artificialguybr's picture
add new params (#3)
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import gradio as gr
import requests
import io
from PIL import Image
import json
import os
import logging
import math
from tqdm import tqdm
import time
#logging.basicConfig(level=logging.DEBUG)
with open('loras.json', 'r') as f:
loras = json.load(f)
def update_selection(selected_state: gr.SelectData):
logging.debug(f"Inside update_selection, selected_state: {selected_state}")
selected_lora_index = selected_state.index
selected_lora = loras[selected_lora_index]
new_placeholder = f"Type a prompt for {selected_lora['title']}"
lora_repo = selected_lora["repo"]
updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
return (
gr.update(placeholder=new_placeholder),
updated_text, # Retorna o texto Markdown atualizado
selected_state
)
def run_lora(prompt, selected_state, progress=gr.Progress(track_tqdm=True)):
logging.debug(f"Inside run_lora, selected_state: {selected_state}")
if not selected_state:
logging.error("selected_state is None or empty.")
raise gr.Error("You must select a LoRA before proceeding.") # Popup error when no LoRA is selected
selected_lora_index = selected_state.index # Changed this line
selected_lora = loras[selected_lora_index]
api_url = f"https://api-inference.huggingface.co/models/{selected_lora['repo']}"
trigger_word = selected_lora["trigger_word"]
#token = os.getenv("API_TOKEN")
payload = {
"inputs": f"{prompt} {trigger_word}",
"parameters":{"negative_prompt": "bad art, ugly, watermark, deformed", "num_inference_steps": 30, "scheduler":"DPMSolverMultistepScheduler"},
}
#headers = {"Authorization": f"Bearer {token}"}
# Add a print statement to display the API request
print(f"API Request: {api_url}")
#print(f"API Headers: {headers}")
print(f"API Payload: {payload}")
error_count = 0
pbar = tqdm(total=None, desc="Loading model")
while(True):
response = requests.post(api_url, json=payload)
if response.status_code == 200:
return Image.open(io.BytesIO(response.content))
elif response.status_code == 503:
#503 is triggered when the model is doing cold boot. It also gives you a time estimate from when the model is loaded but it is not super precise
time.sleep(1)
pbar.update(1)
elif response.status_code == 500 and error_count < 5:
print(response.content)
time.sleep(1)
error_count += 1
continue
else:
logging.error(f"API Error: {response.status_code}")
raise gr.Error("API Error: Unable to fetch the image.") # Raise a Gradio error here
with gr.Blocks(css="custom.css") as app:
title = gr.Markdown("# artificialguybr LoRA portfolio")
description = gr.Markdown(
"### This is my portfolio. Follow me on Twitter [@artificialguybr](https://twitter.com/artificialguybr). \n"
"**Note**: The speed and generation quality are for demonstration purposes. "
"For best quality, use Auto or Comfy or Diffusers. \n"
"**Warning**: The API might take some time to deliver the image. \n"
"Special thanks to Hugging Face for their free inference API."
)
selected_state = gr.State()
with gr.Row():
gallery = gr.Gallery(
[(item["image"], item["title"]) for item in loras],
label="LoRA Gallery",
allow_preview=False,
columns=3
)
with gr.Column():
prompt_title = gr.Markdown("### Click on a LoRA in the gallery to select it")
selected_info = gr.Markdown("") # Novo componente Markdown para exibir o texto da LoRA selecionada
with gr.Row():
prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="Type a prompt after selecting a LoRA")
button = gr.Button("Run")
result = gr.Image(interactive=False, label="Generated Image")
gallery.select(
update_selection,
outputs=[prompt, selected_info, selected_state] # Adicionado selected_info aqui
)
prompt.submit(
fn=run_lora,
inputs=[prompt, selected_state],
outputs=[result]
)
button.click(
fn=run_lora,
inputs=[prompt, selected_state],
outputs=[result]
)
app.queue(max_size=20, concurrency_count=5)
app.launch()