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working on dashboard streamlit
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@MarkoBrie
MarkoBrie committed Feb 12, 2024
1 parent 0198403 commit 5510730
Showing 2 changed files with 100 additions and 4 deletions.
88 changes: 88 additions & 0 deletions 3_STREAMlit_dashboard copie.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,88 @@
import pandas as pd
import streamlit as st
import requests



def request_prediction(model_uri, data):
headers = {"Content-Type": "application/json"}
st.write(data)
#data_json = {'data': data}
data_json = data

#data_json = {'dataframe_split': data.to_dict(orient='records')}
st.write(data_json)

response = requests.request(
method='POST', headers=headers, url=model_uri, json=data_json)

if response.status_code != 200:
raise Exception(
"Request failed with status {}, {}".format(response.status_code, response.text))

return response.json()


def main():
MLFLOW_URI = 'http://127.0.0.1:8099/invocations'
CORTEX_URI = 'http://0.0.0.0:8890/'
RAY_SERVE_URI = 'http://127.0.0.1:8000/regressor'

api_choice = st.sidebar.selectbox(
'Quelle API souhaitez vous utiliser',
['MLflow', 'Cortex', 'Ray Serve'])

st.title('Median House Price Prediction')

revenu_med = st.number_input('Revenu médian dans le secteur (en 10K de dollars)',
min_value=0., value=3.87, step=1.)

age_med = st.number_input('Âge médian des maisons dans le secteur',
min_value=0., value=28., step=1.)

nb_piece_med = st.number_input('Nombre moyen de pièces',
min_value=0., value=5., step=1.)

nb_chambre_moy = st.number_input('Nombre moyen de chambres',
min_value=0., value=1., step=1.)

taille_pop = st.number_input('Taille de la population dans le secteur',
min_value=0, value=1425, step=100)

occupation_moy = st.number_input('Occupation moyenne de la maison (en nombre d\'habitants)',
min_value=0., value=3., step=1.)

latitude = st.number_input('Latitude du secteur',
value=35., step=1.)

longitude = st.number_input('Longitude du secteur',
value=-119., step=1.)

predict_btn = st.button('Prédire')
if predict_btn:
data = pd.DataFrame([[revenu_med, age_med, nb_piece_med, nb_chambre_moy,
taille_pop, occupation_moy, latitude, longitude]])#.to_json(orient='records')

data = {"dataframe_records": [[revenu_med, age_med, nb_piece_med, nb_chambre_moy,
taille_pop, occupation_moy, latitude, longitude]]}

data = { "inputs":[[0, 0, 1, 1, 63000.0, 310500.0, 15232.5, 310500.0, 0.026392, 16263, -214.0, -8930.0, -573, 0.0, 1, 1, 0, 1, 1, 0, 2.0, 2, 2, 11, 0, 0, 0, 0, 1, 1, 0.0, 0.0765011930557638, 0.0005272652387098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, True, False, False, False, False, False, False, False, True, False, False, False, False, False, False, True, False, False, False, False, True, False, False, False, True, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]]}


pred = None

if api_choice == 'MLflow':
st.write(MLFLOW_URI)
st.write(data)
pred = request_prediction(MLFLOW_URI, data)#[0] * 100000
elif api_choice == 'Cortex':
pred = request_prediction(CORTEX_URI, data)[0] * 100000
elif api_choice == 'Ray Serve':
pred = request_prediction(RAY_SERVE_URI, data)[0] * 100000
st.write(
'Le prix médian d\'une habitation est de {:.2f}'.format(pred["predictions"][0]))



if __name__ == '__main__':
main()
16 changes: 12 additions & 4 deletions 3_STREAMlit_dashboard.py
View file
Original file line number Diff line number Diff line change
@@ -24,16 +24,20 @@ def request_prediction(model_uri, data):


def main():
MLFLOW_URI = 'http://127.0.0.1:8099/invocations'
#MLFLOW_URI = 'http://127.0.0.1:8099/invocations'
MLFLOW_URI = 'https://fastapi-cd-webapp.azurewebsites.net/predict'
CORTEX_URI = 'http://0.0.0.0:8890/'
RAY_SERVE_URI = 'http://127.0.0.1:8000/regressor'

api_choice = st.sidebar.selectbox(
'Quelle API souhaitez vous utiliser',
['MLflow', 'Cortex', 'Ray Serve'])
['MLflow', 'Option 2', 'Option 3'])

st.title('Median House Price Prediction')

selected_radio = st.radio('Select an option', ['Option 1', 'Option 2', 'Option 3'])


revenu_med = st.number_input('Revenu médian dans le secteur (en 10K de dollars)',
min_value=0., value=3.87, step=1.)

@@ -73,14 +77,18 @@ def main():

if api_choice == 'MLflow':
st.write(MLFLOW_URI)
st.write(data)
#st.write(data)
pred = request_prediction(MLFLOW_URI, data)#[0] * 100000
st.write(pred)
st.write(pred["prediction"])
elif api_choice == 'Cortex':
pred = request_prediction(CORTEX_URI, data)[0] * 100000
elif api_choice == 'Ray Serve':
pred = request_prediction(RAY_SERVE_URI, data)[0] * 100000
st.write(
'Le prix médian d\'une habitation est de {:.2f}'.format(pred["predictions"][0]))
'Le prix médian d\'une habitation est de {:.2f}'.format(pred["prediction"]))
#'Le prix médian d\'une habitation est de {:.2f}'.format(pred["prediction"][0]))




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