app.py

import gradio as gr
from transformers import pipeline

# Load the pipeline for text generation
pipe = pipeline(
    "text-generation",
    model="Ar4ikov/gpt2-650k-stable-diffusion-prompt-generator",
    tokenizer="gpt2"
)

# Initialize a list to store the history of generated prompts
history = []

# Function to generate text based on input prompt and record the history
def generate_text(prompt):
    generated_text = pipe(prompt, max_length=77)[0]["generated_text"]
    # Append the generated prompt and its result to the history list
    history.append({"prompt": prompt, "generated_text": generated_text})
    return generated_text

# Create a Gradio interface with history recording
iface = gr.Interface(
    fn=generate_text,
    inputs=gr.Textbox(lines=5, label="Prompt"),
    outputs=[gr.Textbox(label="Output 1", show_copy_button=True), gr.Textbox(label="Output 2", show_copy_button=True)],
    title="AI Art Prompt Generator",
    description="Art Prompt Generator is a user-friendly interface designed to optimize input for AI Art Generator or Creator. For faster generation speeds, it's recommended to load the model locally with GPUs, as the online demo at Hugging Face Spaces utilizes CPU, resulting in slower processing times.",
    api_name="predict"
)

# Launch the interface
iface.launch(show_api=True)