Create app.py

app.py

@@ -0,0 +1,658 @@
+import aiofiles
+import asyncio
+import base64
+import fitz
+import glob
+import logging
+import os
+import pandas as pd
+import pytz
+import random
+import re
+import requests
+import shutil
+import streamlit as st
+import time
+import torch
+import zipfile
+
+from dataclasses import dataclass
+from datetime import datetime
+from diffusers import StableDiffusionPipeline
+from io import BytesIO
+from openai import OpenAI
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
+from typing import Optional
+
+# OpenAI client initialization
+client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'), organization=os.getenv('OPENAI_ORG_ID'))
+
+# Logging setup
+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())
+
+# Streamlit configuration
+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! 🌌"
+    }
+)
+
+# Session state initialization
+st.session_state.setdefault('history', [])
+st.session_state.setdefault('builder', None)
+st.session_state.setdefault('model_loaded', False)
+st.session_state.setdefault('processing', {})
+st.session_state.setdefault('asset_checkboxes', {})
+st.session_state.setdefault('downloaded_pdfs', {})
+st.session_state.setdefault('unique_counter', 0)
+st.session_state.setdefault('selected_model_type', "Causal LM")
+st.session_state.setdefault('selected_model', "None")
+st.session_state.setdefault('cam0_file', None)
+st.session_state.setdefault('cam1_file', None)
+if 'asset_gallery_container' not in st.session_state:
+    st.session_state['asset_gallery_container'] = st.sidebar.empty()
+
+@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. ☕",
+            "I told my neural network a joke; it couldn't stop dropping bits! 🤖",
+            "I asked the AI for a pun, and it said, 'I'm punning on parallel processing!' 😄",
+            "Debugging my code is like a stand-up routine—always a series of exceptions! 😆"
+        ]
+    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("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"):
+    return f"{sequence}_{time.strftime('%d%m%Y%H%M%S')}.{ext}"
+
+def pdf_url_to_filename(url):
+    return re.sub(r'[<>:"/\\|?*]', '_', url) + ".pdf"
+
+def get_download_link(file_path, mime_type="application/pdf", label="Download"):
+    return f'<a href="data:{mime_type};base64,{base64.b64encode(open(file_path, "rb").read()).decode()}" 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:
+        [zipf.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), os.path.dirname(directory_path)))
+         for root, _, files in os.walk(directory_path) for file in files]
+
+def get_model_files(model_type="causal_lm"):
+    return [d for d in glob.glob("models/*" if model_type == "causal_lm" else "diffusion_models/*") if os.path.isdir(d)] or ["None"]
+
+def get_gallery_files(file_types=["png", "pdf"]):
+    return sorted(list({f for ext in file_types for f in glob.glob(f"*.{ext}")}))
+
+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)
+            ret = True
+        else:
+            ret = False
+    except requests.RequestException as e:
+        logger.error(f"Failed to download {url}: {e}")
+        ret = False
+    return ret
+
+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!")
+        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)
+    # Force CPU usage to avoid CUDA error until GPU setup is fixed
+    model = AutoModel.from_pretrained("ucaslcl/GOT-OCR2_0", trust_remote_code=True, torch_dtype=torch.float32).to("cpu").eval()
+    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)
+    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)
+    return result
+
+async def process_image_gen(prompt, output_file):
+    start_time = time.time()
+    status = st.empty()
+    status.text("Processing Image Gen... (0s)")
+    pipeline = (st.session_state['builder'].pipeline 
+                if st.session_state.get('builder') and isinstance(st.session_state['builder'], DiffusionBuilder) 
+                and st.session_state['builder'].pipeline 
+                else 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)
+    return gen_image
+
+def process_image_with_prompt(image, prompt, model="gpt-4o-mini", detail="auto"):
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "text", "text": prompt},
+            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img_str}", "detail": detail}}
+        ]
+    }]
+    try:
+        response = client.chat.completions.create(model=model, messages=messages, max_tokens=300)
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error processing image with GPT: {str(e)}"
+
+def process_text_with_prompt(text, prompt, model="gpt-4o-mini"):
+    messages = [{"role": "user", "content": f"{prompt}\n\n{text}"}]
+    try:
+        response = client.chat.completions.create(model=model, messages=messages, max_tokens=300)
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error processing text with GPT: {str(e)}"
+
+# Sidebar: Gallery Settings
+st.sidebar.subheader("Gallery Settings")
+st.session_state.setdefault('gallery_size', 2)
+st.session_state['gallery_size'] = st.sidebar.slider("Gallery Size", 1, 10, st.session_state['gallery_size'], key="gallery_size_slider")
+
+# Tabs setup
+tabs = st.tabs([
+    "Camera Snap 📷", "Download PDFs 📥", "Test OCR 🔍", "Build Titan 🌱", 
+    "Test Image Gen 🎨", "PDF Process 📄", "Image Process 🖼️", "MD Gallery 📚"
+])
+(tab_camera, tab_download, tab_ocr, tab_build, tab_imggen, tab_pdf_process, tab_image_process, tab_md_gallery) = tabs
+
+with tab_camera:
+    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}"
+            st.session_state['history'].append(entry)
+            st.image(Image.open(filename), caption="Camera 0", use_container_width=True)
+            logger.info(f"Saved snapshot from Camera 0: {filename}")
+    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}"
+            st.session_state['history'].append(entry)
+            st.image(Image.open(filename), caption="Camera 1", use_container_width=True)
+            logger.info(f"Saved snapshot from Camera 1: {filename}")
+
+with tab_download:
+    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}"
+                        st.session_state['history'].append(entry)
+                        st.session_state['asset_checkboxes'][output_path] = True
+                    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! 🚀")
+    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:
+                if not os.path.exists(pdf_path):
+                    st.warning(f"File not found: {pdf_path}. Skipping.")
