app.py

import gradio as gr
import numpy as np
import tensorflow as tf  # Or your preferred library for neural networks

# Load your trained ANN model
# Replace 'model_path' with the path to your saved model
model_path = 'reg_model.h5'
model = tf.keras.models.load_model(model_path)

def predict(num1, num2):
    # Preprocess input (if needed)
    #input_data = np.array([[num1, num2]])  # Assuming the model expects a 2D array

    # Make prediction using the loaded model
    prediction = model.predict(scaler.transform([[num1,num2]]))[0]

    return {'prediction': prediction}

iface = gr.Interface(
    fn=predict,
    inputs=[gr.inputs.Number(), gr.inputs.Number()],
    outputs="text"  # Display the prediction as text
)

# Launch the interface on a specific IP and port
iface.launch()