import gradio as gr import numpy as np import json import joblib import tensorflow as tf import pandas as pd from joblib import load from tensorflow.keras.models import load_model from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt import os import sklearn # Assuming the previous setup and imports have been done def predict_and_plot(velocity, temperature, precipitation, humidity): try: # Prepare the example data example_data = pd.DataFrame({ 'Velocity(mph)': [velocity], 'Temperature': [temperature], 'Precipitation': [precipitation], 'Humidity': [humidity] }) # Scale the example data example_data_scaled = scaler_X.transform(example_data) # Function to predict contamination levels def predict_contamination(example_data_scaled): # Predict using MLP model mlp_predictions_contamination, mlp_predictions_gradients = loaded_mlp_model.predict(example_data_scaled) # Predict using RF model rf_predictions = loaded_rf_model.predict(example_data_scaled) # Combine predictions for meta model combined_features = np.concatenate([np.concatenate([mlp_predictions_contamination, mlp_predictions_gradients], axis=1), rf_predictions], axis=1) # Predict using meta model meta_predictions = loaded_meta_model.predict(combined_features) return meta_predictions[:, :6] # Assuming the first 6 columns are contamination predictions # Predict contamination levels for the single example contamination_levels = predict_contamination(example_data_scaled) # Simulate contamination levels at multiple time intervals time_intervals = np.arange(0, 601, 60) # Simulating time intervals from 0 to 600 seconds # Generate simulated contamination levels (linear interpolation between predicted values) simulated_contamination_levels = np.array([ np.linspace(contamination_levels[0][i], contamination_levels[0][i] * 2, len(time_intervals)) for i in range(contamination_levels.shape[1]) ]).T # Function to calculate cleaning time using linear interpolation def calculate_cleaning_time(time_intervals, contamination_levels, threshold=0.4): cleaning_times = [] for i in range(contamination_levels.shape[1]): levels = contamination_levels[:, i] for j in range(1, len(levels)): if levels[j-1] <= threshold <= levels[j]: # Linear interpolation t1, t2 = time_intervals[j-1], time_intervals[j] c1, c2 = levels[j-1], levels[j] cleaning_time = t1 + (threshold - c1) * (t2 - t1) / (c2 - c1) cleaning_times.append(cleaning_time) break return cleaning_times # Calculate cleaning times for all 6 lidars cleaning_times = calculate_cleaning_time(time_intervals, simulated_contamination_levels) # Lidar names lidar_names = ['F/L', 'F/R', 'Left', 'Right', 'Roof', 'Rear'] # Plot the graph plt.figure(figsize=(12, 8)) for i in range(simulated_contamination_levels.shape[1]): plt.plot(time_intervals, simulated_contamination_levels[:, i], label=f'{lidar_names[i]}') plt.axhline(y=0.4, color='r', linestyle='--', label='Contamination Threshold' if i == 0 else "") if i < len(cleaning_times): plt.scatter(cleaning_times[i], 0.4, color='k') # Mark the cleaning time point plt.title('Contamination Levels Over Time for Each Lidar') plt.xlabel('Time (seconds)') plt.ylabel('Contamination Level') plt.legend() plt.grid(True) # Save the plot to a file plt.savefig('plot.png') # Return the plot and predictions return plt, [f"{val * 100:.2f}%" for val in contamination_levels[0]], [f"{val:.2f}" for val in cleaning_times] except Exception as e: print(f"Error in Gradio interface: {e}") return plt.figure(), ["Error"] * 6, ["Error"] * 6 inputs = [ gr.Slider(minimum=0, maximum=100, value=50, step=0.05, label="Velocity (mph)"), gr.Slider(minimum=-2, maximum=30, value=0, step=0.5, label="Temperature (°C)"), gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Precipitation (inch)"), gr.Slider(minimum=0, maximum=100, value=50, label="Humidity (%)") ] contamination_outputs = [ gr.Textbox(label="Front Left Contamination"), gr.Textbox(label="Front Right Contamination"), gr.Textbox(label="Left Contamination"), gr.Textbox(label="Right Contamination"), gr.Textbox(label="Roof Contamination"), gr.Textbox(label="Rear Contamination") ] cleaning_time_outputs = [ gr.Textbox(label="Front Left Cleaning Time"), gr.Textbox(label="Front Right Cleaning Time"), gr.Textbox(label="Left Cleaning Time"), gr.Textbox(label="Right Cleaning Time"), gr.Textbox(label="Roof Cleaning Time"), gr.Textbox(label="Rear Cleaning Time") ] with gr.Blocks() as demo: gr.Markdown("

Environmental Factor-Based Contamination & Cleaning Time Prediction

") gr.Markdown("This application predicts the contamination levels, corresponding gradients, and cleaning times for different parts of a car's LiDAR system based on environmental factors such as velocity, temperature, precipitation, and humidity.") with gr.Row(): with gr.Column(): gr.Markdown("### Input Parameters") for inp in inputs: inp.render() # Centered image display with gr.Row(): with gr.Column(scale=1, min_width=0): gr.Image(image_path) # Ensure the image is centered gr.Button(value="Submit", variant="primary").click( fn=predict_and_plot, inputs=inputs, outputs=[gr.Plot(label="Contamination Levels Over Time")] + contamination_outputs + cleaning_time_outputs ) gr.Button(value="Clear").click(fn=lambda: None) with gr.Column(): gr.Markdown("### Contamination Predictions") for out in contamination_outputs: out.render() with gr.Column(): gr.Markdown("### Cleaning Time Predictions") for out in cleaning_time_outputs: out.render() demo.launch()