+                    continue
+                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
+        else:
+            st.warning("No PDFs selected for snapshotting! Check some boxes in the sidebar.")
+
+with tab_ocr:
+    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}"
+                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}"
+                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}"
+                    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 tab_build:
+    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}"
+        st.session_state['history'].append(entry)
+        st.success(f"Model downloaded and saved to {config.model_path}! 🎉")
+        st.experimental_rerun()
+
+with tab_imggen:
+    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}"
+                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!")
+
+with tab_pdf_process:
+    st.header("PDF Process")
+    st.subheader("Upload PDFs for GPT-based text extraction")
+    gpt_models = ["gpt-4o", "gpt-4o-mini"]
+    selected_gpt_model = st.selectbox("Select GPT Model", gpt_models, key="pdf_gpt_model")
+    detail_level = st.selectbox("Detail Level", ["auto", "low", "high"], key="pdf_detail_level")
+    uploaded_pdfs = st.file_uploader("Upload PDF files", type=["pdf"], accept_multiple_files=True, key="pdf_process_uploader")
+    view_mode = st.selectbox("View Mode", ["Single Page", "Double Page"], key="pdf_view_mode")
+    if st.button("Process Uploaded PDFs", key="process_pdfs"):
+        combined_text = ""
+        for pdf_file in uploaded_pdfs:
+            pdf_bytes = pdf_file.read()
+            temp_pdf_path = f"temp_{pdf_file.name}"
+            with open(temp_pdf_path, "wb") as f:
+                f.write(pdf_bytes)
+            try:
+                doc = fitz.open(temp_pdf_path)
+                st.write(f"Processing {pdf_file.name} with {len(doc)} pages")
+                if view_mode == "Single Page":
+                    for i, page in enumerate(doc):
+                        pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
+                        img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
+                        st.image(img, caption=f"{pdf_file.name} Page {i+1}")
+                        gpt_text = process_image_with_prompt(img, "Extract the electronic text from image", model=selected_gpt_model, detail=detail_level)
+                        combined_text += f"\n## {pdf_file.name} - Page {i+1}\n\n{gpt_text}\n"
+                else:
+                    pages = list(doc)
+                    for i in range(0, len(pages), 2):
+                        if i+1 < len(pages):
+                            pix1 = pages[i].get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
+                            img1 = Image.frombytes("RGB", [pix1.width, pix1.height], pix1.samples)
+                            pix2 = pages[i+1].get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
+                            img2 = Image.frombytes("RGB", [pix2.width, pix2.height], pix2.samples)
+                            total_width = img1.width + img2.width
+                            max_height = max(img1.height, img2.height)
+                            combined_img = Image.new("RGB", (total_width, max_height))
+                            combined_img.paste(img1, (0, 0))
+                            combined_img.paste(img2, (img1.width, 0))
+                            st.image(combined_img, caption=f"{pdf_file.name} Pages {i+1}-{i+2}")
+                            gpt_text = process_image_with_prompt(combined_img, "Extract the electronic text from image", model=selected_gpt_model, detail=detail_level)
+                            combined_text += f"\n## {pdf_file.name} - Pages {i+1}-{i+2}\n\n{gpt_text}\n"
+                        else:
+                            pix = pages[i].get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
+                            img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
+                            st.image(img, caption=f"{pdf_file.name} Page {i+1}")
+                            gpt_text = process_image_with_prompt(img, "Extract the electronic text from image", model=selected_gpt_model, detail=detail_level)
+                            combined_text += f"\n## {pdf_file.name} - Page {i+1}\n\n{gpt_text}\n"
+                doc.close()
+            except Exception as e:
+                st.error(f"Error processing {pdf_file.name}: {str(e)}")
+            finally:
+                os.remove(temp_pdf_path)
+        output_filename = generate_filename("processed_pdf", "md")
+        with open(output_filename, "w", encoding="utf-8") as f:
+            f.write(combined_text)
+        st.success(f"PDF processing complete. MD file saved as {output_filename}")
+        st.markdown(get_download_link(output_filename, "text/markdown", "Download Processed PDF MD"), unsafe_allow_html=True)
+
+with tab_image_process:
+    st.header("Image Process")
+    st.subheader("Upload Images for GPT-based OCR")
+    gpt_models = ["gpt-4o", "gpt-4o-mini"]
+    selected_gpt_model = st.selectbox("Select GPT Model", gpt_models, key="img_gpt_model")
+    detail_level = st.selectbox("Detail Level", ["auto", "low", "high"], key="img_detail_level")
