backup-03252025.app.py

#!/usr/bin/env python3
import os
import glob
import base64
import time
import shutil
import streamlit as st
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
from diffusers import StableDiffusionPipeline
from torch.utils.data import Dataset, DataLoader
import csv
import fitz
import requests
from PIL import Image
import cv2
import numpy as np
import logging
import asyncio
import aiofiles
from io import BytesIO
from dataclasses import dataclass
from typing import Optional, Tuple
import zipfile
import math
import random
import re

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)
log_records = []

class LogCaptureHandler(logging.Handler):
    def emit(self, record):
        log_records.append(record)

logger.addHandler(LogCaptureHandler())

st.set_page_config(
    page_title="AI Vision & SFT Titans 🚀",
    page_icon="🤖",
    layout="wide",
    initial_sidebar_state="expanded",
    menu_items={
        'Get Help': 'https://huggingface.co/awacke1',
        'Report a Bug': 'https://huggingface.co/spaces/awacke1',
        'About': "AI Vision & SFT Titans: PDFs, OCR, Image Gen, Line Drawings, Custom Diffusion, and SFT on CPU! 🌌"
    }
)

if 'history' not in st.session_state:
    st.session_state['history'] = []
if 'builder' not in st.session_state:
    st.session_state['builder'] = None
if 'model_loaded' not in st.session_state:
    st.session_state['model_loaded'] = False
if 'processing' not in st.session_state:
    st.session_state['processing'] = {}
if 'asset_checkboxes' not in st.session_state:
    st.session_state['asset_checkboxes'] = {}
if 'downloaded_pdfs' not in st.session_state:
    st.session_state['downloaded_pdfs'] = {}
if 'unique_counter' not in st.session_state:
    st.session_state['unique_counter'] = 0
if 'selected_model_type' not in st.session_state:
    st.session_state['selected_model_type'] = "Causal LM"
if 'selected_model' not in st.session_state:
    st.session_state['selected_model'] = "None"
if 'cam0_file' not in st.session_state:
    st.session_state['cam0_file'] = None
if 'cam1_file' not in st.session_state:
    st.session_state['cam1_file'] = None

@dataclass
class ModelConfig:
    name: str
    base_model: str
    size: str
    domain: Optional[str] = None
    model_type: str = "causal_lm"
    @property
    def model_path(self):
        return f"models/{self.name}"

@dataclass
class DiffusionConfig:
    name: str
    base_model: str
    size: str
    domain: Optional[str] = None
    @property
    def model_path(self):
        return f"diffusion_models/{self.name}"

class ModelBuilder:
    def __init__(self):
        self.config = None
        self.model = None
        self.tokenizer = None
        self.jokes = ["Why did the AI go to therapy? Too many layers to unpack! 😂", "Training complete! Time for a binary coffee break. ☕"]
    def load_model(self, model_path: str, config: Optional[ModelConfig] = None):
        with st.spinner(f"Loading {model_path}... ⏳"):
            self.model = AutoModelForCausalLM.from_pretrained(model_path)
            self.tokenizer = AutoTokenizer.from_pretrained(model_path)
            if self.tokenizer.pad_token is None:
                self.tokenizer.pad_token = self.tokenizer.eos_token
            if config:
                self.config = config
            self.model.to("cuda" if torch.cuda.is_available() else "cpu")
        st.success(f"Model loaded! 🎉 {random.choice(self.jokes)}")
        return self
    def save_model(self, path: str):
        with st.spinner("Saving model... 💾"):
            os.makedirs(os.path.dirname(path), exist_ok=True)
            self.model.save_pretrained(path)
            self.tokenizer.save_pretrained(path)
        st.success(f"Model saved at {path}! ✅")

class DiffusionBuilder:
    def __init__(self):
        self.config = None
        self.pipeline = None
    def load_model(self, model_path: str, config: Optional[DiffusionConfig] = None):
        with st.spinner(f"Loading diffusion model {model_path}... ⏳"):
            self.pipeline = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float32).to("cpu")
            if config:
                self.config = config
        st.success(f"Diffusion model loaded! 🎨")
        return self
    def save_model(self, path: str):
        with st.spinner("Saving diffusion model... 💾"):
            os.makedirs(os.path.dirname(path), exist_ok=True)
            self.pipeline.save_pretrained(path)
        st.success(f"Diffusion model saved at {path}! ✅")
    def generate(self, prompt: str):
        return self.pipeline(prompt, num_inference_steps=20).images[0]

