app.py

import gradio as gr
import os
import sys
from pathlib import Path
from PIL import Image
import re
import numpy as np

# Create directories if they don't exist
if not os.path.exists('saved_prompts'):
    os.makedirs('saved_prompts')
if not os.path.exists('saved_images'):
    os.makedirs('saved_images')

# Function to generate a safe filename
def generate_safe_filename(text):
    return re.sub('[^a-zA-Z0-9]', '_', text)

# Function to load models from a text file
def load_models_from_file(filename):
    with open(filename, 'r') as f:
        return [line.strip() for line in f]

if __name__ == "__main__":
    models = load_models_from_file('models.txt')
    print(models)

current_model = models[0]
text_gen1 = gr.Interface.load("spaces/Omnibus/MagicPrompt-Stable-Diffusion_link")
models2 = [gr.Interface.load(f"models/{model}", live=True, preprocess=False) for model in models]

# Function to trigger text generation
def text_it1(inputs, text_gen1=text_gen1):
    go_t1 = text_gen1(inputs)
    return (go_t1)

# Function to set the current model
def set_model(current_model):
    current_model = models[current_model]
    return gr.update(label=(f"{current_model}"))

# Function to list saved prompts and images
def list_saved_prompts_and_images():
    saved_prompts = os.listdir('saved_prompts')
    saved_images = os.listdir('saved_images')
    html_str = "<h2>Saved Prompts and Images:</h2><ul>"
    for prompt_file in saved_prompts:
        image_file = f"{prompt_file[:-4]}.png"
        if image_file in saved_images:
            html_str += f'<li>Prompt: {prompt_file[:-4]} | <a href="saved_images/{image_file}" download>Download Image</a></li>'
    html_str += "</ul>"
    return html_str

# Function to handle image generation and saving
def send_it1(inputs, model_choice):
    proc1 = models2[model_choice]
    output1 = proc1(inputs)
    safe_filename = generate_safe_filename(inputs[0])
    image_path = f"saved_images/{safe_filename}.png"
    prompt_path = f"saved_prompts/{safe_filename}.txt"
    with open(prompt_path, 'w') as f:
        f.write(inputs[0])
    # Saving the image based on its type
    if isinstance(output1, np.ndarray):  # If it's a numpy array
        Image.fromarray(np.uint8(output1)).save(image_path)
    elif isinstance(output1, Image.Image):  # If it's already a PIL Image
        output1.save(image_path)
    else:
        print(f"Warning: Unexpected type {type(output1)} for output1.")
    return output1

# Gradio interface layout and logic
with gr.Blocks() as myface:
    # (Omitted for brevity, similar to your original code)

# Launch the Gradio interface
myface.queue(concurrency_count=200)
myface.launch(inline=True, show_api=False, max_threads=400)