def run():
st.sidebar.title("Machine Learning Model Selector")
add_model_selectbox = st.sidebar.selectbox(
"Pick a prediction model",
("Linear Regression", "Huber Regression", "Bayesian Ridge", "Blended Model"))
if add_model_selectbox == "Linear Regression":
model = load_model('Final_LR_Model_05_Dec2020')
elif add_model_selectbox == "Huber Regression":
model = load_model('Final_huber_Model_05Dec2020')
elif add_model_selectbox == "Bayesian Ridge":
model = load_model('Final_br_Model_05Dec2020')
elif add_model_selectbox == "Blended Model":
model = load_model('Final_top3_Model_05Dec2020')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
st.sidebar.success('This app was developed using the following compilation of US education data: https://www.kaggle.com/noriuk/us-education-datasets-unification-project') # CHANGE LATER
st.title("Explore US Education Data")
st.markdown('Select a model from the sidebar dropdown menu to generate a prediction for Grade 4 Reading Score below. This app can be used to explore the relationship between various measures of revenue and expenditure and levels of reading score attainment.')
st.info('Move the sliders and click Predict to generate a Reading Score prediction')
STATE = st.text_input("Enter ONE full state name, example ARIZONA, CALIFORINIA, WYOMING, etc.")
YEAR = st.slider('Year', 1986, 2020, 1995, 1)
ENROLL = st.slider('Enter the number of students enrolled', 0, 1000000, 500000, 100)
FEDERAL_REVENUE = st.slider('Enter the amount of Federal Revenue received', 0, 11000000, 5500000, 100)
STATE_REVENUE = st.slider('Enter the amount of State revenue received', 0, 11000000, 5500000, 100)
LOCAL_REVENUE = st.slider('Enter the amount of Local revenue received', 0, 11000000, 5500000, 100)
INSTRUCTION_EXPENDITURE = st.slider('Enter the amount of expenses in instruction', 0, 11000000, 5500000, 100)
SUPPORT_SERVICES_EXPENDITURE = st.slider("Enter the amount of Support Services expenditure", 0, 11000000, 5500000, 100)
OTHER_EXPENDITURE = st.slider("Enter the amount of expenditure classed as 'Other'", 0, 11000000, 5500000, 100)
CAPITAL_OUTLAY_EXPENDITURE = st.slider("Enter the amount of Capital Outlay expenditure", 0, 11000000, 5500000, 100)
output=""
input_dict = {'STATE' : STATE,
'YEAR' : YEAR,
'ENROLL' : ENROLL,
'FEDERAL_REVENUE' : FEDERAL_REVENUE,
'STATE_REVENUE' : STATE_REVENUE,
'LOCAL_REVENUE' : LOCAL_REVENUE,
'INSTRUCTION_EXPENDITURE': INSTRUCTION_EXPENDITURE,
'SUPPORT_SERVICES_EXPENDITURE': SUPPORT_SERVICES_EXPENDITURE,
'OTHER_EXPENDITURE': OTHER_EXPENDITURE,
'CAPITAL_OUTLAY_EXPENDITURE': CAPITAL_OUTLAY_EXPENDITURE}
input_df = pd.DataFrame([input_dict])
if st.button("Predict"):
output = predict(model=model, input_df=input_df)
output = str(output)
st.success('Predicted Grade 4 reading score: {}'.format(output))
def predict():
predics=load_model('Xgb_Model')
json_data=flask.request.json
a=pd.DataFrame.from_dict(json_data,orient='index')
b=pd.DataFrame.transpose(a)
prediction=predict_model(predics,data=b)
return str(prediction['Label'][0])
def dealing_with_scale_regressor(df):
columns = ['Ativos', 'ANR', 'Falhas', 'Instalações', 'fiveStars', 'fourStars',
'threeStars', 'twoStars', 'oneStar', 'total', 'score',
'Aquisição de Usuários', 'Nota Média',
'Category_FINANCE','Category_FOODS_AND_DRINK','Category_MAPS_AND_NAVIGATION', 'Category_SHOPPING']
_df = df[columns]
#x_file = open(os.path.join("modelos/", "model_scale.pkl"), "rb")
regressor = load_model("modelos/model_scale_sem_desinstalacoes")
predictions = predict_model(regressor,_df)
return predictions['Label'].iloc[0].astype(int)
Inc_Max = st.sidebar.number_input('Inclinacion Máxima De Todo El Pozo (Deg)',
min_value=0,value=3,max_value=100)
# Comenzamos con el tratamiento de la data de entrada
Data = pd.DataFrame( {"Num BHA": SL_BHA,
"MD": MD,
"TVD": TVD,
"DLS Mean": DLS_Mean,
"Azi Mean": Azi_Mean,
"Inc Max" : Inc_Max,
"Azi Max": Azi_Max,
"MW": SL_MW,
"Duracion": Duracion*24,
"Tipo Pozo": SB_Pozo,
},
index=[0]
)
# load the model from disk
model = load_model('Model_NPT')
# Realizar la Prediccion
y_pred = predict_model(model,data=Data).Label
st.subheader("El NPT Total De Tu Pozo Será: %.3f (Hrs)" % y_pred)
image = Image.open('Image.jpg')
st.image(image)
