Upload 2 files

app.py

@@ -1,143 +1,57 @@
-import streamlit as st
+import gradio as gr
+import numpy as np
+from PIL import Image
+import requests
 import hopsworks
 import joblib
-import pandas as pd
-
-import numpy as np
-import folium
-from streamlit_folium import st_folium, folium_static
-import json
-import time
-from datetime import timedelta, datetime
-from branca.element import Figure
-
-from functions import decode_features, get_model
-
-
-def fancy_header(text, font_size=24):
-    res = f'<span style="color:#ff5f27; font-size: {font_size}px;">{text}</span>'
-    st.markdown(res, unsafe_allow_html=True )
-
-
-st.title('⛅️Air Quality Prediction Project🌩')
-
-progress_bar = st.sidebar.header('⚙️ Working Progress')
-progress_bar = st.sidebar.progress(0)
-st.write(36 * "-")
-fancy_header('\n📡 Connecting to Hopsworks Feature Store...')
-
+import os
+from datetime import datetime, timedelta
 project = hopsworks.login()
-fs = project.get_feature_store()
-feature_view = fs.get_feature_view(
-    name = 'miami_air_quality_fv',
-    version = 1
-)
-
-
-st.write("Successfully connected!✔️")
-progress_bar.progress(20)
-
-st.write(36 * "-")
-fancy_header('\n☁️ Getting batch data from Feature Store...')
-
-start_date = datetime.now() - timedelta(days=4) # date today minus 2023-1-10
-start_time = int(start_date.timestamp()) * 1000
-
-X = feature_view.get_batch_data(start_time=start_time)
-progress_bar.progress(50)
-
-latest_date_unix = str(X.date.values[0])[:10]
-latest_date = time.ctime(int(latest_date_unix))
-
-st.write(f"⏱ Data for {latest_date}")
-
-X = X.drop(columns=["date"]).fillna(0)
-
-data_to_display = decode_features(X, feature_view=feature_view)
-
-progress_bar.progress(60)
-
-st.write(36 * "-")
-fancy_header(f"🗺 Processing the map...")
-
-fig = Figure(width=550,height=350)
-
-my_map = folium.Map(location=[58, 20], zoom_start=3.71)
-fig.add_child(my_map)
-folium.TileLayer('Stamen Terrain').add_to(my_map)
-folium.TileLayer('Stamen Toner').add_to(my_map)
-folium.TileLayer('Stamen Water Color').add_to(my_map)
-folium.TileLayer('cartodbpositron').add_to(my_map)
-folium.TileLayer('cartodbdark_matter').add_to(my_map)
-folium.LayerControl().add_to(my_map)
-
-data_to_display = data_to_display[["city", "temp", "humidity",
-                                            "conditions", "aqi"]]
-
-# cities_coords = {("Sundsvall", "Sweden"): [62.390811, 17.306927],
-#                  ("Stockholm", "Sweden"): [59.334591, 18.063240],
-#                  ("Malmo", "Sweden"): [55.604981, 13.003822]}
-cities_coords = {("Miami", "USA"): [25.761681, -80.191788]}
-
-# if "Kyiv" in data_to_display["city"]:
-#     cities_coords[("Kyiv", "Ukraine")]: [50.450001, 30.523333]
-
-data_to_display = data_to_display.set_index("city")
-
-cols_names_dict = {"temp": "Temperature",
-                   "humidity": "Humidity",
-                   "conditions": "Conditions",
-                   "aqi": "AQI"}
-
-data_to_display = data_to_display.rename(columns=cols_names_dict)
-
-cols_ = ["Temperature", "Humidity", "AQI"]
-data_to_display[cols_] = data_to_display[cols_].apply(lambda x: round(x, 1))
-
-for city, country in cities_coords:
-    text = f"""
-            <h4 style="color:green;">{city}</h4>
-            <h5 style="color":"green">
-                <table style="text-align: right;">
-                    <tr>
-                        <th>Country:</th>
-                        <td><b>{country}</b></td>
-                    </tr>
-                    """
-    for column in data_to_display.columns:
-        text += f"""
-                    <tr>
-                        <th>{column}:</th>
-                        <td>{data_to_display.loc[city][column]}</td>
-                    </tr>"""
-    text += """</table>
-                    </h5>"""
-
-    folium.Marker(
-        cities_coords[(city, country)], popup=text, tooltip=f"<strong>{city}</strong>"
-    ).add_to(my_map)
-
-
-# call to render Folium map in Streamlit
-folium_static(my_map)
-progress_bar.progress(80)
-st.sidebar.write("-" * 36)
-
-
-model = get_model(project=project,
-                  model_name="xgboost_model",
-                  evaluation_metric="f1",
-                  sort_metrics_by="max")
-
-preds = model.predict(X)
-
-cities = [city_tuple[0] for city_tuple in cities_coords.keys()]
-
-next_day_date = datetime.today() + timedelta(days=1)
-next_day = next_day_date.strftime ('%d/%m/%Y')
-print('-------', preds, '------', X, '--------')
-df = pd.DataFrame(data=preds, index=cities, columns=[f"AQI Predictions for {next_day}"], dtype=int)
 
-st.sidebar.write(df)
-progress_bar.progress(100)
-st.button("Re-run")
+mr = project.get_model_registry()
+model = mr.get_model("xgboost_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/model.pkl")
+
+
+
+
+def forecast():
+    
+    fs = project.get_feature_store() 
+    feature_view = fs.get_feature_view(
+        name = 'miami_air_quality_fv',
+        version = 1
+    )
+    train_data = feature_view.get_training_data(1)[0]
+    train_data = train_data.drop(labels = 'city_y',axis =1)
+    train_data = train_data.rename(columns = {'city_x':'city'})
+    train_data = train_data.sort_values(by="date", ascending=True).reset_index(drop=True)
+    train_data["aqi_next_day"] = train_data.groupby('city')['aqi'].shift(1)
+
+    X = train_data.drop(columns=["date"]).fillna(0)
+    y = X.pop("aqi_next_day")
+    X = X.drop(columns =['city','conditions']).fillna(0)
+    
+    today_data = X[1:2]
+    y = model.predict(today_data)
+
+    res = int(y[0])
+    return res
+    
+date_today = datetime.now()
+day = timedelta(days = 1)
+date_today = date_today + day
+date_today = date_today.strftime("%Y-%m-%d")
+output_label = date_today + " 's air quality is "
+
+demo = gr.Interface(
+    fn=forecast,
+    title="Air Quality Prediction",
+    description="Get aqi value",
+    allow_flagging="never",
+    inputs=[],
+    outputs=gr.Textbox(label=output_label))
+
+
+demo.launch()

requirements.txt

@@ -6,6 +6,5 @@ numpy==1.23.5
 pandas==1.5.2
 python-dotenv==0.21.0
 requests==2.28.1
-streamlit==1.17.0
-streamlit_folium==0.10.0
-xgboost==0.90
+xgboost==0.90
+gradio