| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| import hopsworks | |
| import joblib | |
| import os | |
| project = hopsworks.login(api_key_value="B8TDkmcSyPyWFM2o.YuXEbXM7MUFk5gdBXFXsbMz24uZipqY4BttbZ9wIoZ0cn9vQd4bSWgj57vDGXqdh") | |
| mr = project.get_model_registry() | |
| model = mr.get_model("air_model_3", version=1) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/air_model3.pkl") | |
| def forecast(): | |
| fs = project.get_feature_store() | |
| feature_view = fs.get_feature_view( | |
| name = 'miami_data_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) | |
| train_data.head(5) | |
| 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) | |
| return y | |
| demo = gr.Interface( | |
| fn=forecast, | |
| title="Air Quality Prediction", | |
| description="Get aqi value", | |
| allow_flagging="never", | |
| inputs=[], | |
| outputs=gr.Textbox(label="Result: ")) | |
| demo.launch() |