app.py

#!/usr/bin/env python3
import os
import base64
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from PIL import Image
import gradio as gr
import torchvision.transforms as transforms
from transformers import AutoModel, AutoTokenizer
from diffusers import StableDiffusionPipeline
from torch.utils.data import Dataset, DataLoader
import asyncio
import aiofiles
import fitz  # PyMuPDF
import requests
import logging
from io import BytesIO
from dataclasses import dataclass
from typing import Optional

# Logging setup
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

# Neural network layers for line drawing
norm_layer = nn.InstanceNorm2d

# Residual Block for Generator
class ResidualBlock(nn.Module):
    def __init__(self, in_features):
        super(ResidualBlock, self).__init__()
        conv_block = [
            nn.ReflectionPad2d(1),
            nn.Conv2d(in_features, in_features, 3),
            norm_layer(in_features),
            nn.ReLU(inplace=True),
            nn.ReflectionPad2d(1),
            nn.Conv2d(in_features, in_features, 3),
            norm_layer(in_features)
        ]
        self.conv_block = nn.Sequential(*conv_block)

    def forward(self, x):
        return x + self.conv_block(x)

# Generator for Line Drawings
class Generator(nn.Module):
    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
        super(Generator, self).__init__()
        model0 = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, 7), norm_layer(64), nn.ReLU(inplace=True)]
        self.model0 = nn.Sequential(*model0)
        model1 = []
        in_features, out_features = 64, 128
        for _ in range(2):
            model1 += [nn.Conv2d(in_features, out_features, 3, stride=2, padding=1), norm_layer(out_features), nn.ReLU(inplace=True)]
            in_features, out_features = out_features, out_features * 2
        self.model1 = nn.Sequential(*model1)
        model2 = [ResidualBlock(in_features) for _ in range(n_residual_blocks)]
        self.model2 = nn.Sequential(*model2)
        model3 = []
        out_features = in_features // 2
        for _ in range(2):
            model3 += [nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1), norm_layer(out_features), nn.ReLU(inplace=True)]
            in_features, out_features = out_features, out_features // 2
        self.model3 = nn.Sequential(*model3)
        model4 = [nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, 7)]
        if sigmoid:
            model4 += [nn.Sigmoid()]
        self.model4 = nn.Sequential(*model4)

    def forward(self, x, cond=None):
        out = self.model0(x)
        out = self.model1(out)
        out = self.model2(out)
        out = self.model3(out)
        out = self.model4(out)
        return out

# Load Line Drawing Models
model1 = Generator(3, 1, 3)
model2 = Generator(3, 1, 3)
try:
    model1.load_state_dict(torch.load('model.pth', map_location='cpu', weights_only=True))
    model2.load_state_dict(torch.load('model2.pth', map_location='cpu', weights_only=True))
except FileNotFoundError:
    logger.warning("Model files not found. Please ensure 'model.pth' and 'model2.pth' are available.")
model1.eval()
model2.eval()

# Tiny Diffusion Model
class TinyUNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=3):
        super(TinyUNet, self).__init__()
        self.down1 = nn.Conv2d(in_channels, 32, 3, padding=1)
        self.down2 = nn.Conv2d(32, 64, 3, padding=1, stride=2)
        self.mid = nn.Conv2d(64, 128, 3, padding=1)
        self.up1 = nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1)
        self.up2 = nn.Conv2d(64 + 32, 32, 3, padding=1)
        self.out = nn.Conv2d(32, out_channels, 3, padding=1)
        self.time_embed = nn.Linear(1, 64)

    def forward(self, x, t):
        t_embed = F.relu(self.time_embed(t.unsqueeze(-1))).view(t_embed.size(0), t_embed.size(1), 1, 1)
        x1 = F.relu(self.down1(x))
        x2 = F.relu(self.down2(x1))
        x_mid = F.relu(self.mid(x2)) + t_embed
        x_up1 = F.relu(self.up1(x_mid))
        x_up2 = F.relu(self.up2(torch.cat([x_up1, x1], dim=1)))
        return self.out(x_up2)

