Update app.py

app.py

@@ -9,22 +9,11 @@ import json
 import time
 from datetime import timedelta, datetime
 from branca.element import Figure
+import gradio as gr
 
-from functions import decode_features, get_model
+from functions import decode_features, get_model, get_weather_df, get_weather_data
 
 
-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...')
-
 project = hopsworks.login()
 fs = project.get_feature_store()
 feature_view = fs.get_feature_view(
@@ -32,113 +21,41 @@ feature_view = fs.get_feature_view(
     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=1)
-start_time = int(start_date.timestamp()) * 1000
-
-# X = feature_view.get_batch_data(start_time=start_time)
-
-X = feature_view.get_batch_data(start_time=1673625600000)
-progress_bar.progress(50)
-
-print(X.date.values)
-
-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)
-print("X is \n %s" % X)
-
-data_to_display = decode_features(X, feature_view=feature_view)
-data_to_display["city"] = "Oslo"
-# print(data_to_display)
-
-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 = {("Oslo", "Norway"): [59.9139, 10.7522]}
-
-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="gradient_boost_model",
+                  model_name="air_quality_model",
                   evaluation_metric="f1_score",
-                  sort_metrics_by="max")
-
-preds = model.predict(X)
-
-cities = [city_tuple[0] for city_tuple in cities_coords.keys()]
-print("cities are %s" % cities)
-
-next_day_date = datetime.today() + timedelta(days=1)
-next_day = next_day_date.strftime ('%d/%m/%Y')
-print("preds is %s" % preds)
-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")
-# hi
+                  sort_metrics_by="max",
+                  version_name=1)
+
+
+def pred(name):
+    str1 = " "
+    for i in range(8):
+        target_date = datetime.today() + timedelta(days=i)
+        target_date = target_date.strftime('%Y-%m-%d')
+
+        data_weather = [get_weather_data('oslo',target_date)]
+        df_weather = get_weather_df(data_weather)
+        df_weather['conditions'] = df_weather['conditions'].replace(['Rain','Clear','Snow','Partially cloudy','Overcast','Snow, Partially cloudy',
+                                                             'Rain, Partially cloudy','Rain, Overcast','Snow, Overcast',
+                                                             'Snow, Freezing Drizzle/Freezing Rain, Overcast','Snow, Rain',
+                                                             'Snow, Rain, Freezing Drizzle/Freezing Rain, Ice, Overcast',
+                                                             'Snow, Rain, Freezing Drizzle/Freezing Rain, Overcast','Snow, Rain, Ice, Overcast',
+                                                             'Snow, Rain, Overcast','Snow, Rain, Partially cloudy'],[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15])
+        df_weather = df_weather.drop(columns=["date"]).fillna(0)
+        df_weather["aqi"] = 0
+        preds = model.predict(df_weather)
+        print(preds)
+        if(i!=0):
+            str1 += target_date + " predicted aqi:" + str(int(preds))+"\n"
+    
+    return str1
+            
+
+    # next_day_date = datetime.today() + timedelta(days=i)
+    # next_day = next_day_date.strftime ('%d/%m/%Y')
+
+demo = gr.Interface(fn=pred,title="Predict AQI of Oslo" ,inputs="text", outputs="text")
+
+if __name__ == "__main__":
+    demo.launch()