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Subida de archivos necesarios para mlops
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,123 +1,204 @@ | ||
| import streamlit as st | ||
| import joblib | ||
| import pandas as pd | ||
| import numpy as np | ||
| from sklearn.base import BaseEstimator, TransformerMixin | ||
| from sklearn.preprocessing import QuantileTransformer, StandardScaler | ||
| from sklearn.cluster import KMeans | ||
| from datetime import date, datetime | ||
| from funciones import season,divide_estaciones,rellenar,eliminar,rellenar_con_cols,eliminar_viento,dummies_elim,rellenar_ultimos,estandarizacion,preprocesamiento, agrego_columnas | ||
| from clases import NeuralNetworkClass, NeuralNetworkReg, MiniBatchGradientDescentRegressor | ||
| import pandas as pd | ||
| import numpy as np | ||
| from tensorflow_addons.metrics import RSquare | ||
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| pipe = joblib.load('regresion_logistica (2).pkl') | ||
| pipe_reg = joblib.load('gradiente-mini-batch (2).pkl') | ||
| #pipe_red_reg = joblib.load('red_neuronal_regresion1.pkl') | ||
| #pipe_red_clas = joblib.load('red_neuronal_clasificacion.pkl') | ||
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| def get_user_input(): | ||
| with st.form(key='my_form'): | ||
| submit_button = st.form_submit_button(label='Submit') | ||
| return submit_button | ||
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| st.title('TP Aprendizaje Automatico') | ||
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| #st.title('Cargar CSV') | ||
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| # Widget para cargar un archivo CSV | ||
| #file = st.file_uploader('Cargar archivo CSV', type=['csv']) | ||
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| #if file is not None: | ||
| # Leer el archivo CSV y mostrar los datos | ||
| # df = pd.read_csv(file) | ||
| # st.write('Datos del archivo CSV:') | ||
| # st.write(df) | ||
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| selected_date = st.date_input('Date', date.today()) | ||
| location = st.text_input('Location (Sydney, SydneyAirport, Canberra, Melbourne y MelbourneAirport)', 'Sidney') | ||
| a1 = st.slider('MinTemp', -20.0, 40.0, 5.0) | ||
| a2 = st.slider('MaxTemp', -10.0, 60.0, 5.0) | ||
| a3 = st.slider('Rainfall', 0.0, 500.0, 5.0) | ||
| a4 = st.slider('Evaporation', 0.0, 30.0, 5.0) | ||
| a5 = st.slider('Sunshine', 0.0, 14.0, 5.0) | ||
| dir = st.text_input('WindGustDir', 'N') | ||
| a6 = st.slider('WindGustSpeed', 0.0, 130.0, 5.0) | ||
| dir9 = st.text_input('WindDir9am', 'N') | ||
| dir3 = st.text_input('WindDir3pm', 'N') | ||
| a7 = st.slider('WindSpeed9am', 0.0, 130.0, 5.0) | ||
| a8 = st.slider('WindSpeed3pm', 0.0, 130.0, 5.0) | ||
| a9 = st.slider('Humidity9am', 0.0, 100.0, 5.0) | ||
| a10 = st.slider('Humidity3pm', 0.0, 100.0, 5.0) | ||
| a11 = st.slider('Pressure9am', 500.0, 1500.0, 5.0) | ||
| a12 = st.slider('Pressure3pm', 500.0, 1500.0, 5.0) | ||
| a13 = st.slider('Cloud9am', 0.0, 10.0, 5.0) | ||
| a14 = st.slider('Cloud3pm', 0.0, 10.0, 5.0) | ||
| a15 = st.slider('Temp9am', -20.0, 40.0, 5.0) | ||
| a16 = st.slider('Temp3pm', -10.0, 60.0, 5.0) | ||
| rain = st.text_input('RainToday (Yes, No)', 'No') | ||
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| submit_button = get_user_input() | ||
| # When the 'Submit' button is pressed, perform the prediction | ||
| if submit_button: | ||
|
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| # Crear el diccionario | ||
| data_para_predecir = { | ||
| 'Date': selected_date.strftime('%Y-%m-%d'), | ||
| 'Location': location, | ||
| 'MinTemp': a1, | ||
| 'MaxTemp': a2, | ||
| 'Rainfall': a3, | ||
| 'Evaporation': a4, | ||
| 'Sunshine': a5, | ||
| 'WindGustDir': dir, | ||
| 'WindGustSpeed': a6, | ||
| 'WindDir9am': dir9, | ||
| 'WindDir3pm': dir3, | ||
| 'WindSpeed9am': a7, | ||
| 'WindSpeed3pm': a8, | ||
| 'Humidity9am': a9, | ||
| 'Humidity3pm': a10, | ||
| 'Pressure9am': a11, | ||
| 'Pressure3pm': a12, | ||
| 'Cloud9am': a13, | ||
| 'Cloud3pm': a14, | ||
| 'Temp9am': a15, | ||
| 'Temp3pm': a16, | ||
| 'RainToday': rain | ||
| } | ||
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| data = pd.DataFrame([data_para_predecir]) | ||
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| # Predicciones lluvia | ||
| prediction = pipe.predict(data) | ||
| prediction_clas = prediction[0] | ||
| # Display the prediction | ||
| st.header("Rain Tomorrow?") | ||
| if prediction_clas == 1: | ||
| st.write('Yes') | ||
| else: | ||
| st.write('No') | ||
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| prediction = pipe_reg.predict(data) | ||