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

def pdf_url_to_filename(url):
    safe_name = re.sub(r'[<>:"/\\|?*]', '_', url)
    return f"{safe_name}.pdf"

def get_download_link(file_path, mime_type="application/pdf", label="Download"):
    with open(file_path, 'rb') as f:
        data = f.read()
    b64 = base64.b64encode(data).decode()
    return f'<a href="data:{mime_type};base64,{b64}" download="{os.path.basename(file_path)}">{label}</a>'

def zip_directory(directory_path, zip_path):
    with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
        for root, _, files in os.walk(directory_path):
            for file in files:
                zipf.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), os.path.dirname(directory_path)))

def get_model_files(model_type="causal_lm"):
    path = "models/*" if model_type == "causal_lm" else "diffusion_models/*"
    dirs = [d for d in glob.glob(path) if os.path.isdir(d)]
    return dirs if dirs else ["None"]

def get_gallery_files(file_types=["png", "pdf"]):
    return sorted(list(set([f for ext in file_types for f in glob.glob(f"*.{ext}")])))  # Deduplicate files

def get_pdf_files():
    return sorted(glob.glob("*.pdf"))

def download_pdf(url, output_path):
    try:
        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 True
    except requests.RequestException as e:
        logger.error(f"Failed to download {url}: {e}")
    return False

async def process_pdf_snapshot(pdf_path, mode="single"):
    start_time = time.time()
    status = st.empty()
    status.text(f"Processing PDF Snapshot ({mode})... (0s)")
    try:
        doc = fitz.open(pdf_path)
        output_files = []
        if mode == "single":
            page = doc[0]
            pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
            output_file = generate_filename("single", "png")
            pix.save(output_file)
            output_files.append(output_file)
        elif mode == "twopage":
            for i in range(min(2, len(doc))):
                page = doc[i]
                pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
                output_file = generate_filename(f"twopage_{i}", "png")
                pix.save(output_file)
                output_files.append(output_file)
        elif mode == "allpages":
            for i in range(len(doc)):
                page = doc[i]
                pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
                output_file = generate_filename(f"page_{i}", "png")
                pix.save(output_file)
                output_files.append(output_file)
        doc.close()
        elapsed = int(time.time() - start_time)
        status.text(f"PDF Snapshot ({mode}) completed in {elapsed}s!")
        update_gallery()
        return output_files
    except Exception as e:
        status.error(f"Failed to process PDF: {str(e)}")
        return []

async def process_ocr(image, output_file):
    start_time = time.time()
    status = st.empty()
    status.text("Processing GOT-OCR2_0... (0s)")
    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()
    # Save image to temporary file since GOT-OCR2_0 expects a file path
    temp_file = f"temp_{int(time.time())}.png"
    image.save(temp_file)
    result = model.chat(tokenizer, temp_file, ocr_type='ocr')
    os.remove(temp_file)  # Clean up temporary file
    elapsed = int(time.time() - start_time)
    status.text(f"GOT-OCR2_0 completed in {elapsed}s!")
    async with aiofiles.open(output_file, "w") as f:
        await f.write(result)
    update_gallery()
    return result

async def process_image_gen(prompt, output_file):
    start_time = time.time()
    status = st.empty()
    status.text("Processing Image Gen... (0s)")
    if st.session_state['builder'] and isinstance(st.session_state['builder'], DiffusionBuilder) and st.session_state['builder'].pipeline:
        pipeline = st.session_state['builder'].pipeline
    else:
        pipeline = StableDiffusionPipeline.from_pretrained("OFA-Sys/small-stable-diffusion-v0", torch_dtype=torch.float32).to("cpu")
    gen_image = pipeline(prompt, num_inference_steps=20).images[0]
    elapsed = int(time.time() - start_time)
    status.text(f"Image Gen completed in {elapsed}s!")
    gen_image.save(output_file)
    update_gallery()
    return gen_image

st.title("AI Vision & SFT Titans 🚀")

