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Update app.py
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app.py
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import gradio as gr
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models = {
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"Flux Lora": "models/prashanth970/flux-lora-uncensored",
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"TrioHMH Flux": "models/DiegoJR1973/NSFW-TrioHMH-Flux",
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"Master": "models/pimpilikipilapi1/NSFW_master"
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}
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def generate_image(text, model_name):
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model_path = models[model_name]
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print(f"Fetching model from: {model_path}")
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try:
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except Exception as e:
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print(f"Error loading model: {e}")
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return None
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interface = gr.Interface(
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fn=generate_image,
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inputs=[
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gr.Textbox(label="Type here your imagination:", placeholder="Type your description here..."),
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gr.Dropdown(label="Select Model", choices=list(models.keys()), value="Flux Lora")
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],
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outputs=gr.Image(label="Generated Image"),
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theme="NoCrypt/miku",
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description="Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding.",
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)
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interface.launch()
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import gradio as gr
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# Define the available models
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models = {
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"Flux Lora": "models/prashanth970/flux-lora-uncensored",
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"TrioHMH Flux": "models/DiegoJR1973/NSFW-TrioHMH-Flux",
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"Master": "models/pimpilikipilapi1/NSFW_master"
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}
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# Function to generate an image from text using the selected model
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def generate_image(text, model_name):
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model_path = models[model_name] # Get the path of the selected model
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print(f"Fetching model from: {model_path}")
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try:
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# Dynamically load the model based on the selected model path
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model = gr.load(model_path) # Ensure this is the correct method to load your model
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result_image = model(text) # Generate the image from the input text
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# Ensure the result is in a proper image format
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if isinstance(result_image, str): # if model returns a path to the image
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return gr.Image(result_image)
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elif isinstance(result_image, bytes): # if model returns raw image bytes
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return gr.Image(value=result_image)
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else:
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return result_image
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except Exception as e:
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print(f"Error loading model: {e}")
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return None
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# Gradio Interface setup
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interface = gr.Interface(
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fn=generate_image,
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inputs=[
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gr.Textbox(label="Type here your imagination:", placeholder="Type your description here..."), # Textbox for input
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gr.Dropdown(label="Select Model", choices=list(models.keys()), value="Flux Lora") # Dropdown for selecting the model
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],
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outputs=gr.Image(label="Generated Image"), # Image output
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theme="NoCrypt/miku", # Set theme for the interface
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description="Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding.",
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)
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# Launch the Gradio interface
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interface.launch()
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