Added initial project files

app.py

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+# Everything here goes into the app.py file
+# Define Gradio inference function
+
+# 15 March 2025
+# Prakash Kota
+# East Greenbush
+
+import numpy as np
+import tensorflow as tf
+import os
+import gradio as gr
+import joblib
+
+# Fixed reactor parameters
+V = 100   # Reactor volume (L)
+k = 0.1   # Reaction rate constant (1/min)
+delta_H = -50000  # Heat of reaction (J/mol)
+rho = 1   # Density in kg/L
+Cp = 4184  # Heat capacity in J/kg·K
+
+# Define Gradio inference function
+def predict_cstr(CA_in, T_in, F):
+    # Load scalers and model
+    scaler_X = joblib.load(f"{model_dir}/scaler_X.pkl")
+    scaler_Y = joblib.load(f"{model_dir}/scaler_Y.pkl")
+    model = tf.keras.models.load_model(f"{model_dir}/cstr_model.keras")
+    
+    # Scale input
+    input_scaled = scaler_X.transform([[CA_in, T_in, F]])
+    
+    # Predict using the model
+    prediction_scaled = model.predict(input_scaled)
+    prediction_original = scaler_Y.inverse_transform(prediction_scaled)
+    CA_ss_pred, T_ss_pred = prediction_original[0]
+    
+    # Compute analytical solution
+    CA_ss_analytical = (CA_in * (F / V)) / ((F / V) + k)
+    T_ss_analytical = T_in + ((-delta_H * k * CA_ss_analytical) / (rho * Cp)) * (V / F)
+    
+    # Compute % Error and % Accuracy
+    percent_error = abs((CA_ss_pred - CA_ss_analytical) / CA_ss_analytical) * 100
+    percent_accuracy = (1 - abs(CA_ss_pred - CA_ss_analytical) / CA_ss_analytical) * 100
+    
+    return (f"Predicted CA_ss: {CA_ss_pred:.4f} mol/L\n"
+            f"Predicted T_ss: {T_ss_pred:.2f} K\n"
+            f"Analytical CA_ss: {CA_ss_analytical:.4f} mol/L\n"
+            f"Analytical T_ss: {T_ss_analytical:.2f} K\n"
+            f"% Error: {percent_error:.2f}%\n"
+            f"% Accuracy: {percent_accuracy:.2f}%")
+
+# Deploy using Gradio
+iface = gr.Interface(
+    fn=predict_cstr,
+    inputs=[
+        gr.Number(label="Input Concentration CA_in - Range [0.5-2.0] mol/L"),
+        gr.Number(label="Input Temperature T_in - Range [300-350] K"),
+        gr.Number(label="CSTR Flow Rate F - Range [5-20] L/min")
+    ],
+    outputs="text",
+    title="CSTR Surrogate Model Inference",
+    description="Enter the input values to predict steady-state concentration and temperature."

requirements.txt

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+tensorflow
+gradio
+numpy
+joblib
+scikit-learn
+scipy