# Sidebar
model_type = st.sidebar.selectbox("Model Type", ["Causal LM", "Diffusion"], key="sidebar_model_type", index=0 if st.session_state['selected_model_type'] == "Causal LM" else 1)
model_dirs = get_model_files(model_type)
if model_dirs and st.session_state['selected_model'] == "None" and "None" not in model_dirs:
    st.session_state['selected_model'] = model_dirs[0]
selected_model = st.sidebar.selectbox("Select Saved Model", model_dirs, key="sidebar_model_select", index=model_dirs.index(st.session_state['selected_model']) if st.session_state['selected_model'] in model_dirs else 0)
if selected_model != "None" and st.sidebar.button("Load Model 📂"):
    builder = ModelBuilder() if model_type == "Causal LM" else DiffusionBuilder()
    config = (ModelConfig if model_type == "Causal LM" else DiffusionConfig)(name=os.path.basename(selected_model), base_model="unknown", size="small")
    builder.load_model(selected_model, config)
    st.session_state['builder'] = builder
    st.session_state['model_loaded'] = True
    st.rerun()

st.sidebar.header("Captured Files 📜")
cols = st.sidebar.columns(2)
with cols[0]:
    if st.button("Zip All 🤐"):
        zip_path = f"all_assets_{int(time.time())}.zip"
        with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
            for file in get_gallery_files():
                zipf.write(file, os.path.basename(file))
        st.sidebar.markdown(get_download_link(zip_path, "application/zip", "Download All Assets"), unsafe_allow_html=True)
with cols[1]:
    if st.button("Zap All! 🗑️"):
        for file in get_gallery_files():
            os.remove(file)
        st.session_state['asset_checkboxes'].clear()
        st.session_state['downloaded_pdfs'].clear()
        st.session_state['cam0_file'] = None
        st.session_state['cam1_file'] = None
        st.sidebar.success("All assets vaporized! 💨")
        st.rerun()

gallery_size = st.sidebar.slider("Gallery Size", 1, 10, 2)
def update_gallery():
    all_files = get_gallery_files()
    if all_files:
        st.sidebar.subheader("Asset Gallery 📸📖")
        cols = st.sidebar.columns(2)
        for idx, file in enumerate(all_files[:gallery_size * 2]):
            with cols[idx % 2]:
                st.session_state['unique_counter'] += 1
                unique_id = st.session_state['unique_counter']
                if file.endswith('.png'):
                    st.image(Image.open(file), caption=os.path.basename(file), use_container_width=True)
                else:
                    doc = fitz.open(file)
                    pix = doc[0].get_pixmap(matrix=fitz.Matrix(0.5, 0.5))
                    img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
                    st.image(img, caption=os.path.basename(file), use_container_width=True)
                    doc.close()
                checkbox_key = f"asset_{file}_{unique_id}"
                st.session_state['asset_checkboxes'][file] = st.checkbox(
                    "Use for SFT/Input", 
                    value=st.session_state['asset_checkboxes'].get(file, False), 
                    key=checkbox_key
                )
                mime_type = "image/png" if file.endswith('.png') else "application/pdf"
                st.markdown(get_download_link(file, mime_type, "Snag It! 📥"), unsafe_allow_html=True)
                if st.button("Zap It! 🗑️", key=f"delete_{file}_{unique_id}"):
                    os.remove(file)
                    if file in st.session_state['asset_checkboxes']:
                        del st.session_state['asset_checkboxes'][file]
                    if file.endswith('.pdf'):
                        url_key = next((k for k, v in st.session_state['downloaded_pdfs'].items() if v == file), None)
                        if url_key:
                            del st.session_state['downloaded_pdfs'][url_key]
                    if file == st.session_state['cam0_file']:
                        st.session_state['cam0_file'] = None
                    if file == st.session_state['cam1_file']:
                        st.session_state['cam1_file'] = None
                    st.sidebar.success(f"Asset {os.path.basename(file)} vaporized! 💨")
                    st.rerun()
update_gallery()

