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# import gradio as gr
# gr.Interface.load("models/Akbartus/Lora360").launch(show_api=True)
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)
# Select the default LoRA
default_lora = loras[0] # Assuming the first LoRA is the default one
def run_lora(prompt, progress=gr.Progress(track_tqdm=True)):
logging.debug(f"Inside run_lora")
api_url = f"https://api-inference.huggingface.co/models/{default_lora['repo']}"
trigger_word = default_lora["trigger_word"]
payload = {
"inputs": f"{prompt} {trigger_word}",
"parameters":{"negative_prompt": "bad art, ugly, watermark, deformed", "num_inference_steps": 30, "scheduler":"DPMSolverMultistepScheduler"},
}
# Add a print statement to display the API request
print(f"API Request: {api_url}")
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:
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("# LoRA 360 Demonstration")
description = gr.Markdown(
"### Lora 360 demonstration and API endpoint."
)
with gr.Row():
prompt_title = gr.Markdown(f"### Type a prompt for {default_lora['title']}")
with gr.Row():
prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder=f"Type a prompt for {default_lora['title']}")
button = gr.Button("Run")
result = gr.Image(interactive=False, label="Generated Image")
prompt.submit(
fn=run_lora,
inputs=[prompt],
outputs=[result]
)
button.click(
fn=run_lora,
inputs=[prompt],
outputs=[result]
)
app.queue(max_size=20, concurrency_count=5)
app.launch()
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