+    prompt_img = st.text_input("Enter prompt for image processing", "Extract the electronic text from image", key="img_process_prompt")
+    uploaded_images = st.file_uploader("Upload image files", type=["png", "jpg", "jpeg"], accept_multiple_files=True, key="image_process_uploader")
+    if st.button("Process Uploaded Images", key="process_images"):
+        combined_text = ""
+        for img_file in uploaded_images:
+            try:
+                img = Image.open(img_file)
+                st.image(img, caption=img_file.name)
+                gpt_text = process_image_with_prompt(img, prompt_img, model=selected_gpt_model, detail=detail_level)
+                combined_text += f"\n## {img_file.name}\n\n{gpt_text}\n"
+            except Exception as e:
+                st.error(f"Error processing image {img_file.name}: {str(e)}")
+        output_filename = generate_filename("processed_image", "md")
+        with open(output_filename, "w", encoding="utf-8") as f:
+            f.write(combined_text)
+        st.success(f"Image processing complete. MD file saved as {output_filename}")
+        st.markdown(get_download_link(output_filename, "text/markdown", "Download Processed Image MD"), unsafe_allow_html=True)
+
+with tab_md_gallery:
+    st.header("MD Gallery and GPT Processing")
+    gpt_models = ["gpt-4o", "gpt-4o-mini"]
+    selected_gpt_model = st.selectbox("Select GPT Model", gpt_models, key="md_gpt_model")
+    md_files = sorted(glob.glob("*.md"))
+    if md_files:
+        st.subheader("Individual File Processing")
+        cols = st.columns(2)
+        for idx, md_file in enumerate(md_files):
+            with cols[idx % 2]:
+                st.write(md_file)
+                if st.button(f"Process {md_file}", key=f"process_md_{md_file}"):
+                    try:
+                        with open(md_file, "r", encoding="utf-8") as f:
+                            content = f.read()
+                        prompt_md = "Summarize this into markdown outline with emojis and number the topics 1..12"
+                        result_text = process_text_with_prompt(content, prompt_md, model=selected_gpt_model)
+                        st.markdown(result_text)
+                        output_filename = generate_filename(f"processed_{os.path.splitext(md_file)[0]}", "md")
+                        with open(output_filename, "w", encoding="utf-8") as f:
+                            f.write(result_text)
+                        st.markdown(get_download_link(output_filename, "text/markdown", f"Download {output_filename}"), unsafe_allow_html=True)
+                    except Exception as e:
+                        st.error(f"Error processing {md_file}: {str(e)}")
+        st.subheader("Batch Processing")
+        st.write("Select MD files to combine and process:")
+        selected_md = {}
+        for md_file in md_files:
+            selected_md[md_file] = st.checkbox(md_file, key=f"checkbox_md_{md_file}")
+        batch_prompt = st.text_input("Enter batch processing prompt", "Summarize this into markdown outline with emojis and number the topics 1..12", key="batch_prompt")
+        if st.button("Process Selected MD Files", key="process_batch_md"):
+            combined_content = ""
+            for md_file, selected in selected_md.items():
+                if selected:
+                    try:
+                        with open(md_file, "r", encoding="utf-8") as f:
+                            combined_content += f"\n## {md_file}\n" + f.read() + "\n"
+                    except Exception as e:
+                        st.error(f"Error reading {md_file}: {str(e)}")
+            if combined_content:
+                result_text = process_text_with_prompt(combined_content, batch_prompt, model=selected_gpt_model)
+                st.markdown(result_text)
+                output_filename = generate_filename("batch_processed_md", "md")
+                with open(output_filename, "w", encoding="utf-8") as f:
+                    f.write(result_text)
+                st.success(f"Batch processing complete. MD file saved as {output_filename}")
+                st.markdown(get_download_link(output_filename, "text/markdown", "Download Batch Processed MD"), unsafe_allow_html=True)
+            else:
+                st.warning("No MD files selected.")
+    else:
+        st.warning("No MD files found.")
+
+def update_gallery():
+    container = st.session_state['asset_gallery_container']
+    container.empty()
+    all_files = get_gallery_files()
+    if all_files:
+        container.markdown("### Asset Gallery 📸📖")
+        cols = container.columns(2)
+        for idx, file in enumerate(all_files[:st.session_state['gallery_size']]):
+            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)
+                    st.session_state['asset_checkboxes'].pop(file, None)
+                    st.success(f"Asset {os.path.basename(file)} vaporized! 💨")
+                    st.experimental_rerun()
+
+update_gallery()
+
+st.sidebar.subheader("Action Logs 📜")
+for record in log_records:
+    st.sidebar.write(f"{record.asctime} - {record.levelname} - {record.message}")
+
+st.sidebar.subheader("History 📜")
+for entry in st.session_state.get("history", []):
+    if entry is not None:
+        st.sidebar.write(entry)