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('deployment_12082020')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('logo.png')
image_hospital = Image.open('hospital.jpg')
st.image(image,use_column_width=False)
add_selectbox = st.sidebar.selectbox(
"How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict patient hospital charges')
st.sidebar.success('https://www.pycaret.org')
st.sidebar.image(image_hospital)
st.title("Insurance Charges Prediction App")
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('deployment_24102020')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('logo.png')
st.image(image, use_column_width=False)
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict insurance premium charges')
st.sidebar.success('https://www.kvssetty.com')
st.title("Insurance Charges Prediction App")
if add_selectbox == 'Online':
import streamlit as st
import pandas as pd
import numpy as np
import base64
import io
import requests
def concat(*args):
strs = [str(arg) for arg in args if not pd.isnull(arg)]
return ''.join(strs) if strs else np.nan
np_concat = np.vectorize(concat)
model = load_model('modelifoods')
#### Funciones para predecir y descargar ####
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def get_table_download_link(df, filename, linkname):
"""Generates a link allowing the data in a given panda dataframe to be downloaded
in: dataframe
out: href string
"""
import ML_streamlit_app as app
from pycaret.regression import load_model
import pandas as pd
model = load_model('./models/lr_deployment_20210521')
input_dict = {
'age': 35,
'sex': 'M',
'bmi': 15,
'children': 1,
'smoker': 'yes',
'region': 'nortwest'
}
input_df = pd.DataFrame([input_dict])
class TestMLapp:
def test_predict(self):
assert 1000 < app.predict(model=model, input_df=input_df)
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('deployment_pycaretinsuranceMLapp_19062020')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('Pascal.png')
image_hospital = Image.open('hospital.jpg')
st.image(image, use_column_width=False)
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict patient hospital charges')
st.sidebar.success('https://www.pycaret.org')
st.sidebar.image(image_hospital)
st.title("Insurance Charges Prediction App")
# 1. Library imports
import pandas as pd
from pycaret.regression import load_model, predict_model
from fastapi import FastAPI
import uvicorn
# 2. Create the app object
app = FastAPI()
#. Load trained Pipeline
model = load_model('diamond-pipeline')
# Define predict function
@app.post('/predict')
def predict(carat_weight, cut, color, clarity, polish, symmetry, report):
data = pd.DataFrame(
[[carat_weight, cut, color, clarity, polish, symmetry, report]])
data.columns = [
'Carat Weight', 'Cut', 'Color', 'Clarity', 'Polish', 'Symmetry',
'Report'
]
predictions = predict_model(model, data=data)
return {'prediction': int(predictions['Label'][0])}
if __name__ == '__main__':
uvicorn.run(app, host='127.0.0.1', port=8000)
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model("deploy_some_model")
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df["Label"][0]
return predictions
def run():
from PIL import Image
image = Image.open("picture.jfif")
st.image(image, use_column_width=False)
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
st.sidebar.info("This app is to predict the insurance bill")
st.sidebar.success("Some other text")
st.title("The insurance app")
#!/usr/bin/env python
# coding: utf-8
# In[17]:
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('hp_pyc_deployment_07122020')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('logo1.PNG')
image_hospital = Image.open('house.jpeg')
st.image(image, use_column_width=False)
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
st.sidebar.info(
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('FinalModel')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image_hospital = Image.open('hospital.jpg')
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict patient hospital charges')
st.sidebar.success('https://www.pycaret.org')
st.sidebar.image(image_hospital)
st.title("Insurance Charges Prediction App")
if add_selectbox == 'Online':
desc_NumRotatableBonds,
