Update app.py

app.py

@@ -1,46 +1,46 @@
-#!/usr/bin/env python3
-import os
-import glob
+import aiofiles
+import asyncio
 import base64
-import time
+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 pandas as pd
+import time
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
+import zipfile
+
+from dataclasses import dataclass
+from datetime import datetime
 from diffusers import StableDiffusionPipeline
-from torch.utils.data import Dataset, DataLoader
-import csv
-import fitz  # PyMuPDF
-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
+from openai import OpenAI
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
+from typing import Optional
+
+# 🤖 OpenAI wizardry: Summon your API magic!
+client = OpenAI(
+    api_key=os.getenv('OPENAI_API_KEY'),
+    organization=os.getenv('OPENAI_ORG_ID')
+)
 
-# Logging setup with custom buffer
+# 📜 Logging activated: Capturing chaos and calm!
 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())
 
-# Page Configuration
+# 🎨 Streamlit styling: Designing a cosmic interface!
 st.set_page_config(
     page_title="AI Vision & SFT Titans 🚀",
     page_icon="🤖",
@@ -53,22 +53,24 @@ st.set_page_config(
     }
 )
 
-# Initialize st.session_state
-if 'history' not in st.session_state:
-    st.session_state['history'] = []  # Flat list for 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 'pdf_checkboxes' not in st.session_state:
-    st.session_state['pdf_checkboxes'] = {}  # Shared cache for PDF checkboxes
-if 'downloaded_pdfs' not in st.session_state:
-    st.session_state['downloaded_pdfs'] = {}  # Cache for downloaded PDF paths
-
-# Model Configuration Classes
-@dataclass
+# Set up default session state values.
+st.session_state.setdefault('history', [])             # History: starting fresh if empty!
+st.session_state.setdefault('builder', None)             # Builder: set up if missing.
+st.session_state.setdefault('model_loaded', False)       # Model Loaded: not loaded by default.
+st.session_state.setdefault('processing', {})            # Processing: initialize as an empty dict.
+st.session_state.setdefault('asset_checkboxes', {})      # Asset Checkboxes: default to an empty dict.
+st.session_state.setdefault('downloaded_pdfs', {})       # Downloaded PDFs: start with none.
+st.session_state.setdefault('unique_counter', 0)         # Unique Counter: initialize to zero.
+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)
+
+# Create a single container for the asset gallery in the sidebar.
+if 'asset_gallery_container' not in st.session_state:
+    st.session_state['asset_gallery_container'] = st.sidebar.empty()
+
+@dataclass  # ModelConfig: A blueprint for model configurations.
 class ModelConfig:
     name: str
     base_model: str
@@ -76,138 +78,31 @@ class ModelConfig:
     domain: Optional[str] = None
     model_type: str = "causal_lm"
     @property
-    def model_path(self):
+    def model_path(self): 
         return f"models/{self.name}"
 
-@dataclass
+@dataclass  # DiffusionConfig: Where diffusion magic takes shape.
 class DiffusionConfig:
     name: str
     base_model: str
     size: str
+    domain: Optional[str] = None
     @property
     def model_path(self):
         return f"diffusion_models/{self.name}"
 
