app.py

import gradio as gr
from transformers import StableDiffusionPipeline
import torch
from PIL import Image
import requests

def generate_image(prompt):
    # Load the preprocessing and model pipeline
    # Here, we assume the Kvikontent/midjourney-v6 model has text-to-image capabilities in a manner similar to stable diffusion.
    # This part needs verification and adjustment according to actual model documentation and availability.
    model_id = "Kvikontent/midjourney-v6"
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    # Setup the model pipeline (this can be adjusted if the model's actual interface differs)
    # This example uses the typical usage pattern for generative models, but you should adjust according to the actual model's specs.
    pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True)  # Replace with actual method to load Kvikontent/midjourney-v6 if different
    pipe = pipe.to(device)
    
    # Generating the image
    image = pipe(prompt).images[0]  # This line assumes the return type is accessible like this, adjust this according to actual usage.
    
    # Convert tensor to PIL Image (adjust if the output format differs)
    image = Image.fromarray(image.numpy(), 'RGB')
    return image

# Create a Gradio interface
iface = gr.Interface(fn=generate_image,
                     inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
                     outputs="image",
                     title="Text to Image Generator",
                     description="Type some text and generate an image using the Kvikontent/midjourney-v6 model.")

# Running the application
iface.launch()