import gradio as gr
import requests
import os
from PIL import Image
from io import BytesIO
from tqdm import tqdm
import time

# Defining the repository information and the trigger word
repo = "artificialguybr/ColoringBookRedmond-V2"
trigger_word = "Coloring Book, ColoringBookAF, "

def generate_image(prompt):
    print("Generating image with prompt:", prompt)
    api_url = f"https://api-inference.huggingface.co/models/{repo}"
    #token = os.getenv("API_TOKEN")  # Uncomment and use your Hugging Face API token
    headers = {
        #"Authorization": f"Bearer {token}"
    }
    full_prompt = f"{prompt} {trigger_word}"
    payload = {
        "inputs": full_prompt,
        "parameters": {
            "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)",
            "num_inference_steps": 30,
            "scheduler": "DPMSolverMultistepScheduler"
        },
    }

    error_count = 0
    pbar = tqdm(total=None, desc="Loading model")
    while True:
        print("Sending request to API...")
        response = requests.post(api_url, headers=headers, json=payload)
        print("API response status code:", response.status_code)
        if response.status_code == 200:
            print("Image generation successful!")
            return Image.open(BytesIO(response.content))  # Changed to match the first code
        elif response.status_code == 503:
            time.sleep(1)
            pbar.update(1)
        elif response.status_code == 500 and error_count < 5:
            time.sleep(1)
            error_count += 1
        else:
            print("API Error:", response.status_code)
            raise Exception(f"API Error: {response.status_code}")

iface = gr.Interface(
    fn=generate_image,
    inputs=gr.Textbox(lines=2, placeholder="Type your prompt here..."),
    outputs="image",
    title="Coloring Book XL Image Generator",
    description="Powered by the generous GPU time from Redmond.AI, this LORA, fine-tuned on SD XL 1.0, excels at creating Coloring Book -themed images across a wide range of subjects. Optimized for 1024x1024 resolution, it incorporates the specific tag 'Coloring Book, ColoringBookAF' directly in the HF Space for ease of use. If you appreciate this model and wish to support, consider a donation via [Patreon](https://www.patreon.com/user?u=81570187) or [Ko-fi](https://ko-fi.com/artificialguybr). Stay updated on new models by following on [Twitter](https://twitter.com/artificialguybr).",
    examples=[["Cute Panda"], ["Little Penguin"]]
)

print("Launching Gradio interface...")
iface.launch()