app.py

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):
    print("Inside run_lora")
    selected_lora = loras[0]  # You may need to adjust this index if you have multiple models
    api_url = f"https://api-inference.huggingface.co/models/{selected_lora['repo']}"
    trigger_word = selected_lora["trigger_word"]
    token = os.getenv("API_TOKEN")  # This will read the API token set in your managed environment
    payload = {"inputs": f"{prompt} {trigger_word}"}
    
    print("Calling query function...")
    image_bytes = query(payload, api_url, token)
    print("Query function executed successfully.")
    return Image.open(image_bytes)

# Gradio UI
print("Before Gradio Interface")

title = gr.HTML("<h1>LoRA the Explorer</h1>")

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")

gr.Interface(
    fn=run_lora,
    inputs=[title, gallery, prompt, {"Advanced Options": advanced_options}, weight],
    outputs=[result],
    css="custom.css"  # Make sure your custom CSS file is in the same directory
).launch()

print("After Gradio Interface")