| prediction_reg = prediction[0] | ||
| # Display the prediction | ||
| st.header("Rainfall Tomorrow?") | ||
| st.write(prediction_reg) | ||
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| #prediction = pipe_red_reg.predict(data) | ||
| #prediction_red_reg = prediction[0] | ||
| # Display the prediction | ||
| #st.header("Regresion de una red neuronal") | ||
| #st.write(prediction_red_reg) | ||
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| #prediction = pipe_red_clas.predict(pd.DataFrame([data_para_predecir])) | ||
| #prediction_red_clas = prediction[0] | ||
| #Display the prediction | ||
| #st.header("Clasificacion de una red neuronal") | ||
| #st.write(prediction_red_clas) | ||
|
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||
| import streamlit as st | ||
| import joblib | ||
| import pandas as pd | ||
| import numpy as np | ||
| from sklearn.base import BaseEstimator, TransformerMixin | ||
| from sklearn.preprocessing import QuantileTransformer, StandardScaler | ||
| from sklearn.cluster import KMeans | ||
| from funciones import season,divide_estaciones,rellenar,eliminar,rellenar_con_cols,eliminar_viento,dummies_elim,rellenar_ultimos,estandarizacion,preprocesamiento, agrego_columnas, MiniBatchGradientDescentRegressor, CustomStandardScaler, NeuralNetworkReg, NeuralNetworkClass | ||
| from datetime import date, datetime | ||
| from keras.models import load_model | ||
| from sklearn.pipeline import Pipeline, make_pipeline | ||
| from tensorflow_addons.metrics import RSquare | ||
| from keras.models import model_from_json | ||
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| pipeline1 = joblib.load('pipeline.pkl') | ||
| pipeline2 = joblib.load('pipeline.pkl') | ||
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| pipe = joblib.load('regresion_logistica (2).pkl') | ||
| pipe_reg = joblib.load('gradiente-mini-batch (3).pkl') | ||
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| json_file = open("model_reg.json", 'r') | ||
| loaded_model_json = json_file.read() | ||
| json_file.close() | ||
| model = model_from_json(loaded_model_json) | ||
| model.load_weights("red_regresion.h5") | ||
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| pipeline_reg = pipeline1.set_params(Model = model) | ||
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| json_file = open("model_clas.json", 'r') | ||
| loaded_model_json = json_file.read() | ||
| json_file.close() | ||
| model_clas = model_from_json(loaded_model_json) | ||
| model_clas.load_weights("red_clasificacion (3).h5") | ||
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| pipeline_clas = pipeline2.set_params(Model = model_clas) | ||
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| def get_user_input(): | ||
| input_dict = {} | ||
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| with st.form(key='my_form'): | ||
|
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| selected_date = st.date_input('Date', date.today()) | ||
| location = st.text_input('Location (Sydney, SydneyAirport, Canberra, Melbourne y MelbourneAirport)', 'Sidney') | ||
| a1 = st.slider('MinTemp', -20.0, 40.0, 22.0) | ||
| a2 = st.slider('MaxTemp', -10.0, 60.0, 27.0) | ||
| a3 = st.slider('Rainfall', 0.0, 500.0, 5.0) | ||
| a4 = st.slider('Evaporation', 0.0, 30.0, 15.0) | ||
| a5 = st.slider('Sunshine', 0.0, 14.0, 3.6) | ||
| wind_gust_dir = st.text_input('WindGustDir', 'S') | ||
| a6 = st.slider('WindGustSpeed', 0.0, 130.0, 54.0) | ||
| wind_dir_9am = st.text_input('WindDir9am', 'SE') | ||
| wind_dir_3pm = st.text_input('WindDir3pm', 'S') | ||
| a7 = st.slider('WindSpeed9am', 0.0, 130.0, 17.0) | ||
| a8 = st.slider('WindSpeed3pm', 0.0, 130.0, 43.0) | ||
| a9 = st.slider('Humidity9am', 0.0, 100.0, 56.0) | ||
| a10 = st.slider('Humidity3pm', 0.0, 100.0, 92.0) | ||
| a11 = st.slider('Pressure9am', 500.0, 1500.0, 1014.0) | ||
| a12 = st.slider('Pressure3pm', 500.0, 1500.0, 1017.0) | ||
| a13 = st.slider('Cloud9am', 0.0, 10.0, 6.0) | ||
| a14 = st.slider('Cloud3pm', 0.0, 10.0, 8.0) | ||
| a15 = st.slider('Temp9am', -20.0, 40.0, 26.4) | ||
| a16 = st.slider('Temp3pm', -10.0, 60.0, 20.7) | ||
| rain_today = st.text_input('RainToday (Yes, No)', 'No') | ||
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| input_dict = { | ||
| 'Date': selected_date.strftime('%Y-%m-%d'), | ||
| 'Location': location, | ||
| 'MinTemp': a1, | ||
| 'MaxTemp': a2, | ||
| 'Rainfall': a3, | ||
| 'Evaporation': a4, | ||
| 'Sunshine': a5, | ||
| 'WindGustDir': wind_gust_dir, | ||
| 'WindGustSpeed': a6, | ||
| 'WindDir9am': wind_dir_9am, | ||
| 'WindDir3pm': wind_dir_3pm, | ||
| 'WindSpeed9am': a7, | ||
