app.py

import gradio as gr
import torch
import os

# Function to check if the model is loaded
model = None

def load_model(model_path):
    global model
    if os.path.exists(model_path):
        model = torch.hub.load(model_path, 'StableNormal', trust_repo=True)
        return 'Model loaded successfully!'
    else:
        return 'Error: Model path does not exist.'

# Function to process input and generate output
def process_input(image, model_path):
    if model is None:
        return 'Error: Model not loaded. Please load the model first.'
    try:
        # Simulate processing the image with the model
        # This should be replaced with actual model inference code
        output = f'Processed image with model at {model_path}'
        return output
    except Exception as e:
        return f'Error during processing: {str(e)}'

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("""
    # LangScene-X Gradio Interface
    This app allows you to load a model and process images using the LangScene-X framework.
    **Instructions:**
    1. Load the model by providing the model path.
    2. Upload an image to process.
    3. Click on 'Process Image' to see the output.
    """)
    model_path = gr.Textbox(label='Model Path', placeholder='Enter the path to the model')
    load_button = gr.Button('Load Model')
    load_output = gr.Textbox(label='Load Model Output')
    load_button.click(load_model, inputs=model_path, outputs=load_output)
    image_input = gr.Image(type='pil', label='Upload Image')
    process_button = gr.Button('Process Image')
    output_text = gr.Textbox(label='Output')
    process_button.click(process_input, inputs=[image_input, model_path], outputs=output_text)

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