app.py

import gradio as gr
import torch
import os

# Load the model (dummy function for demonstration)
def load_model(encoder_path, decoder_path):
    if not os.path.exists(encoder_path) or not os.path.exists(decoder_path):
        raise FileNotFoundError("Model files not found. Please check the paths.")
    # Simulate loading a model
    return "Model loaded successfully!"

# Inference function (dummy function for demonstration)
def infer(input_data, encoder_path, decoder_path):
    try:
        # Load model
        load_model(encoder_path, decoder_path)
        # Simulate inference
        return f"Inference completed on input: {input_data}"
    except Exception as e:
        return str(e)

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Galileo Remote Sensing Model Inference")
    gr.Markdown("This app allows you to perform inference using the Galileo pretrained models.")
    encoder_path = gr.Textbox(label="Encoder Model Path", placeholder="Path to encoder.pt")
    decoder_path = gr.Textbox(label="Decoder Model Path", placeholder="Path to decoder.pt")
    input_data = gr.Textbox(label="Input Data", placeholder="Enter input data for inference")
    output = gr.Textbox(label="Output")
    submit_btn = gr.Button("Run Inference")

    submit_btn.click(infer, inputs=[input_data, encoder_path, decoder_path], outputs=output)

# Launch the app
if __name__ == "__main__":
    demo.launch()