app.py

import gradio as gr
import os, pandas as pd
from transformers import pipeline, set_seed
import random, re
import sys
from pathlib import Path

# read models from csv and init
models = [r for idx,r in pd.read_csv('models.csv').iterrows()]
current_model = models[0]

# pipeline and core fncn for prompt generation
gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
with open("ideas.txt", "r") as f:
    line = f.readlines()

def generate(starting_text):
    seed = random.randint(100, 1000000)
    set_seed(seed)
    if starting_text == "":
        starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
        starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
    response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4)
    response_list = []
    for x in response:
        resp = x['generated_text'].strip()
        if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
            response_list.append(resp+'\n')
    response_end = "\n".join(response_list)
    response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
    response_end = response_end.replace("<", "").replace(">", "")
    if response_end != "":
        return response_end

examples = []
for x in range(8):
    examples.append(line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize())
    
# text_gen = gr.Interface.load("spaces/Omnibus/MagicPrompt-Stable-Diffusion_link") 

models2 = [] #can improve this, no need to load and keep all models in memory, increases cpu usage + latency
for model in models:
    model_url = f"models/{model['url']}"
    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
    models2.append(loaded_model)


def text_it(inputs,text_gen=generate):
    return text_gen(inputs).split('\n\n')[0]


def set_model(current_model_index):
    global current_model
    current_model = models[current_model_index]
    return gr.update(value=f"{current_model['name']}")


def send_it(inputs, model_choice):
    proc = models2[model_choice]
    return proc(inputs)


with gr.Blocks() as myface:
    gr.HTML()
    with gr.Row():
        with gr.Row():
            input_text = gr.Textbox(label="Prompt idea", lines=1)
            # Model selection dropdown
            model_name1 = gr.Dropdown(
                label="Choose Model",
                choices=[m["name"] for m in models],
                type="index",
                value=current_model["name"],
                interactive=True,
            )
        with gr.Row():
            see_prompts = gr.Button("Step 1 - Generate Prompts", variant="primary")
            run = gr.Button("Step 2 - Generate Images", variant="primary")
    
    with gr.Row():
        output1 = gr.Image(label="")
        output2 = gr.Image(label="")
        output3 = gr.Image(label="")
    with gr.Row():
        magic1 = gr.Textbox(label="Generated Prompt", lines=2)
        magic2 = gr.Textbox(label="Generated Prompt", lines=2)
        magic3 = gr.Textbox(label="Generated Prompt", lines=2)
    with gr.Row():
        output4 = gr.Image(label="")
        output5 = gr.Image(label="")
        output6 = gr.Image(label="")
    with gr.Row():
        magic4 = gr.Textbox(label="Generated Prompt", lines=2)
        magic5 = gr.Textbox(label="Generated Prompt", lines=2)
        magic6 = gr.Textbox(label="Generated Prompt", lines=2)
    with gr.Row():
        output7 = gr.Image(label="")
        output8 = gr.Image(label="")
        output9 = gr.Image(label="")
    with gr.Row():
        magic7 = gr.Textbox(label="Generated Prompt", lines=2)
        magic8 = gr.Textbox(label="Generated Prompt", lines=2)
        magic9 = gr.Textbox(label="Generated Prompt", lines=2)

    model_name1.change(set_model, inputs=model_name1, outputs=model_name1)

    run.click(send_it, inputs=[magic1, model_name1], outputs=[output1])
    run.click(send_it, inputs=[magic2, model_name1], outputs=[output2])
    run.click(send_it, inputs=[magic3, model_name1], outputs=[output3])
    run.click(send_it, inputs=[magic4, model_name1], outputs=[output4])
    run.click(send_it, inputs=[magic5, model_name1], outputs=[output5])
    run.click(send_it, inputs=[magic6, model_name1], outputs=[output6])
    run.click(send_it, inputs=[magic7, model_name1], outputs=[output7])
    run.click(send_it, inputs=[magic8, model_name1], outputs=[output8])
    run.click(send_it, inputs=[magic9, model_name1], outputs=[output9])

    see_prompts.click(text_it, inputs=[input_text], outputs=[magic1])
    see_prompts.click(text_it, inputs=[input_text], outputs=[magic2])
    see_prompts.click(text_it, inputs=[input_text], outputs=[magic3])
    see_prompts.click(text_it, inputs=[input_text], outputs=[magic4])
    see_prompts.click(text_it, inputs=[input_text], outputs=[magic5])
    see_prompts.click(text_it, inputs=[input_text], outputs=[magic6])
    see_prompts.click(text_it, inputs=[input_text], outputs=[magic7])
    see_prompts.click(text_it, inputs=[input_text], outputs=[magic8])
    see_prompts.click(text_it, inputs=[input_text], outputs=[magic9])
myface.queue(concurrency_count=64)
myface.launch(inline=True, show_api=False, max_threads=64)