app.py

import subprocess
import os
import gradio as gr
import torch
import numpy as np
from PIL import Image, ImageEnhance
import spaces
from pymongo import MongoClient
from pymongo.errors import ConnectionError

# MongoDB connection
mongo_client = None
try:
    mongo_client = MongoClient("mongodb+srv://skandanv:[email protected]/?retryWrites=true&w=majority&appName=cluster1")
    db = mongo_client['minecraft_skin_generator']  # Replace with your database name
    collection = db['generated_skins']  # Collection to store generated skins
    connection_message = "Connected to MineSkin Server"
except ConnectionError:
    connection_message = "Failed to connect to MineSkin Server"

if torch.cuda.is_available():
    device = "cuda"
    print("Using GPU")
else:
    device = "cpu"
    print("Using CPU")

MAX_SEED = np.iinfo(np.int32).max

subprocess.run(["git", "clone", "https://github.com/Nick088Official/Stable_Diffusion_Finetuned_Minecraft_Skin_Generator.git"])
os.chdir("Stable_Diffusion_Finetuned_Minecraft_Skin_Generator")

@spaces.GPU(duration=75)
def run_inference(prompt, stable_diffusion_model, num_inference_steps, guidance_scale, model_precision_type, seed, filename, model_3d, verbose):
    # Inference
    if stable_diffusion_model == '2':
        sd_model = "minecraft-skins"
    elif stable_diffusion_model == 'xl':
        sd_model = "minecraft-skins-sdxl"

    inference_command = f"python Scripts/{sd_model}.py '{prompt}' {num_inference_steps} {guidance_scale} {model_precision_type} {seed} {filename} {'--model_3d' if model_3d else ''} {'--verbose' if verbose else ''}"
    
    os.system(inference_command)

    # File paths for generated assets
    image_path = os.path.join(f"output_minecraft_skins/{filename}")
    model_path = os.path.join(f"output_minecraft_skins/{filename}_3d_model.glb") if model_3d else None

    # Prepare data for MongoDB
    skin_data = {
        'prompt': prompt,
        'filename': filename,
        'image_path': image_path,
        'model_path': model_path,
        'num_inference_steps': num_inference_steps,
        'guidance_scale': guidance_scale,
        'model_precision_type': model_precision_type,
        'seed': seed,
        'model_3d': model_3d,
        'verbose': verbose
    }

    # Insert generated skin data into MongoDB and show alert if successful
    try:
        collection.insert_one(skin_data)
        success_message = "The Skin has been pushed to MineSkin Server"
        alert_type = "success"  # Gradio Alert type for success
    except Exception as e:
        success_message = f"Failed to push skin to database: {e}"
        alert_type = "error"

    return image_path, model_path, success_message, alert_type

# Define Gradio UI components
prompt = gr.Textbox(label="Your Prompt", info="What the Minecraft Skin should look like")
stable_diffusion_model = gr.Dropdown(['2', 'xl'], value="xl", label="Stable Diffusion Model", info="Choose which Stable Diffusion Model to use, xl understands prompts better")
num_inference_steps = gr.Slider(label="Number of Inference Steps", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference", minimum=1, maximum=50, value=25, step=1)
guidance_scale = gr.Slider(label="Guidance Scale", info="Controls how much the image generation process follows the text prompt. Higher values make the image stick more closely to the input text.", minimum=0.0, maximum=10.0, value=7.5, step=0.1)
model_precision_type = gr.Dropdown(["fp16", "fp32"], value="fp16", label="Model Precision Type", info="The precision type to load the model, like fp16 which is faster, or fp32 which is more precise but more resource consuming")
seed = gr.Slider(value=42, minimum=0, maximum=MAX_SEED, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
filename = gr.Textbox(label="Output Image Name", info="The name of the file of the output image skin, keep the .png", value="output-skin.png")
model_3d = gr.Checkbox(label="See as 3D Model too", info="View the generated skin as a 3D Model too", value=True)
verbose = gr.Checkbox(label="Verbose Output", info="Produce more detailed output while running", value=False)

# Create the Gradio interface
output_image = gr.Image(label="Generated Minecraft Skin Image Asset", elem_classes="pixelated checkered")
output_model = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model View of the Skin")
output_message = gr.Alert()

gr.Interface(
    fn=run_inference,
    inputs=[
        prompt,
        stable_diffusion_model,
        num_inference_steps,
        guidance_scale,
        model_precision_type,
        seed,
        filename,
        model_3d,
        verbose
    ],
    outputs=[
        output_image,
        output_model,
        output_message
    ],
    title="Minecraft Skin Generator",
    description="Make AI generated Minecraft Skins by a Finetuned Stable Diffusion Version!<br>Github Repository & Model used: https://github.com/Nick088Official/Stable_Diffusion_Finetuned_Minecraft_Skin_Generator<br>Credits: [Monadical-SAS](https://github.com/Monadical-SAS/minecraft_skin_generator) (Creators of the model), [Nick088](https://linktr.ee/Nick088) (Improving usage of the model), daroche (helping me fix the 3d model texture issue), [Brottweiler](https://gist.github.com/Brottweiler/483d0856c6692ef70cf90bf1a85ce364)(script to fix the 3d model texture), [not-holar](https://huggingface.co/not-holar) (made the rendering of the image asset in the web ui look pixelated like minecraft and have a checkered background),[meew](https://huggingface.co/spaces/meeww/Minecraft_Skin_Generator/blob/main/models/player_model.glb) (Minecraft Player 3d model) <br> [![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/AQsmBmgEPy)",
    css=".pixelated {image-rendering: pixelated} .checkered img {background-image: url('data:image/svg+xml,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"2\" height=\"2\" fill-opacity=\".15\"><rect x=\"1\" width=\"1\" height=\"1\"/><rect y=\"1\" width=\"1\" height=\"1\"/></svg>');background-size: 16px;}"
).launch(show_api=True, share=True)

# Show connection message as alert when the app starts
if mongo_client:
    gr.Interface().launch()
    output_message.update(value=connection_message, type="info")