desc_AromaticProportion])
if i == 0:
baseData = row
else:
baseData = np.vstack([baseData, row])
i = i + 1
columnNames = ["MolLogP", "MolWt", "NumRotatableBonds", "AromaticProportion"]
descriptors = pd.DataFrame(data=baseData, columns=columnNames)
return descriptors
model = load_model("WaterSolubility 19-JAN-21")
########################################################################################################################
#Creating Graphical User Interface
########################################################################################################################
st.write("""
# Molecular Solubility Prediction Web App
This WebApp predicts the **Solubility (LogS)** values of molecules!""")
st.image(PIL.Image.open("banner-project.jpg"), width=600)
st.header("Input Smile Format of Molecule\n Multiple Entries are followed by new line")
SMILES_input = "NCCC\nCN"
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('deployment_01012021')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('logo.png')
image_hospital = Image.open('hospital.jpg')
st.image(image, use_column_width=False)
add_selectbox = st.sidebar.selectbox('How would you like to predict?',
('Online', 'Batch'))
st.sidebar.info('This app is created to predict patient hospital charges')
st.sidebar.success('https://www.pycaret.org')
st.sidebar.success('http://justinboggs.us')
st.sidebar.image(image_hospital)
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('final_project_bengaluru')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('banglore_1.jpg')
image_office = Image.open('banglore_2.jpg')
st.image(image,use_column_width=True)
add_selectbox = st.sidebar.selectbox(
"How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict the house prices at various locations in Bengaluru')
st.sidebar.success('https://www.pycaret.org')
st.sidebar.image(image_office)
st.title("Predicting House Prices")
if add_selectbox == 'Online':
location=st.selectbox('location', ['Electronic City Phase II', 'Chikka Tirupathi', 'Uttarahalli',
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
# Load Model
model = load_model('deployment_11102020')
def run():
from PIL import Image
image_hospital = Image.open('hospital.jpg')
st.sidebar.info('This app is created using PyCaret and Strealit')
st.sidebar.success('https://youtube.com/KunaalNaik')
st.sidebar.image(image_hospital)
st.title('Insurance Application')
# Capture
age = st.number_input('Age', min_value=1, max_value=100, value=21)
sex = st.selectbox('Sex', ['male', 'female'])
bmi = st.number_input('BMI', min_value=10, max_value=50, value=10)
children = st.selectbox('Children', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
if st.checkbox('Smoker'):
smoker = 'yes'
else:
smoker = 'no'
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('deployment_2_07052020')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
#from PIL import Image
#image = Image.open('logo.png')
#image_hospital = Image.open('hospital.jpg')
#st.image(image,use_column_width=False)
add_selectbox = st.sidebar.selectbox(
"How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict car mileage')
#st.sidebar.success('https://www.pycaret.org')
#st.sidebar.image(image_hospital)
st.title("Car Mileage Prediction App")
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model("deployment_28042020")
def video_youtube(src: str = "https://www.youtube.com/embed/B2iAodr0fOo",
width="100%",
height=315):
"""An extension of the video widget
Arguments:
src {str} -- A youtube url like https://www.youtube.com/embed/B2iAodr0fOo
Keyword Arguments:
width {str} -- The width of the video (default: {"100%"})
height {int} -- The height of the video (default: {315})
"""
st.write(
f'<iframe width="{width}" height="{height}" src="{src}" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>',
unsafe_allow_html=True,
)
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df["Label"][0]
return predictions
def run():
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('deploy_model')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('logo.png')
image_hospital = Image.open('hospital.jpg')
st.image(image, use_column_width=True)
html_temp = """
<div style="background-color:midnightblue;padding:1.5px">
