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| import gradio as gr | |
| from datetime import timedelta, datetime | |
| import hopsworks | |
| import joblib | |
| from functions import * | |
| #Connect to hopsworks and get feature store | |
| project = hopsworks.login() | |
| fs = project.get_feature_store() | |
| #Function for the app | |
| def predict_weather(location): | |
| #Get future weather data | |
| weather_data1 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=1)).strftime("%Y-%m-%d"))]) | |
| weather_data2 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=2)).strftime("%Y-%m-%d"))]) | |
| weather_data3 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=3)).strftime("%Y-%m-%d"))]) | |
| weather_data4 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=4)).strftime("%Y-%m-%d"))]) | |
| weather_data5 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=5)).strftime("%Y-%m-%d"))]) | |
| weather_data6 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=6)).strftime("%Y-%m-%d"))]) | |
| weather_data7 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=7)).strftime("%Y-%m-%d"))]) | |
| weather_df = pd.concat([weather_data1, weather_data2, weather_data3, weather_data4, weather_data5, weather_data6, weather_data7], axis=0) | |
| weather_df = weather_df.drop(columns=["precipprob", "uvindex", "date", "city", "conditions"]).fillna(0) | |
| weather_df.rename( | |
| columns={"pressure": "sealevelpressure"}, inplace=True) | |
| print(weather_df) | |
| #Get model | |
| mr = project.get_model_registry() | |
| model = mr.get_model("gradient_boost_model5", version=1) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/model5.pkl") | |
| #Create predictions | |
| preds = model.predict(weather_df) | |
| print(preds) | |
| list_of_predictions = [] | |
| for x in range(7): | |
| list_of_predictions.append("Aqi on " + (datetime.now() + timedelta(days=x+1)).strftime('%Y-%m-%d') + ": " + str(int(preds[x]))) | |
| return list_of_predictions | |
| #Gradio interface | |
| demo = gr.Interface( | |
| fn=predict_weather, | |
| title="Future air quality predictor", | |
| description="Input the name of a location below to get future air quality predictions for that location", | |
| allow_flagging="never", | |
| inputs="text", | |
| outputs="text" | |
| ) | |
| demo.launch() |