class TinyDiffusion:
    def __init__(self, model, timesteps=100):
        self.model = model
        self.timesteps = timesteps
        self.beta = torch.linspace(0.0001, 0.02, timesteps)
        self.alpha = 1 - self.beta
        self.alpha_cumprod = torch.cumprod(self.alpha, dim=0)

    def train(self, images, epochs=10):
        dataset = [torch.tensor(np.array(img.convert("RGB")).transpose(2, 0, 1), dtype=torch.float32) / 255.0 for img in images]
        dataloader = DataLoader(dataset, batch_size=1, shuffle=True)
        optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-4)
        device = torch.device("cpu")
        self.model.to(device)
        for epoch in range(epochs):
            total_loss = 0
            for x in dataloader:
                x = x.to(device)
                t = torch.randint(0, self.timesteps, (x.size(0),), device=device).float()
                noise = torch.randn_like(x)
                alpha_t = self.alpha_cumprod[t.long()].view(-1, 1, 1, 1)
                x_noisy = torch.sqrt(alpha_t) * x + torch.sqrt(1 - alpha_t) * noise
                pred_noise = self.model(x_noisy, t)
                loss = F.mse_loss(pred_noise, noise)
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
                total_loss += loss.item()
            logger.info(f"Epoch {epoch + 1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}")
        return self

    def generate(self, size=(64, 64), steps=100):
        device = torch.device("cpu")
        x = torch.randn(1, 3, size[0], size[1], device=device)
        for t in reversed(range(steps)):
            t_tensor = torch.full((1,), t, device=device, dtype=torch.float32)
            alpha_t = self.alpha_cumprod[t].view(-1, 1, 1, 1)
            pred_noise = self.model(x, t_tensor)
            x = (x - (1 - self.alpha[t]) / torch.sqrt(1 - alpha_t) * pred_noise) / torch.sqrt(self.alpha[t])
            if t > 0:
                x += torch.sqrt(self.beta[t]) * torch.randn_like(x)
        x = torch.clamp(x * 255, 0, 255).byte()
        return Image.fromarray(x.squeeze(0).permute(1, 2, 0).cpu().numpy())

# Utility Functions
def generate_filename(sequence, ext="png"):
    timestamp = time.strftime("%d%m%Y%H%M%S")
    return f"{sequence}_{timestamp}.{ext}"

def predict_line_drawing(input_img, ver):
    original_img = Image.open(input_img) if isinstance(input_img, str) else input_img
    original_size = original_img.size
    transform = transforms.Compose([
        transforms.Resize(256, Image.BICUBIC),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])
    input_tensor = transform(original_img).unsqueeze(0)
    with torch.no_grad():
        output = model2(input_tensor) if ver == 'Simple Lines' else model1(input_tensor)
    output_img = transforms.ToPILImage()(output.squeeze().cpu().clamp(0, 1))
    return output_img.resize(original_size, Image.BICUBIC)

async def process_ocr(image):
    tokenizer = AutoTokenizer.from_pretrained("ucaslcl/GOT-OCR2_0", trust_remote_code=True)
    model = AutoModel.from_pretrained("ucaslcl/GOT-OCR2_0", trust_remote_code=True, torch_dtype=torch.float32).to("cpu").eval()
    result = model.chat(tokenizer, image, ocr_type='ocr')
    output_file = generate_filename("ocr_output", "txt")
    async with aiofiles.open(output_file, "w") as f:
        await f.write(result)
    return result, output_file

async def process_diffusion(images):
    unet = TinyUNet()
    diffusion = TinyDiffusion(unet)
    diffusion.train(images)
    gen_image = diffusion.generate()
    output_file = generate_filename("diffusion_output", "png")
    gen_image.save(output_file)
    return gen_image, output_file

def download_pdf(url):
    output_path = f"pdf_{int(time.time())}.pdf"
    response = requests.get(url, stream=True, timeout=10)
    if response.status_code == 200:
        with open(output_path, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
        return output_path
    return None

