Create app.py

app.py

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+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
+
+# Display library versions
+print(f"Gradio version: {gr.__version__}")
+print(f"NumPy version: {np.__version__}")
+print(f"Scikit-learn version: {sklearn.__version__}")
+print(f"Joblib version: {joblib.__version__}")
+print(f"TensorFlow version: {tf.__version__}")
+print(f"Pandas version: {pd.__version__}")
+
+# Directory paths for the saved models
+script_dir = os.path.dirname(os.path.abspath(__file__))
+scaler_path = os.path.join(script_dir, 'toolkit', 'scaler_X.json')
+rf_model_path = os.path.join(script_dir, 'toolkit', 'rf_model.joblib')
+mlp_model_path = os.path.join(script_dir, 'toolkit', 'mlp_model.keras')
+meta_model_path = os.path.join(script_dir, 'toolkit', 'meta_model.joblib')
+image_path = os.path.join(script_dir, 'toolkit', 'car.png')
+
+# Load the scaler and models
+try:
+    # Load the scaler
+    with open(scaler_path, 'r') as f:
+        scaler_params = json.load(f)
+    scaler_X = MinMaxScaler()
+    scaler_X.scale_ = np.array(scaler_params["scale_"])
+    scaler_X.min_ = np.array(scaler_params["min_"])
+    scaler_X.data_min_ = np.array(scaler_params["data_min_"])
+    scaler_X.data_max_ = np.array(scaler_params["data_max_"])
+    scaler_X.data_range_ = np.array(scaler_params["data_range_"])
+    scaler_X.n_features_in_ = scaler_params["n_features_in_"]
+    scaler_X.feature_names_in_ = np.array(scaler_params["feature_names_in_"])
+
+    # Load the models
+    loaded_rf_model = load(rf_model_path)
+    print("Random Forest model loaded successfully.")
+    loaded_mlp_model = load_model(mlp_model_path)
+    print("MLP model loaded successfully.")
+    loaded_meta_model = load(meta_model_path)
+    print("Meta model loaded successfully.")
+except Exception as e:
+    print(f"Error loading models or scaler: {e}")
+
+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 and gradients
+        def predict_contamination_and_gradients(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], meta_predictions[:, 6:]  # Split predictions into contamination and gradients
+
+        # Predict contamination levels and gradients for the single example
+        contamination_levels, gradients = predict_contamination_and_gradients(example_data_scaled)
+
+        # Simulate contamination levels at multiple time intervals
+        time_intervals = np.arange(0, 3601, 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
+                else:
+                    cleaning_times.append(time_intervals[-1])  # If threshold is not reached
+            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
+        fig, ax = plt.subplots(figsize=(12, 8))
+
+        for i in range(simulated_contamination_levels.shape[1]):
+            ax.plot(time_intervals, simulated_contamination_levels[:, i], label=f'{lidar_names[i]}')
+            ax.axhline(y=0.4, color='r', linestyle='--', label='Contamination Threshold' if i == 0 else "")
+            if i < len(cleaning_times):
+                ax.scatter(cleaning_times[i], 0.4, color='k')  # Mark the cleaning time point
+
+        ax.set_title('Contamination Levels Over Time for Each Lidar')
+        ax.set_xlabel('Time (seconds)')
+        ax.set_ylabel('Contamination Level')
+        ax.legend()
+        ax.grid(True)
+        
+        # Flatten the results into a single list of 19 outputs (1 plot + 6 contamination + 6 gradients + 6 cleaning times)
+        plot_output = fig
+        contamination_output = [f"{val * 100:.2f}%" for val in contamination_levels[0]]
+        gradients_output = [f"{val:.4f}" for val in gradients[0]]
+        cleaning_time_output = [f"{val:.2f}" for val in cleaning_times]
+
+        return [plot_output] + contamination_output + gradients_output + cleaning_time_output
+
+    except Exception as e:
+        print(f"Error in Gradio interface: {e}")
+        return [plt.figure()] + ["Error"] * 18
+
+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")
+]
+
+gradients_outputs = [
+    gr.Textbox(label="Front Left Gradient"),
+    gr.Textbox(label="Front Right Gradient"),
+    gr.Textbox(label="Left Gradient"),
+    gr.Textbox(label="Right Gradient"),
+    gr.Textbox(label="Roof Gradient"),
+    gr.Textbox(label="Rear Gradient")
+]
+
+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(css=".column-container {height: 100%; display: flex; flex-direction: column; justify-content: space-between;}") as demo:
+    gr.Markdown("<h1 style='text-align: center;'>Environmental Factor-Based Contamination, Gradient, & Cleaning Time Prediction</h1>")
+    gr.Markdown("This application predicts the contamination levels, gradients, and cleaning times for different parts of a car's LiDAR system based on environmental factors such as velocity, temperature, precipitation, and humidity.")
+    
+    # Top Section: Inputs and Car Image
+    with gr.Row():
+        with gr.Column(scale=2, elem_classes="column-container"):
+            gr.Markdown("### Input Parameters")
+            for inp in inputs:
+                inp.render()
+            submit_button = gr.Button(value="Submit", variant="primary")
+            clear_button = gr.Button(value="Clear")
+
+        with gr.Column(scale=1):
+            gr.Markdown("### Location of LiDARs")
+            gr.Image(image_path)
+
+    # Bottom Section: Outputs (Three columns)
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Markdown("### Contamination Predictions")
+            for out in contamination_outputs:
+                out.render()
+
+        with gr.Column(scale=2):
+            gr.Markdown("### Gradient Predictions")
+            for out in gradients_outputs:
+                out.render()
+
+        with gr.Column(scale=2):
+            gr.Markdown("### Cleaning Time Predictions")
+            for out in cleaning_time_outputs:
+                out.render()
+
+    # Graph below the outputs
+    with gr.Row():
+        plot_output = gr.Plot(label="Contamination Levels Over Time")
+
+    submit_button.click(
+        fn=predict_and_plot, 
+        inputs=inputs, 
+        outputs=[plot_output] + contamination_outputs + gradients_outputs + cleaning_time_outputs
+    )
+    clear_button.click(fn=lambda: None)
+
+demo.launch()