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import os | |
import pandas as pd | |
import numpy as np | |
from sklearn.model_selection import train_test_split | |
from sklearn.ensemble import RandomForestRegressor | |
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score | |
import gradio as gr | |
# Read the dataset | |
data = pd.read_csv('Well_Rates.csv') | |
# Define input features and target variable | |
input_features = ['Qwater', 'Qgas', 'BHP', 'WHP', 'WHT', 'Tsep', 'Psep', 'Choke_in'] | |
target_variable = 'Qoil' | |
# Split the dataset into training and testing sets | |
X = data[input_features] | |
y = data[target_variable] | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
# Train the random forest regression model | |
rf = RandomForestRegressor(n_estimators=100, random_state=42) | |
rf.fit(X_train, y_train) | |
# Fine-tune the model | |
rf_tuned = RandomForestRegressor(n_estimators=200, max_depth=10, random_state=42) | |
rf_tuned.fit(X_train, y_train) | |
def predict_qoil(Qwater, Qgas, BHP, WHP, WHT, Tsep, Psep, Choke_in): | |
new_input = [[Qwater, Qgas, BHP, WHP, WHT, Tsep, Psep, Choke_in]] | |
predicted_qoil = rf_tuned.predict(new_input) | |
return predicted_qoil[0] | |
iface = gr.Interface( | |
fn=predict_qoil, | |
inputs=["number", "number", "number", "number", "number", "number", "number", "number"], | |
outputs="number", | |
interpretation="default") | |
iface.launch() | |