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"}, } #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()