<h1 style="color:white;text-align:center;">Health Bucks </h1>
</div><br>"""
st.markdown(html_temp, unsafe_allow_html=True)
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('deployment_28042020')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('logo.png')
image_hospital = Image.open('hospital.jpg')
st.image(image, use_column_width=False)
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict patient hospital charges')
st.sidebar.success('https://www.pycaret.org')
st.sidebar.image(image_hospital)
st.title("Insurance Charges Prediction App")
import streamlit as st
import pandas as pd
from pycaret.regression import load_model, predict_model
import numpy as np
import seaborn as sns
import statsmodels.formula.api as smf
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
model = load_model('catboost 2_10_2021')
st.write("""
# Simple Tips Prediction App
""")
##import image
from PIL import Image
image = Image.open('picmoney.jpg')
st.image(image)
st.sidebar.header('User Input Parameters')
def user_input_features():
total_bill = st.sidebar.slider('total_bill',
min_value=1,
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
model = load_model('deployment')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
add_selectbox = st.sidebar.selectbox(
"How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict patient hospital charges')
st.title("Insurance Charges Prediction App")
if add_selectbox == 'Online':
age = st.number_input('Age', min_value=1, max_value=100, value=25)
sex = st.selectbox('Sex', ['male', 'female'])
bmi = st.number_input('BMI', min_value=10, max_value=50, value=10)
children = st.selectbox('Children', [0,1,2,3,4,5,6,7,8,9,10])
if st.checkbox('Smoker'):
smoker = 'yes'
else:
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model=load_model('deployment_20200805')
def predict(model, input_df):
prediction_df=predict_model(estimator=model, data=input_df)
predictions=prediction_df['Label'][0]
return predictions
def run():
from PIL import Image
image=Image.open('logo.png')
image_hospital = Image.open('hospital.jpg')
st.image(image,use_column_width=False)
add_selectbox = st.sidebar.selectbox("How would you like to predict?",("Online", "Batch"))
st.sidebar.info("This app is created to predict patient hospital charges")
st.sidebar.success('https://www.pycaret.org')
st.sidebar.image(image_hospital)
st.title('This hospital charges prediction')
if add_selectbox=='Online':
age=st.number_input('Age',min_value=1, max_value=100, value=25)
sex=st.selectbox('Sex',['male','female'])
bmi=st.number_input('BMI',min_value=10, max_value=50, value=20)
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('deployment_05102020')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict patient hospital charges')
st.sidebar.success('https://www.pycaret.org')
st.title("Insurance Charges Prediction App")
if add_selectbox == 'Online':
age = st.number_input('Age', min_value=1, max_value=100, value=25)
sex = st.selectbox('Sex', ['male', 'female'])
bmi = st.number_input('BMI', min_value=10, max_value=50, value=10)
children = st.selectbox('Children', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
if st.checkbox('Smoker'):
import os
# entorno streamlit
hide_st_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_st_style, unsafe_allow_html=True)
try:
folder = os.path.dirname(os.path.abspath(__file__))
name_model = os.path.join(folder, 'model')
final_model = load_model(name_model)
except:
print("Se necesita un modelo entrenado")
st.title('Certificación energética con Machine Learning')
st.title('\n\n')
st.error('Entorno web en pruebas... (actualización 2021-04-11)')
with st.beta_expander("Información:", expanded=True):
st.success(
'El proyecto ha sido elaborado por el investigador [Raúl Mora-García](https://publons.com/researcher/1717710/raul-tomas-mora-garcia/) [:email:](mailto:[email protected]) en colaboración con [Grupo Valero](https://www.grupovalero.com/) durante el año 2020. Subvención AEST/2019/005 del Programa para la promoción de la investigación científica, el desarrollo tecnológico y la innovación en la Comunitat Valenciana (Anexo VII) [DOGV nº8355](http://www.dogv.gva.es/datos/2018/08/06/pdf/2018_7758.pdf).'
