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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() | |