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| import gradio as gr | |
| import hopsworks | |
| import joblib | |
| import pandas as pd | |
| import numpy as np | |
| import folium | |
| import sklearn.preprocessing as proc | |
| import json | |
| import time | |
| from datetime import timedelta, datetime | |
| from branca.element import Figure | |
| from functions import decode_features, get_weather_data, get_weather_df, get_weather_json_quick | |
| ################## | |
| def greet(total_pred_days): | |
| str1 = "" | |
| if(total_pred_days == ""): | |
| return "Empty input" | |
| count = int(total_pred_days) | |
| if count > 14: | |
| str1 += "Warning: 14 days at most. " + '\n' | |
| count = 14 | |
| if count <0: | |
| str1 = "Invalid input." | |
| return str1 | |
| count = count + 1 | |
| X = pd.DataFrame() | |
| for i in range(count+1): | |
| # Get, rename column and rescale | |
| next_day_date = datetime.today() + timedelta(days=i) | |
| next_day = next_day_date.strftime ('%Y-%m-%d') | |
| json = get_weather_json_quick(next_day) | |
| temp = get_weather_data(json) | |
| X = X.append(temp, ignore_index=True) | |
| # X reshape | |
| X.drop('preciptype', inplace = True, axis = 1) | |
| X.drop('severerisk', inplace = True, axis = 1) | |
| X.drop('stations', inplace = True, axis = 1) | |
| X.drop('sunrise', inplace = True, axis = 1) | |
| X.drop('sunset', inplace = True, axis = 1) | |
| X.drop('moonphase', inplace = True, axis = 1) | |
| X.drop('description', inplace = True, axis = 1) | |
| X.drop('icon', inplace = True, axis = 1) | |
| X = X.drop(columns=["sunriseEpoch", "sunsetEpoch", "source", "datetimeEpoch"]).fillna(0) | |
| X = X.rename(columns={'pressure':'sealevelpressure'}) | |
| # Merge X and query | |
| #Y = X.append(Q, ignore_index=True) | |
| # Data scaling | |
| X = X.drop(columns = ['conditions', "datetime", "temp", "tempmax", "tempmin"]) | |
| category_cols = ['conditions'] | |
| cat_std_cols = ['feelslikemax','feelslikemin','feelslike','dew','humidity','precip','precipprob','precipcover','snow','snowdepth','windgust','windspeed','winddir','sealevelpressure','cloudcover','visibility','solarradiation','solarenergy','uvindex'] | |
| scaler_std = proc.StandardScaler() | |
| X.insert(19,"conditions",0) | |
| X.insert(0,"name",0) | |
| X[cat_std_cols] = scaler_std.fit_transform(X[cat_std_cols]) | |
| X[category_cols] = scaler_std.fit_transform(X[category_cols]) | |
| # Predict | |
| preds = model.predict(X[0:count]) | |
| preds1= model1.predict(X[0:count]) | |
| preds2= model2.predict(X[0:count]) | |
| for x in range(count): | |
| if (x != 0): | |
| str1 += (datetime.now() + timedelta(days=x)).strftime('%Y-%m-%d') + " predicted temperature: " +str(float(preds[len(preds) - count + x]))+ "\npredicted max temperature: " +str(float(preds1[len(preds1) - count + x]))+ "\npredicted min temperature: " +str(float(preds2[len(preds2) - count + x]))+"\n" | |
| return str1 | |
| ####################################################### | |
| # Preparations | |
| project = hopsworks.login() | |
| mr=project.get_model_registry() | |
| # fs = project.get_feature_store() | |
| # weather_fg = fs.get_or_create_feature_group( | |
| # name = 'weather_fg', | |
| # version = 1 | |
| # ) | |
| # | |
| # query = weather_fg.select_all() | |
| # Q = query.read() | |
| model = mr.get_model("temp_model_new", version=1) | |
| model_dir=model.download() | |
| model1 = mr.get_model("tempmax_model_new", version=1) | |
| model_dir1=model1.download() | |
| model2 = mr.get_model("tempmin_model_new", version=1) | |
| model_dir2=model2.download() | |
| model = joblib.load(model_dir + "/model_temp_new.pkl") | |
| model1 = joblib.load(model_dir1 + "/model_tempmax_new.pkl") | |
| model2 = joblib.load(model_dir2+ "/model_tempmin_new.pkl") | |
| ######################################################## | |
| # Gradio Interface | |
| #demo = gr.Interface(fn=greet, inputs = "text", outputs="text") | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| days = gr.Slider( | |
| label="How many days do you want to predict the temperature of? ", value=1, minimum=1, maximum=15, step=1 | |
| ) | |
| with gr.Column(): | |
| output = gr.Textbox( | |
| label="Predicted results: " | |
| ) | |
| days.change(greet, days, output) | |
| if __name__ == "__main__": | |
| demo.launch() | |