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import gradio as gr
from scipy.optimize import differential_evolution
import pandas as pd
import joblib
import matplotlib.pyplot as plt
# Load trained model
best_model = joblib.load("xgb_model.pkl")
# Predictor names and bounds
predictors = [
'loaded_drv_time_percycle',
'empty_drv_time_percycle',
'Eng_Speed_Ave',
'empty_stop_time_percycle',
'loadingstoptime_percycle',
'loaded_stop_time_percycle'
]
bounds_dict = {
'loaded_drv_time_percycle': (3, 60),
'empty_drv_time_percycle': (2, 51),
'Eng_Speed_Ave': (1051, 1596),
'empty_stop_time_percycle': (0.2, 24.6),
'loadingstoptime_percycle': (2, 18),
'loaded_stop_time_percycle': (0.4, 9)
}
# Optimization function
def optimize_dynamic(
loaded_drv, empty_drv, eng_speed, empty_stop, loading_stop, loaded_stop,
fix_loaded_drv, fix_empty_drv, fix_eng_speed, fix_empty_stop, fix_loading_stop, fix_loaded_stop
):
input_values = {
'loaded_drv_time_percycle': loaded_drv,
'empty_drv_time_percycle': empty_drv,
'Eng_Speed_Ave': eng_speed,
'empty_stop_time_percycle': empty_stop,
'loadingstoptime_percycle': loading_stop,
'loaded_stop_time_percycle': loaded_stop
}
fixed_flags = {
'loaded_drv_time_percycle': fix_loaded_drv,
'empty_drv_time_percycle': fix_empty_drv,
'Eng_Speed_Ave': fix_eng_speed,
'empty_stop_time_percycle': fix_empty_stop,
'loadingstoptime_percycle': fix_loading_stop,
'loaded_stop_time_percycle': fix_loaded_stop
}
bounds = []
variable_names = []
for name in predictors:
if not fixed_flags[name]:
bounds.append(bounds_dict[name])
variable_names.append(name)
if len(variable_names) == 0:
pred = best_model.predict(pd.DataFrame([input_values]))[0]
return f"✅ All inputs fixed.\nPredicted Fuel Rate: {pred:.2f} L/cycle"
def objective(x):
current_input = input_values.copy()
for i, name in enumerate(variable_names):
current_input[name] = x[i]
return best_model.predict(pd.DataFrame([current_input]))[0]
result = differential_evolution(objective, bounds=bounds, seed=42, maxiter=100)
final_input = input_values.copy()
for i, name in enumerate(variable_names):
final_input[name] = result.x[i]
changes = []
for name in variable_names:
original = input_values[name]
optimized = final_input[name]
if abs(original - optimized) > 0.01:
changes.append(f"{name}: {original:.2f} → {optimized:.2f}")
else:
changes.append(f"{name}: unchanged ({original:.2f})")
result_text = "\n".join([f"{k}: {v:.2f}" for k, v in final_input.items()])
result_text += "\n\n🔁 Optimized Inputs:\n" + "\n".join(changes)
result_text += f"\n\n⚡️ Predicted Fuel Rate: {result.fun:.2f} L/cycle"
# Sensitivity analysis for Engine Speed
# sensitivity_lines = ["\n📊 Suggested RPM vs. Fuel Rate:"]
start_rpm = int(round(final_input['Eng_Speed_Ave'] / 50.0) * 50)
end_rpm = min(start_rpm + 300, bounds_dict['Eng_Speed_Ave'][1])
start_rpm = max(start_rpm, bounds_dict['Eng_Speed_Ave'][0])
rpm_values = []
fuel_rates = []
for rpm in range(start_rpm, end_rpm + 1, 50):
temp_input = final_input.copy()
temp_input['Eng_Speed_Ave'] = rpm
temp_bounds = []
temp_names = []
for name in predictors:
if name != 'Eng_Speed_Ave' and not fixed_flags.get(name, False):
temp_bounds.append(bounds_dict[name])
temp_names.append(name)
def temp_objective(x):
t_input = temp_input.copy()
for i, name in enumerate(temp_names):
t_input[name] = x[i]
return best_model.predict(pd.DataFrame([t_input]))[0]
if temp_bounds:
temp_result = differential_evolution(temp_objective, bounds=temp_bounds, seed=42, maxiter=50)
fuel_rate = temp_result.fun
else:
fuel_rate = best_model.predict(pd.DataFrame([temp_input]))[0]
rpm_values.append(rpm)
fuel_rates.append(fuel_rate)
# Create plot
plt.figure(figsize=(6, 4))
plt.plot(rpm_values, fuel_rates, marker='o')
plt.title("Sensitivity of Fuel Rate to Engine Speed")
plt.xlabel("Engine Speed (RPM)")
plt.ylabel("Predicted Fuel Rate (L/cycle)")
plt.grid(True)
# Save and return plot image
plot_path = "sensitivity_plot.png"
plt.savefig(plot_path)
plt.close()
return result_text, plot_path
# Gradio Interface (Interface-style)
interface = gr.Interface(
fn=optimize_dynamic,
inputs=[
gr.Slider(3, 60, value=30, label="Loaded Drive Time"),
gr.Slider(2, 51, value=23.8, label="Empty Drive Time"),
gr.Slider(1051, 1596, value=1300, label="Engine Speed"),
gr.Slider(0.2, 24.6, value=10.0, label="Empty Stop Time"),
gr.Slider(2, 18, value=2.0, label="Loading Stop Time"),
gr.Slider(0.4, 9, value=0.4, label="Loaded Stop Time"),
gr.Checkbox(label="Fix Loaded Drive"),
gr.Checkbox(label="Fix Empty Drive"),
gr.Checkbox(label="Fix Engine Speed"),
gr.Checkbox(label="Fix Empty Stop"),
gr.Checkbox(label="Fix Loading Stop"),
gr.Checkbox(label="Fix Loaded Stop"),
],
outputs=[
gr.Textbox(label="Optimization Result"),
gr.Image(type="filepath", label="Sensitivity Plot")
],
title="⚙️ Fuel Rate What-If Optimizer",
description="Perform global optimization of fuel rate with optional fixed inputs and sensitivity on engine RPM."
)
interface.launch() |