st.sidebar.subheader("Action Logs 📜")
log_container = st.sidebar.empty()
with log_container:
    for record in log_records:
        st.write(f"{record.asctime} - {record.levelname} - {record.message}")

st.sidebar.subheader("History 📜")
history_container = st.sidebar.empty()
with history_container:
    for entry in st.session_state['history'][-gallery_size * 2:]:
        st.write(entry)

tab1, tab2, tab3, tab4 = st.tabs([
    "Camera Snap 📷", "Download PDFs 📥", "Test OCR 🔍", "Build Titan 🌱"
])

with tab1:
    st.header("Camera Snap 📷")
    st.subheader("Single Capture")
    cols = st.columns(2)
    with cols[0]:
        cam0_img = st.camera_input("Take a picture - Cam 0", key="cam0")
        if cam0_img:
            filename = generate_filename("cam0")
            if st.session_state['cam0_file'] and os.path.exists(st.session_state['cam0_file']):
                os.remove(st.session_state['cam0_file'])
            with open(filename, "wb") as f:
                f.write(cam0_img.getvalue())
            st.session_state['cam0_file'] = filename
            entry = f"Snapshot from Cam 0: {filename}"
            if entry not in st.session_state['history']:
                st.session_state['history'] = [e for e in st.session_state['history'] if not e.startswith("Snapshot from Cam 0:")] + [entry]
            st.image(Image.open(filename), caption="Camera 0", use_container_width=True)
            logger.info(f"Saved snapshot from Camera 0: {filename}")
            update_gallery()
        elif st.session_state['cam0_file'] and os.path.exists(st.session_state['cam0_file']):
            st.image(Image.open(st.session_state['cam0_file']), caption="Camera 0", use_container_width=True)
    with cols[1]:
        cam1_img = st.camera_input("Take a picture - Cam 1", key="cam1")
        if cam1_img:
            filename = generate_filename("cam1")
            if st.session_state['cam1_file'] and os.path.exists(st.session_state['cam1_file']):
                os.remove(st.session_state['cam1_file'])
            with open(filename, "wb") as f:
                f.write(cam1_img.getvalue())
            st.session_state['cam1_file'] = filename
            entry = f"Snapshot from Cam 1: {filename}"
            if entry not in st.session_state['history']:
                st.session_state['history'] = [e for e in st.session_state['history'] if not e.startswith("Snapshot from Cam 1:")] + [entry]
            st.image(Image.open(filename), caption="Camera 1", use_container_width=True)
            logger.info(f"Saved snapshot from Camera 1: {filename}")
            update_gallery()
        elif st.session_state['cam1_file'] and os.path.exists(st.session_state['cam1_file']):
            st.image(Image.open(st.session_state['cam1_file']), caption="Camera 1", use_container_width=True)

with tab2:
    st.header("Download PDFs 📥")
    if st.button("Examples 📚"):
        example_urls = [
            "https://arxiv.org/pdf/2308.03892",
            "https://arxiv.org/pdf/1912.01703",
            "https://arxiv.org/pdf/2408.11039",
            "https://arxiv.org/pdf/2109.10282",
            "https://arxiv.org/pdf/2112.10752",
            "https://arxiv.org/pdf/2308.11236",
            "https://arxiv.org/pdf/1706.03762",
            "https://arxiv.org/pdf/2006.11239",
            "https://arxiv.org/pdf/2305.11207",
            "https://arxiv.org/pdf/2106.09685",
            "https://arxiv.org/pdf/2005.11401",
            "https://arxiv.org/pdf/2106.10504"
        ]
        st.session_state['pdf_urls'] = "\n".join(example_urls)
    