# Gradio Blocks UI
with gr.Blocks(title="Mystical AI Vision Studio 🌌", css="""
    .gr-button {background-color: #4CAF50; color: white;}
    .gr-tab {border: 2px solid #2196F3; border-radius: 5px;}
    #gallery img {border: 1px solid #ddd; border-radius: 4px;}
""") as demo:
    gr.Markdown("<h1 style='text-align: center; color: #2196F3;'>Mystical AI Vision Studio 🌌</h1>")
    gr.Markdown("<p style='text-align: center;'>Transform images into line drawings, extract text with OCR, and craft unique art with diffusion!</p>")

    with gr.Tab("Image to Line Drawings 🎨"):
        with gr.Row():
            with gr.Column():
                img_input = gr.Image(type="pil", label="Upload Image")
                version = gr.Radio(['Complex Lines', 'Simple Lines'], label='Style', value='Simple Lines')
                submit_btn = gr.Button("Generate Line Drawing")
            with gr.Column():
                line_output = gr.Image(type="pil", label="Line Drawing")
                download_btn = gr.Button("Download Output")
        submit_btn.click(predict_line_drawing, inputs=[img_input, version], outputs=line_output)
        download_btn.click(lambda x: gr.File(x, label="Download Line Drawing"), inputs=line_output, outputs=None)

    with gr.Tab("OCR Vision 🔍"):
        with gr.Row():
            with gr.Column():
                ocr_input = gr.Image(type="pil", label="Upload Image or PDF Snapshot")
                ocr_btn = gr.Button("Extract Text")
            with gr.Column():
                ocr_text = gr.Textbox(label="Extracted Text", interactive=False)
                ocr_file = gr.File(label="Download OCR Result")
        async def run_ocr(img):
            result, file_path = await process_ocr(img)
            return result, file_path
        ocr_btn.click(run_ocr, inputs=ocr_input, outputs=[ocr_text, ocr_file])

    with gr.Tab("Custom Diffusion 🎨🤓"):
        with gr.Row():
            with gr.Column():
                diffusion_input = gr.File(label="Upload Images for Training", multiple=True)
                diffusion_btn = gr.Button("Train & Generate")
            with gr.Column():
                diffusion_output = gr.Image(type="pil", label="Generated Art")
                diffusion_file = gr.File(label="Download Art")
        async def run_diffusion(files):
            images = [Image.open(BytesIO(f.read())) for f in files]
            img, file_path = await process_diffusion(images)
            return img, file_path
        diffusion_btn.click(run_diffusion, inputs=diffusion_input, outputs=[diffusion_output, diffusion_file])

    with gr.Tab("PDF Downloader 📥"):
        with gr.Row():
            pdf_url = gr.Textbox(label="Enter PDF URL")
            pdf_btn = gr.Button("Download PDF")
            pdf_output = gr.File(label="Downloaded PDF")
        pdf_btn.click(download_pdf, inputs=pdf_url, outputs=pdf_output)

    with gr.Tab("Gallery 📸"):
        gallery = gr.Gallery(label="Processed Outputs", elem_id="gallery")
        def update_gallery():
            files = [f for f in os.listdir('.') if f.endswith(('.png', '.txt', '.pdf'))]
            return [f for f in files]
        gr.Button("Refresh Gallery").click(update_gallery, outputs=gallery)

    # JavaScript for dynamic UI enhancements
    gr.HTML("""
    <script>
        document.addEventListener('DOMContentLoaded', () => {
            const buttons = document.querySelectorAll('.gr-button');
            buttons.forEach(btn => {
                btn.addEventListener('mouseover', () => btn.style.backgroundColor = '#45a049');
                btn.addEventListener('mouseout', () => btn.style.backgroundColor = '#4CAF50');
            });
        });
    </script>
    """)

demo.launch()