.session_state.df)

app3.py

@@ -0,0 +1,87 @@
+import pandas as pd
+from random import randint, random
+import gradio as gr
+
+
+temp_sensor_data = pd.DataFrame(
+    {
+        "time": pd.date_range("2021-01-01", end="2021-01-05", periods=200),
+        "temperature": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],
+        "humidity": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],
+        "location": ["indoor", "outdoor"] * 100,
+    }
+)
+
+food_rating_data = pd.DataFrame(
+    {
+        "cuisine": [["Italian", "Mexican", "Chinese"][i % 3] for i in range(100)],
+        "rating": [random() * 4 + 0.5 * (i % 3) for i in range(100)],
+        "price": [randint(10, 50) + 4 * (i % 3) for i in range(100)],
+        "wait": [random() for i in range(100)],
+    }
+)
+
+with gr.Blocks() as line_plots:
+    with gr.Row():
+        start = gr.DateTime("2021-01-01 00:00:00", label="Start")
+        end = gr.DateTime("2021-01-05 00:00:00", label="End")
+        apply_btn = gr.Button("Apply", scale=0)
+    with gr.Row():
+        group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by")
+        aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation")
+
+    temp_by_time = gr.LinePlot(
+        temp_sensor_data,
+        x="time",
+        y="temperature",
+    )
+    temp_by_time_location = gr.LinePlot(
+        temp_sensor_data,
+        x="time",
+        y="temperature",
+        color="location",
+    )
+
+    time_graphs = [temp_by_time, temp_by_time_location]
+    group_by.change(
+        lambda group: [gr.LinePlot(x_bin=None if group == "None" else group)] * len(time_graphs),
+        group_by,
+        time_graphs
+    )
+    aggregate.change(
+        lambda aggregate: [gr.LinePlot(y_aggregate=aggregate)] * len(time_graphs),
+        aggregate,
+        time_graphs
+    )
+
+    def rescale(select: gr.SelectData):
+        return select.index
+    rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])
+
+    for trigger in [apply_btn.click, rescale_evt.then]:
+        trigger(
+            lambda start, end: [gr.LinePlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs
+        )
+
+    price_by_cuisine = gr.LinePlot(
+        food_rating_data,
+        x="cuisine",
+        y="price",
+    )
+    with gr.Row():
+        price_by_rating = gr.LinePlot(
+            food_rating_data,
+            x="rating",
+            y="price",
+        )
+        price_by_rating_color = gr.LinePlot(
+            food_rating_data,
+            x="rating",
+            y="price",
+            color="cuisine",
+            color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
+        )
+
+if __name__ == "__main__":
+    line_plots.launch()
+    

functions/util.py

@@ -15,6 +15,10 @@ import hopsworks
 import hsfs
 from pathlib import Path
 
+import sys
+print(sys.path)
+
+
 def get_historical_weather(city, start_date,  end_date, latitude, longitude):
     # latitude, longitude = get_city_coordinates(city)
 

requirements.txt

@@ -1,4 +1,5 @@
-gradio-datetimerange
-gradio
+#gradio-datetimerange
+#gradio
 pandas
-hopsworks
+hopsworks
+streamlit

scheduler.py

@@ -5,3 +5,41 @@ app = modal.App('scheduler')
 @app.function(schedule=modal.Period(seconds=10))
 def update():
     print('Updating...')
+    
+    # Send dataframe to Streamlit frontend
+    
+
+@app.function()
+def predict():
+    
+    # Extract features
+
+    # Run model
+
+    print('Predicting...')
+
+
+"""
+project = hopsworks.login(project=project_name, api_key_value=api_key)
+fs = project.get_feature_store() 
+secrets = util.secrets_api(project.name)
+
+AQI_API_KEY = secrets.get_secret("AQI_API_KEY").value
+location_str = secrets.get_secret("SENSOR_LOCATION_JSON").value
+location = json.loads(location_str)
+
+country=location['country']
+city=location['city']
+street=location['street']
+aqicn_url=location['aqicn_url']
+latitude=location['latitude']
+longitude=location['longitude']
+
+today = datetime.date.today()
+
+# Retrieve feature groups
+air_quality_fg = fs.get_feature_group(
+    name='air_quality',
+    version=1,
+)
+"""