    url_input = st.text_area("Enter PDF URLs (one per line)", value=st.session_state.get('pdf_urls', ""), height=200)
    if st.button("Robo-Download 🤖"):
        urls = url_input.strip().split("\n")
        progress_bar = st.progress(0)
        status_text = st.empty()
        total_urls = len(urls)
        existing_pdfs = get_pdf_files()
        for idx, url in enumerate(urls):
            if url:
                output_path = pdf_url_to_filename(url)
                status_text.text(f"Fetching {idx + 1}/{total_urls}: {os.path.basename(output_path)}...")
                if output_path not in existing_pdfs:
                    if download_pdf(url, output_path):
                        st.session_state['downloaded_pdfs'][url] = output_path
                        logger.info(f"Downloaded PDF from {url} to {output_path}")
                        entry = f"Downloaded PDF: {output_path}"
                        if entry not in st.session_state['history']:
                            st.session_state['history'].append(entry)
                        st.session_state['asset_checkboxes'][output_path] = True  # Auto-check the box
                    else:
                        st.error(f"Failed to nab {url} 😿")
                else:
                    st.info(f"Already got {os.path.basename(output_path)}! Skipping... 🐾")
                    st.session_state['downloaded_pdfs'][url] = output_path
                progress_bar.progress((idx + 1) / total_urls)
        status_text.text("Robo-Download complete! 🚀")
        update_gallery()

    mode = st.selectbox("Snapshot Mode", ["Single Page (High-Res)", "Two Pages (High-Res)", "All Pages (High-Res)"], key="download_mode")
    if st.button("Snapshot Selected 📸"):
        selected_pdfs = [path for path in get_gallery_files() if path.endswith('.pdf') and st.session_state['asset_checkboxes'].get(path, False)]
        if selected_pdfs:
            for pdf_path in selected_pdfs:
                mode_key = {"Single Page (High-Res)": "single", "Two Pages (High-Res)": "twopage", "All Pages (High-Res)": "allpages"}[mode]
                snapshots = asyncio.run(process_pdf_snapshot(pdf_path, mode_key))
                for snapshot in snapshots:
                    st.image(Image.open(snapshot), caption=snapshot, use_container_width=True)
                    st.session_state['asset_checkboxes'][snapshot] = True  # Auto-check new snapshots
            update_gallery()
        else:
            st.warning("No PDFs selected for snapshotting! Check some boxes in the sidebar gallery.")

with tab3:
    st.header("Test OCR 🔍")
    all_files = get_gallery_files()
    if all_files:
        if st.button("OCR All Assets 🚀"):
            full_text = "# OCR Results\n\n"
            for file in all_files:
                if file.endswith('.png'):
                    image = Image.open(file)
                else:
                    doc = fitz.open(file)
                    pix = doc[0].get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
                    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
                    doc.close()
                output_file = generate_filename(f"ocr_{os.path.basename(file)}", "txt")
                result = asyncio.run(process_ocr(image, output_file))
                full_text += f"## {os.path.basename(file)}\n\n{result}\n\n"
                entry = f"OCR Test: {file} -> {output_file}"
                if entry not in st.session_state['history']:
                    st.session_state['history'].append(entry)
            md_output_file = f"full_ocr_{int(time.time())}.md"
            with open(md_output_file, "w") as f:
                f.write(full_text)
            st.success(f"Full OCR saved to {md_output_file}")
            st.markdown(get_download_link(md_output_file, "text/markdown", "Download Full OCR Markdown"), unsafe_allow_html=True)
        selected_file = st.selectbox("Select Image or PDF", all_files, key="ocr_select")
        if selected_file:
            if selected_file.endswith('.png'):
                image = Image.open(selected_file)
            else:
                doc = fitz.open(selected_file)
                pix = doc[0].get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
                image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
                doc.close()
            st.image(image, caption="Input Image", use_container_width=True)
            if st.button("Run OCR 🚀", key="ocr_run"):
                output_file = generate_filename("ocr_output", "txt")
                st.session_state['processing']['ocr'] = True
                result = asyncio.run(process_ocr(image, output_file))
                entry = f"OCR Test: {selected_file} -> {output_file}"
                if entry not in st.session_state['history']:
                    st.session_state['history'].append(entry)
                st.text_area("OCR Result", result, height=200, key="ocr_result")
                st.success(f"OCR output saved to {output_file}")
                st.session_state['processing']['ocr'] = False
            if selected_file.endswith('.pdf') and st.button("OCR All Pages 🚀", key="ocr_all_pages"):
                doc = fitz.open(selected_file)
                full_text = f"# OCR Results for {os.path.basename(selected_file)}\n\n"
                for i in range(len(doc)):
                    pix = doc[i].get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
                    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
                    output_file = generate_filename(f"ocr_page_{i}", "txt")
                    result = asyncio.run(process_ocr(image, output_file))
                    full_text += f"## Page {i + 1}\n\n{result}\n\n"
                    entry = f"OCR Test: {selected_file} Page {i + 1} -> {output_file}"
                    if entry not in st.session_state['history']:
                        st.session_state['history'].append(entry)
                md_output_file = f"full_ocr_{os.path.basename(selected_file)}_{int(time.time())}.md"
                with open(md_output_file, "w") as f:
                    f.write(full_text)
                st.success(f"Full OCR saved to {md_output_file}")
                st.markdown(get_download_link(md_output_file, "text/markdown", "Download Full OCR Markdown"), unsafe_allow_html=True)
    else:
        st.warning("No assets in gallery yet. Use Camera Snap or Download PDFs!")

