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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)
# Define the function to run when the button is clicked
def run_lora(prompt, selected_state):
if not selected_state:
raise gr.Error("You must select a LoRA")
selected_lora_index = selected_state['index']
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}"}
# API call
headers = {"Authorization": f"Bearer {token}"}
response = requests.post(api_url, headers=headers, json=payload)
if response.status_code == 200:
return Image.open(io.BytesIO(response.content))
else:
return "API Error"
# Placeholder for gallery.select function
def update_selection(selected=None):
if selected is None:
return None
return {'index': selected[0]},
# Gradio UI
with gr.Blocks(css="custom.css") as app:
title = gr.HTML("<h1>LoRA the Explorer</h1>")
selected_state = gr.State() # Initialize with empty 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")
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=[selected_state]
)
button.click(
fn=run_lora,
inputs=[prompt, selected_state],
outputs=[result]
)
app.queue(max_size=20)
app.launch()
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