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 def greet(name): project = hopsworks.login() mr = project.get_model_registry() #api = project.get_dataset_api() fs = project.get_feature_store() feature_view = fs.get_feature_view( name = 'hel_air_fv1', version = 1 ) # start_time = 1672614000000 # #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 = mr.get_model("gradient_boost_model",version = 4) model_dir = model.download() # 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 "model got" demo = gr.Interface(fn=greet, inputs="text", outputs="text") if __name__ == "__main__": demo.launch()