-# Datasets
-class SFTDataset(Dataset):
-    def __init__(self, data, tokenizer, max_length=128):
-        self.data = data
-        self.tokenizer = tokenizer
-        self.max_length = max_length
-    def __len__(self):
-        return len(self.data)
-    def __getitem__(self, idx):
-        prompt = self.data[idx]["prompt"]
-        response = self.data[idx]["response"]
-        full_text = f"{prompt} {response}"
-        full_encoding = self.tokenizer(full_text, max_length=self.max_length, padding="max_length", truncation=True, return_tensors="pt")
-        prompt_encoding = self.tokenizer(prompt, max_length=self.max_length, padding=False, truncation=True, return_tensors="pt")
-        input_ids = full_encoding["input_ids"].squeeze()
-        attention_mask = full_encoding["attention_mask"].squeeze()
-        labels = input_ids.clone()
-        prompt_len = prompt_encoding["input_ids"].shape[1]
-        if prompt_len < self.max_length:
-            labels[:prompt_len] = -100
-        return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}
-
-class DiffusionDataset(Dataset):
-    def __init__(self, images, texts):
-        self.images = images
-        self.texts = texts
-    def __len__(self):
-        return len(self.images)
-    def __getitem__(self, idx):
-        return {"image": self.images[idx], "text": self.texts[idx]}
-
-class TinyDiffusionDataset(Dataset):
-    def __init__(self, images):
-        self.images = [torch.tensor(np.array(img.convert("RGB")).transpose(2, 0, 1), dtype=torch.float32) / 255.0 for img in images]
-    def __len__(self):
-        return len(self.images)
-    def __getitem__(self, idx):
-        return self.images[idx]
-
-# Custom 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)))
-        t_embed = t_embed.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=50):
-        dataset = TinyDiffusionDataset(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())
-
-    def upscale(self, image, scale_factor=2):
-        img_tensor = torch.tensor(np.array(image.convert("RGB")).transpose(2, 0, 1), dtype=torch.float32).unsqueeze(0) / 255.0
-        upscaled = F.interpolate(img_tensor, scale_factor=scale_factor, mode='bilinear', align_corners=False)
-        upscaled = torch.clamp(upscaled * 255, 0, 255).byte()
-        return Image.fromarray(upscaled.squeeze(0).permute(1, 2, 0).cpu().numpy())
-
-# Model Builders
 class ModelBuilder:
     def __init__(self):
         self.config = None
         self.model = None
         self.tokenizer = None
-        self.sft_data = None
-        self.jokes = ["Why did the AI go to therapy? Too many layers to unpack! 😂", "Training complete! Time for a binary coffee break. ☕"]
+        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)
@@ -219,53 +114,12 @@ class ModelBuilder:
             self.model.to("cuda" if torch.cuda.is_available() else "cpu")
         st.success(f"Model loaded! 🎉 {random.choice(self.jokes)}")
         return self
-    def fine_tune_sft(self, csv_path: str, epochs: int = 3, batch_size: int = 4):
-        self.sft_data = []
-        with open(csv_path, "r") as f:
-            reader = csv.DictReader(f)
-            for row in reader:
-                self.sft_data.append({"prompt": row["prompt"], "response": row["response"]})
-        dataset = SFTDataset(self.sft_data, self.tokenizer)
-        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
-        optimizer = torch.optim.AdamW(self.model.parameters(), lr=2e-5)
-        self.model.train()
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.model.to(device)
-        for epoch in range(epochs):
-            with st.spinner(f"Training epoch {epoch + 1}/{epochs}... ⚙️"):
-                total_loss = 0
-                for batch in dataloader:
-                    optimizer.zero_grad()
-                    input_ids = batch["input_ids"].to(device)
-                    attention_mask = batch["attention_mask"].to(device)
-                    labels = batch["labels"].to(device)
-                    outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
-                    loss = outputs.loss
-                    loss.backward()
-                    optimizer.step()
-                    total_loss += loss.item()
-                st.write(f"Epoch {epoch + 1} completed. Average loss: {total_loss / len(dataloader):.4f}")
-        st.success(f"SFT Fine-tuning completed! 🎉 {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}! ✅")
-    def evaluate(self, prompt: str, status_container=None):
-        self.model.eval()
-        if status_container:
-            status_container.write("Preparing to evaluate... 🧠")
-        try:
-            with torch.no_grad():
-                inputs = self.tokenizer(prompt, return_tensors="pt", max_length=128, truncation=True).to(self.model.device)
-                outputs = self.model.generate(**inputs, max_new_tokens=50, do_sample=True, top_p=0.95, temperature=0.7)
-                return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        except Exception as e:
-            if status_container:
-                status_container.error(f"Oops! Something broke: {str(e)} 💥")
-            return f"Error: {str(e)}"
 
