app.py

import gradio as gr
from diffusers import DiffusionPipeline

# Load the base model
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model)

# Load the LoRA weights from your repository
pipe.load_lora_weights("Yaquv/rick", weight_name="rick.safetensors")

# Define a prediction function
def predict(text):
    output = pipe(text)
    image = output.images[0]  # Extract the first image from the output
    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()