app.py

import gradio as gr

# Define the available models
models = {
    "Flux Lora": "models/prashanth970/flux-lora-uncensored",
    "TrioHMH Flux": "models/DiegoJR1973/NSFW-TrioHMH-Flux",
    "Master": "models/pimpilikipilapi1/NSFW_master"
}

# Function to generate an image from text using the selected model
def generate_image(text, model_name):
    model_path = models[model_name]  # Get the path of the selected model
    print(f"Fetching model from: {model_path}")
    
    try:
        # Dynamically load the model based on the selected model path
        model = gr.load(model_path)  # Ensure this is the correct method to load your model
        result_image = model(text)  # Generate the image from the input text
        
        # Ensure the result is in a proper image format
        if isinstance(result_image, str):  # if model returns a path to the image
            return gr.Image(result_image)
        elif isinstance(result_image, bytes):  # if model returns raw image bytes
            return gr.Image(value=result_image)
        else:
            return result_image
    except Exception as e:
        print(f"Error loading model: {e}")
        return None

# Gradio Interface setup
interface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Type here your imagination:", placeholder="Type your description here..."),  # Textbox for input
        gr.Dropdown(label="Select Model", choices=list(models.keys()), value="Flux Lora")  # Dropdown for selecting the model
    ],
    outputs=gr.Image(label="Generated Image"),  # Image output
    theme="NoCrypt/miku",  # Set theme for the interface
    description="Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding.",
)

# Launch the Gradio interface
interface.launch()