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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 os
import sklearn # 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_new_values(new_input_data):
try:
# Ensure the new input data is in the correct format
print(f"Raw Input Data: {new_input_data}")
new_input_data = np.array(new_input_data).reshape(1, -1)
# Scale the new input data
new_input_scaled = scaler_X.transform(new_input_data)
print(f"Scaled Input Data: {new_input_scaled}")
# Make predictions with the MLP model
contamination_predictions, gradients_predictions = loaded_mlp_model.predict(new_input_scaled)
return contamination_predictions[0], gradients_predictions[0]
except Exception as e:
print(f"Error in prediction: {e}")
return (["Error"] * 6, ["Error"] * 6)
def gradio_interface(velocity, temperature, precipitation, humidity):
try:
input_data = [velocity, temperature, precipitation, humidity]
print(f"Input Data: {input_data}")
contamination_predictions, gradients_predictions = predict_new_values(input_data)
print(f"Contamination Predictions: {contamination_predictions}")
print(f"Gradients Predictions: {gradients_predictions}")
return (
[f"{val * 100:.2f}%" if val != "Error" else "Error" for val in contamination_predictions],
[f"{val:.2f}" if val != "Error" else "Error" for val in gradients_predictions]
)
except Exception as e:
print(f"Error in Gradio interface: {e}")
return (["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")
]
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")
]
with gr.Blocks() as demo:
gr.Markdown("<h1 style='text-align: center;'>Environmental Factor-Based Contamination & Gradient Prediction</h1>")
gr.Markdown("This application predicts the contamination levels and corresponding gradients 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=gradio_interface, inputs=inputs, outputs=contamination_outputs + gradients_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("### Gradients Predictions")
for out in gradients_outputs:
out.render()
demo.launch()
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