 class DiffusionBuilder:
     def __init__(self):
@@ -276,32 +130,7 @@ class DiffusionBuilder:
             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 fine_tune_sft(self, images, texts, epochs=3):
-        dataset = DiffusionDataset(images, texts)
-        dataloader = DataLoader(dataset, batch_size=1, shuffle=True)
-        optimizer = torch.optim.AdamW(self.pipeline.unet.parameters(), lr=1e-5)
-        self.pipeline.unet.train()
-        for epoch in range(epochs):
-            with st.spinner(f"Training diffusion epoch {epoch + 1}/{epochs}... ⚙️"):
-                total_loss = 0
-                for batch in dataloader:
-                    optimizer.zero_grad()
-                    image = batch["image"][0].to(self.pipeline.device)
-                    text = batch["text"][0]
-                    latents = self.pipeline.vae.encode(torch.tensor(np.array(image)).permute(2, 0, 1).unsqueeze(0).float().to(self.pipeline.device)).latent_dist.sample()
-                    noise = torch.randn_like(latents)
-                    timesteps = torch.randint(0, self.pipeline.scheduler.num_train_timesteps, (latents.shape[0],), device=latents.device)
-                    noisy_latents = self.pipeline.scheduler.add_noise(latents, noise, timesteps)
-                    text_embeddings = self.pipeline.text_encoder(self.pipeline.tokenizer(text, return_tensors="pt").input_ids.to(self.pipeline.device))[0]
-                    pred_noise = self.pipeline.unet(noisy_latents, timesteps, encoder_hidden_states=text_embeddings).sample
-                    loss = torch.nn.functional.mse_loss(pred_noise, noise)
-                    loss.backward()
-                    optimizer.step()
-                    total_loss += loss.item()
-                st.write(f"Epoch {epoch + 1} completed. Average loss: {total_loss / len(dataloader):.4f}")
-        st.success("Diffusion SFT Fine-tuning completed! 🎨")
+        st.success("Diffusion model loaded! 🎨")
         return self
     def save_model(self, path: str):
         with st.spinner("Saving diffusion model... 💾"):
@@ -311,34 +140,25 @@ class DiffusionBuilder:
     def generate(self, prompt: str):
         return self.pipeline(prompt, num_inference_steps=20).images[0]
 
-# Utility Functions
 def generate_filename(sequence, ext="png"):
-    timestamp = time.strftime("%d%m%Y%H%M%S")
-    return f"{sequence}_{timestamp}.{ext}"
+    return f"{sequence}_{time.strftime('%d%m%Y%H%M%S')}.{ext}"
 
 def pdf_url_to_filename(url):
-    # Convert full URL to filename, replacing illegal characters
-    safe_name = re.sub(r'[<>:"/\\|?*]', '_', url)
-    return f"{safe_name}.pdf"
+    return re.sub(r'[<>:"/\\|?*]', '_', url) + ".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>'
+    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:
-        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)))
+        [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"):
-    path = "models/*" if model_type == "causal_lm" else "diffusion_models/*"
-    return [d for d in glob.glob(path) if os.path.isdir(d)]
+    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"]):
-    return sorted([f for ext in file_types for f in glob.glob(f"*.{ext}")])
+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"))
@@ -350,12 +170,15 @@ def download_pdf(url, output_path):
             with open(output_path, "wb") as f:
                 for chunk in response.iter_content(chunk_size=8192):
                     f.write(chunk)
-            return True
+            ret = True
+        else:
+            ret = False
     except requests.RequestException as e:
         logger.error(f"Failed to download {url}: {e}")
-    return False
+        ret = False
+    return ret
 
-# Async Processing Functions
+# Async PDF Snapshot: Snap your PDF pages without blocking.
 async def process_pdf_snapshot(pdf_path, mode="single"):
     start_time = time.time()
     status = st.empty()
@@ -365,206 +188,107 @@ async def process_pdf_snapshot(pdf_path, mode="single"):
         output_files = []
         if mode == "single":
             page = doc[0]
-            pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0))  # High-res: 200% scale
+            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))  # High-res: 200% scale
+                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 == "allthumbs":
+        elif mode == "allpages":
             for i in range(len(doc)):
                 page = doc[i]
-                pix = page.get_pixmap(matrix=fitz.Matrix(0.5, 0.5))  # Thumbnail: 50% scale
-                output_file = generate_filename(f"thumb_{i}", "png")
+                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 OCR: Convert images to text.
 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()
-    result = model.chat(tokenizer, image, ocr_type='ocr')
+    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)
-    update_gallery()
     return result
 
+# Async Image Gen: Your image genie.
 async def process_image_gen(prompt, output_file):
     start_time = time.time()
     status = st.empty()
     status.text("Processing Image Gen... (0s)")
-    pipeline = StableDiffusionPipeline.from_pretrained("OFA-Sys/small-stable-diffusion-v0", torch_dtype=torch.float32).to("cpu")
+    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)
-    update_gallery()
     return gen_image
 
