app.py

import gradio as gr
from diffusers import DiffusionPipeline

# Load the model
pipe = DiffusionPipeline.from_pretrained("Yaquv/rick")

# Define a prediction function to handle the output
def predict(text):
    output = pipe(text)  # Run the model with the text input
    # Assuming the output is a tuple and the image is the first element
    image = output[0]
    return image

# Create a Gradio interface
iface = gr.Interface(
    fn=predict,        # The prediction function
    inputs="text",     # Text input for the prompt
    outputs="image"    # Expecting an image output
)

# Launch the app
iface.launch()