| 'WindSpeed3pm': a8, | ||
| 'Humidity9am': a9, | ||
| 'Humidity3pm': a10, | ||
| 'Pressure9am': a11, | ||
| 'Pressure3pm': a12, | ||
| 'Cloud9am': a13, | ||
| 'Cloud3pm': a14, | ||
| 'Temp9am': a15, | ||
| 'Temp3pm': a16, | ||
| 'RainToday': rain_today, | ||
| 'RainTomorrow': np.nan, | ||
| 'RainfallTomorrow': np.nan | ||
| } | ||
|
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| user_imput = pd.DataFrame([input_dict]) | ||
|
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| submit_button = st.form_submit_button(label='Submit') | ||
| st.write(user_imput) | ||
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| return user_imput, submit_button | ||
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| st.title('TP Aprendizaje Automatico') | ||
|
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| st.title('Cargar CSV') | ||
|
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| #Widget para cargar un archivo CSV | ||
| file = st.file_uploader('Cargar archivo CSV', type=['csv']) | ||
|
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| if file is not None: | ||
| # Leer el archivo CSV y mostrar los datos | ||
| df = pd.read_csv(file) | ||
| st.write('Datos del archivo CSV:') | ||
| # Lista de opciones | ||
| options = range(len(df)) | ||
| # Selección del usuario | ||
| selected_option = st.selectbox('Seleccione un número', options, 0) | ||
| st.write(df.iloc[selected_option, : ]) | ||
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| # Predict csv | ||
| prediction = pipe.predict(df) | ||
| prediction_clas_csv = prediction[selected_option] | ||
| # Display the prediction | ||
| st.header("Rain Tomorrow?") | ||
| if prediction_clas_csv == 1: | ||
| st.write('Yes') | ||
| else: | ||
| st.write('No') | ||
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| prediction = pipe_reg.predict(df) | ||
| prediction_reg_csv = prediction[selected_option] | ||
| # Display the prediction | ||
| st.header("Rainfall Tomorrow?") | ||
| st.write(str(prediction_reg_csv[0]), ' mm') | ||
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| prediction = pipeline_clas.predict(df) | ||
| prediction_red_clas = prediction[selected_option] | ||
| #Display the prediction | ||
| st.header("Clasificacion de la red neuronal") | ||
| if prediction_red_clas > 0.5: | ||
| st.write('Llovera') | ||
| else: | ||
| st.write('No llovera') | ||
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| prediction = pipeline_reg.predict(df) | ||
| prediction_red_reg = prediction[selected_option] | ||
| #Display the prediction | ||
| st.header("Regresion de la red neuronal") | ||
| st.write(str(prediction_red_reg[0]), ' mm') | ||
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| #USUARIO | ||
| user_imput, submit_button = get_user_input() | ||
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| numeric = ['MinTemp', 'MaxTemp', 'Rainfall', 'Humidity3pm','Pressure3pm','Sunshine','WindGustSpeed','Cloud3pm','Evaporation'] | ||
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| if submit_button: | ||
| #st.write("Preprocesamiento:") | ||
| #df2 = preprocesamiento(user_imput) | ||
| #df2[numeric] = scaler.transform(df2[numeric]) | ||
| #st.write(df2) | ||
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| # Prediccion | ||
| prediction = pipe.predict(user_imput) | ||
| prediction_clas = prediction[0] | ||
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| prediction = pipe_reg.predict(user_imput) | ||
| prediction_reg = prediction[0] | ||
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| prediction = pipeline_clas.predict(user_imput) | ||
| prediction_red_clas = prediction[0] | ||
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| prediction = pipeline_reg.predict(user_imput) | ||
| prediction_red_reg = prediction[0] | ||
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| # Display the prediction | ||
| st.header("Rain Tomorrow?") | ||
| if prediction_clas == 1: | ||
| st.write('Yes') | ||
| else: | ||
| st.write('No') | ||
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| # Display the prediction | ||
| st.header("Rainfall Tomorrow?") | ||
| st.write(str(prediction_reg[0]), ' mm') | ||
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| #Display the prediction | ||
| st.header("Clasificacion de la red neuronal") | ||
| if prediction_red_clas > 0.5: | ||
| st.write('Llovera') | ||
| else: | ||
| st.write('No llovera') | ||
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| #Display the prediction | ||
| st.header("Regresion de la red neuronal") | ||
| st.write(str(prediction_red_reg[0]), ' mm') | ||
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