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import warnings
warnings.filterwarnings("ignore")
import pickle
import pandas as pd
import numpy as np
import gradio as gr
import random
def import_model():
model_names = ['random_forest', 'xgboost', 'decision_tree']
loaded_models = tuple()
for model_name in model_names:
with open(f'{model_name}.pkl', 'rb') as file:
model = pickle.load(file)
loaded_models += (model,)
return loaded_models
def predict_liquid_rate(*input):
input_list = list(input)
inp_arr = np.array(input_list[:-1]).reshape(1, -1)
random_forest_model, xgboost_model, decision_tree_model = import_model()
model_selection = input_list[-1]
print(model_selection)
result = {}
model_names = {
'XGBoost': 'XGBoost Oil Rate',
'Random Forest': 'Random Forest Oil Rate',
'Decision Tree': 'Decision Tree Oil Rate',
'Prophet Model': 'Prophet Model Oil Rate'
}
xg_output = ''
dt_output = ''
rf_output = ''
for choice in model_selection:
if choice == 'XGBoost':
xg_pred = xgboost_model.predict(inp_arr)
result[choice] = xg_pred[0]
xg_output = f"{model_names['XGBoost']}: {result['XGBoost']:.2f} Bbls/day"
elif choice == 'Decision Tree':
dt_pred = decision_tree_model.predict(inp_arr)
result[choice] = dt_pred[0]
dt_output = f"{model_names['Decision Tree']}: {result['Decision Tree']:.2f} Bbls/day"
elif choice == 'Random Forest':
rf_pred = random_forest_model.predict(inp_arr)
result[choice] = rf_pred[0]
rf_output = f"{model_names['Random Forest']}: {result['Random Forest']:.2f} Bbls/day"
return xg_output, dt_output, rf_output
with gr.Blocks() as demo:
gr.Markdown(
"""
# Oil Rate Prediction
Use this table as Reference for Last Well test data.
""")
with gr.Column():
with gr.Box():
frame_output = gr.Dataframe(
value=[['2022-12-23', 32, 1000, 280, 0.45, 775.12]],
headers=['Date', 'Choke', 'FTHP', 'FLP', 'BS&W', 'OilRate'],
datatype=["str", "number", "number", "number", "number", "number"],
)
gr.Markdown(
""" Use the different input slider to select new welltest information
""")
with gr.Box():
choke = gr.Slider(minimum=0, maximum=100, value=32, step=2, label="Choke Size (1/64\")", interactive=True)
fthp = gr.Slider(minimum=500, maximum=5000, step=1, value=1000, label="Tubing Head Pressure (FTHP)(psi)", interactive=True)
flp = gr.Slider(minimum=0, maximum=5000, step=1, value=280,label="Flow Line Pressure (FLP)(psi)", interactive=True)
bsw = gr.Slider(minimum=0, maximum=100, value=0.45, label="Basic Sediment and Water (BS&W)(%)", interactive=True)
gr.Markdown(
""" Use the different trained models to perform Oil rate prediction
""")
# Output Controls
with gr.Column():
select_model = gr.CheckboxGroup(choices=["Random Forest", "XGBoost", "Decision Tree"], value='XGBoost', label="Select Model", info="Select Model to make prediction", interactive=True)
btn_predict = gr.Button("Test Prediction")
xg_output = gr.Label(label="XGBoost model")
dt_output = gr.Label(label="Decision Tree")
rf_output = gr.Label(label="Random Forest")
input_items = [choke, fthp, flp, bsw, select_model]
btn_predict.click(fn=predict_liquid_rate, inputs=input_items, outputs=[xg_output,dt_output,rf_output])
#gr.describe()
demo.launch(debug=True) |