)
st.info(
'\n\nEsta aplicación predice el consumo de energía (kWh/m²año) a partir de miles de datos de certificados energéticos elaborados con el programa **[CE3X](https://www.efinova.es/CE3X)**. Después se evalúa la posible reducción del consumo de energía al mejorar el aislamiento de la envolvente.'
'\n\n**Modelo:** Esta herramienta se desarrolla mediante aprendizaje automático supervisado (*supervised machine learning*) con algoritmos de regresión. Se ha diseñado un modelo de conjunto (*enseble learning*) que combina tres algoritmos de aprendizaje distintos basados en *boosting*: CatBoost Regressor [*catboost*](https://catboost.ai/), Light Gradient Boosting Machine [*lightgbm*](https://lightgbm.readthedocs.io/) y Gradient Boosting Regressor [*gbr*](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html).'
'\n\n**Datos:** Se utilizan más de 10.000 datos de certificados energéticos de viviendas individuales de la provincia de Barcelona (ubicados en zona climática C2), procedentes del [Instituto Catalán de Energía](http://icaen.gencat.cat/es/inici/).'
'\n\n**Precisión:** El modelo de conjunto se ha probado en un set de datos de entrenamiento obteniéndose un R2 de 0.888, y en el set de prueba un R2 de 0.732. Para datos nuevos no utilizados en el modelo se ha obtenido un R2 de 0.790, lo que indica que el modelo generaliza correctamente. '
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('model')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('logo.jpeg')
image_hospital = Image.open('hospital.jpg')
st.image(image, use_column_width=False)
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
st.sidebar.info(
'This Project is Developed by Mohd Aquib,Team SCRIPTHON.This app is created to predict patient hospital charges.'
)
st.sidebar.success('https://github.com/AquibPy')
st.sidebar.image(image_hospital)
st.title("Insurance Charges Prediction App")
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('deployment_26072020')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('logo2.png')
image_hospital = Image.open('hospital.jpg')
st.image(image, use_column_width=False)
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict patient hospital charges')
st.sidebar.success('https://www.pycaret.org')
st.sidebar.image(image_hospital)
st.title("Insurance Charges Prediction App")
import streamlit as st
import pickle
from pycaret.regression import load_model, predict_model
import pandas as pd
import numpy as np
model = load_model('deployment_1')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('panels.jpeg')
st.image(image, use_column_width=True)
add_selectbox = st.sidebar.selectbox(
"Would you like to predict a single day or upload a .csv?",
("Single Day", "Upload .csv"))
st.sidebar.info(
'Using a Machine Learning model to predict the kW production of Solar Panels in Antwerp, Belgium'
)
st.sidebar.info(
'Please refer to the GitHub repo to view the Weather Mapping for the "Weather Condition" input'
from pycaret.regression import load_model, predict_model
import streamlit as st
import pandas as pd
import numpy as np
model = load_model('final-model')
def predict(model, input_df):
predictions_df = predict_model(estimator=model, data=input_df)
predictions = predictions_df['Label'][0]
return predictions
def run():
from PIL import Image
image = Image.open('logo.png')
image_hospital = Image.open('hospital.jpg')
st.image(image, use_column_width=False)
add_selectbox = st.sidebar.selectbox("How would you like to predict?",
("Online", "Batch"))
st.sidebar.info('This app is created to predict patient hospital charges')
st.sidebar.success('https://www.pycaret.org')
st.sidebar.image(image_hospital)
st.title("Insurance Charges Prediction App")