with tab4:
    st.header("Build Titan 🌱")
    model_type = st.selectbox("Model Type", ["Causal LM", "Diffusion"], key="build_type")
    base_model = st.selectbox("Select Tiny Model", 
        ["HuggingFaceTB/SmolLM-135M", "Qwen/Qwen1.5-0.5B-Chat"] if model_type == "Causal LM" else 
        ["OFA-Sys/small-stable-diffusion-v0", "stabilityai/stable-diffusion-2-base"])
    model_name = st.text_input("Model Name", f"tiny-titan-{int(time.time())}")
    domain = st.text_input("Target Domain", "general")
    if st.button("Download Model ⬇️"):
        config = (ModelConfig if model_type == "Causal LM" else DiffusionConfig)(name=model_name, base_model=base_model, size="small", domain=domain)
        builder = ModelBuilder() if model_type == "Causal LM" else DiffusionBuilder()
        builder.load_model(base_model, config)
        builder.save_model(config.model_path)
        st.session_state['builder'] = builder
        st.session_state['model_loaded'] = True
        st.session_state['selected_model_type'] = model_type
        st.session_state['selected_model'] = config.model_path
        entry = f"Built {model_type} model: {model_name}"
        if entry not in st.session_state['history']:
            st.session_state['history'].append(entry)
        st.success(f"Model downloaded and saved to {config.model_path}! 🎉")
        st.rerun()

tab5 = st.tabs(["Test Image Gen 🎨"])[0]
with tab5:
    st.header("Test Image Gen 🎨")
    all_files = get_gallery_files()
    if all_files:
        selected_file = st.selectbox("Select Image or PDF", all_files, key="gen_select")
        if selected_file:
            if selected_file.endswith('.png'):
                image = Image.open(selected_file)
            else:
                doc = fitz.open(selected_file)
                pix = doc[0].get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
                image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
                doc.close()
            st.image(image, caption="Reference Image", use_container_width=True)
            prompt = st.text_area("Prompt", "Generate a neon superhero version of this image", key="gen_prompt")
            if st.button("Run Image Gen 🚀", key="gen_run"):
                output_file = generate_filename("gen_output", "png")
                st.session_state['processing']['gen'] = True
                result = asyncio.run(process_image_gen(prompt, output_file))
                entry = f"Image Gen Test: {prompt} -> {output_file}"
                if entry not in st.session_state['history']:
                    st.session_state['history'].append(entry)
                st.image(result, caption="Generated Image", use_container_width=True)
                st.success(f"Image saved to {output_file}")
                st.session_state['processing']['gen'] = False
    else:
        st.warning("No images or PDFs in gallery yet. Use Camera Snap or Download PDFs!")

update_gallery()