import gradio as gr import requests import io from PIL import Image import json import os # Load LoRAs from JSON with open('loras.json', 'r') as f: loras = json.load(f) # API call function def query(payload, api_url, token): headers = {"Authorization": f"Bearer {token}"} response = requests.post(api_url, headers=headers, json=payload) return io.BytesIO(response.content) # Define the function to run when the button is clicked def run_lora(prompt, weight): selected_lora = loras[0] 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}"} image_bytes = query(payload, api_url, token) return Image.open(image_bytes) # Gradio UI print("Before Gradio Interface") with gr.Blocks() as app: title = gr.HTML("

LoRA the Explorer

") gallery = gr.Gallery( [(item["image"], item["title"]) for item in loras], label="LoRA Gallery", allow_preview=False, columns=3, ) prompt = gr.Textbox(label="Prompt", lines=1, max_lines=1, placeholder="Type a prompt after selecting a LoRA") advanced_options = gr.Accordion("Advanced options", open=False) weight = gr.Slider(0, 10, value=1, step=0.1, label="LoRA weight") result = gr.Image(interactive=False, label="Generated Image") with gr.Row(): with gr.Column(): title gallery prompt advanced_options weight gr.Button("Run").click( fn=run_lora, inputs=[prompt, weight], outputs=[result] ) with gr.Column(): result print("After Gradio Interface") # Launch the Gradio interface with a queue app.launch(share=True, debug=True, queue=True)