app.py

import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
import joblib  # For loading the saved preprocessing objects
import pickle
# Load your pre-trained model
model = load_model('crack-v3.2.h5')
with open('poly_features.pkl', 'rb') as f:
    poly_loaded = pickle.load(f)

# Load the StandardScaler object
with open('standard_scaler.pkl', 'rb') as f:
    scaler_loaded = pickle.load(f)

    
# Load the preprocessing objects
poly = PolynomialFeatures(degree=5, include_bias=False)
scaler = StandardScaler()

# 
def preprocess_input(geometric_factor, flange_width, beam_width, beamh, columntw):
    """
    Preprocess the input data: apply polynomial features and then scale.
    """
    
    # # Apply Polynomial Features
    # ['GEOMETRIC_FACTOR',	'COLUMN_WIDTH',	'BEAM_WIDTH','BEAM - h', 'COLUMN - tw']
    X_test_poly = poly_loaded.transform([[geometric_factor, flange_width, beam_width, beamh, columntw]])

    # Standardize the polynomial features
    scaled_features = scaler_loaded.transform(X_test_poly)
    
    return scaled_features

def predict_fatigue_crack_growth_rate(geometric_factor, flange_width, beam_width, beamh, columntw):
    """
    Function to predict the fatigue crack growth rate based on the preprocessed input features.
    """
    # Preprocess the input data
    preprocessed_input = preprocess_input(geometric_factor, flange_width, beam_width, beamh, columntw)
    
    # Predict using the loaded model
    prediction = model.predict(preprocessed_input)
    
    # Return the prediction
    return f"Predicted Fatigue Crack Growth Rate: {prediction[0][0]}"

# Create a Gradio interface
iface = gr.Interface(fn=predict_fatigue_crack_growth_rate,
                     inputs=[gr.Number(label="Geometric Factor"),
                             gr.Number(label="Column Width"),
                             gr.Number(label="Beam Width"),
                             gr.Number(label="Beam - h"),
                             gr.Number(label="Column - tw")],
                     outputs=gr.Textbox(label="Prediction"),
                     title="Fatigue Crack Growth Rate Predictor",
                     description="Enter the values for Geometric Factor, Column Width, Beam Width, Beam - h, and COLUMN - tw to predict the Fatigue Crack Growth Rate.")

if __name__ == "__main__":
    iface.launch()