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| 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() | |
| fs = project.get_feature_store() | |
| feature_view = fs.get_feature_view( | |
| name = 'hel_air_fv1', | |
| version = 1 | |
| ) | |
| start_time = 1668985200000 | |
| #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() |