Create app.py

app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+import hopsworks
+import joblib
+import pandas as pd
+import numpy as np
+import folium
+import json
+import time
+from datetime import timedelta, datetime
+from branca.element import Figure
+
+from functions import decode_features, get_model
+
+def greet(name):
+
+    project = hopsworks.login()
+    api = project.get_dataset_api()
+    fs = project.get_feature_store()
+    feature_view = fs.get_feature_view(
+        name = 'hel_air_fv1',
+        version = 1
+    )
+
+    start_time = 1671058800000
+    #start_date = datetime.now() - timedelta(days=1)
+    #start_time = int(start_date.timestamp()) * 1000
+
+
+    X = feature_view.get_batch_data(start_time=start_time)
+    latest_date_unix = str(X.date.values[0])[:10]
+    latest_date = time.ctime(int(latest_date_unix))
+
+    X = X.drop(columns=["date"]).fillna(0)
+
+
+    model = get_model(project=project,
+                      model_name="gradient_boost_model",
+                      evaluation_metric="f1_score",
+                      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')
+ #   df = pd.DataFrame(data=preds[0], columns=[f"AQI Predictions for {next_day}"], dtype=int)
+    str1 = ""
+#    return int(preds[0])
+
+
+    for x in range(8):
+      if(x != 0):
+         str1 += (datetime.now() + timedelta(days=x)).strftime('%Y-%m-%d') + " predicted aqi:      " + str(int(preds[len(preds) - 8 + x]))+"\n"
+    
+    print(str1)
+    return str1
+
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+
+    
+if __name__ == "__main__":
+    demo.launch()