-async def process_custom_diffusion(images, output_file, model_name):
-    start_time = time.time()
-    status = st.empty()
-    status.text(f"Training {model_name}... (0s)")
-    unet = TinyUNet()
-    diffusion = TinyDiffusion(unet)
-    diffusion.train(images)
-    gen_image = diffusion.generate()
-    upscaled_image = diffusion.upscale(gen_image, scale_factor=2)
-    elapsed = int(time.time() - start_time)
-    status.text(f"{model_name} completed in {elapsed}s!")
-    upscaled_image.save(output_file)
-    update_gallery()
-    return upscaled_image
-
-# Mock Search Tool for RAG
-def mock_search(query: str) -> str:
-    if "superhero" in query.lower():
-        return "Latest trends: Gold-plated Batman statues, VR superhero battles."
-    return "No relevant results found."
-
-def mock_duckduckgo_search(query: str) -> str:
-    if "superhero party trends" in query.lower():
-        return """
-        Latest trends for 2025: 
-        - Luxury decorations: Gold-plated Batman statues, holographic Avengers displays.
-        - Entertainment: Live stunt shows with Iron Man suits, VR superhero battles.
-        - Catering: Gourmet kryptonite-green cocktails, Thor’s hammer-shaped appetizers.
-        """
-    return "No relevant results found."
-
-# Agent Classes
-class PartyPlannerAgent:
-    def __init__(self, model, tokenizer):
-        self.model = model
-        self.tokenizer = tokenizer
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.model.to(self.device)
-    def generate(self, prompt: str) -> str:
-        self.model.eval()
-        with torch.no_grad():
-            inputs = self.tokenizer(prompt, return_tensors="pt", max_length=128, truncation=True).to(self.device)
-            outputs = self.model.generate(**inputs, max_new_tokens=100, do_sample=True, top_p=0.95, temperature=0.7)
-            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-    def plan_party(self, task: str) -> pd.DataFrame:
-        search_result = mock_duckduckgo_search("latest superhero party trends")
-        prompt = f"Given this context: '{search_result}'\n{task}"
-        plan_text = self.generate(prompt)
-        locations = {
-            "Wayne Manor": (42.3601, -71.0589),
-            "New York": (40.7128, -74.0060),
-            "Los Angeles": (34.0522, -118.2437),
-            "London": (51.5074, -0.1278)
-        }
-        wayne_coords = locations["Wayne Manor"]
-        travel_times = {loc: calculate_cargo_travel_time(coords, wayne_coords) for loc, coords in locations.items() if loc != "Wayne Manor"}
-        catchphrases = ["To the Batmobile!", "Avengers, assemble!", "I am Iron Man!", "By the power of Grayskull!"]
-        data = [
-            {"Location": "New York", "Travel Time (hrs)": travel_times["New York"], "Luxury Idea": "Gold-plated Batman statues", "Catchphrase": random.choice(catchphrases)},
-            {"Location": "Los Angeles", "Travel Time (hrs)": travel_times["Los Angeles"], "Luxury Idea": "Holographic Avengers displays", "Catchphrase": random.choice(catchphrases)},
-            {"Location": "London", "Travel Time (hrs)": travel_times["London"], "Luxury Idea": "Live stunt shows with Iron Man suits", "Catchphrase": random.choice(catchphrases)},
-            {"Location": "Wayne Manor", "Travel Time (hrs)": 0.0, "Luxury Idea": "VR superhero battles", "Catchphrase": random.choice(catchphrases)},
-            {"Location": "New York", "Travel Time (hrs)": travel_times["New York"], "Luxury Idea": "Gourmet kryptonite-green cocktails", "Catchphrase": random.choice(catchphrases)},
-            {"Location": "Los Angeles", "Travel Time (hrs)": travel_times["Los Angeles"], "Luxury Idea": "Thor’s hammer-shaped appetizers", "Catchphrase": random.choice(catchphrases)},
+# GPT-Image Interpreter: Turning pixels into prose!
+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}}
         ]
-        return pd.DataFrame(data)
+    }]
+    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)}"
 
-class CVPartyPlannerAgent:
-    def __init__(self, pipeline):
-        self.pipeline = pipeline
-    def generate(self, prompt: str) -> Image.Image:
-        return self.pipeline(prompt, num_inference_steps=20).images[0]
-    def plan_party(self, task: str) -> pd.DataFrame:
-        search_result = mock_search("superhero party trends")
-        prompt = f"Given this context: '{search_result}'\n{task}"
-        data = [
-            {"Theme": "Batman", "Image Idea": "Gold-plated Batman statue"},
-            {"Theme": "Avengers", "Image Idea": "VR superhero battle scene"}
-        ]
-        return pd.DataFrame(data)
-
-def calculate_cargo_travel_time(origin_coords: Tuple[float, float], destination_coords: Tuple[float, float], cruising_speed_kmh: float = 750.0) -> float:
-    def to_radians(degrees: float) -> float:
-        return degrees * (math.pi / 180)
-    lat1, lon1 = map(to_radians, origin_coords)
-    lat2, lon2 = map(to_radians, destination_coords)
-    EARTH_RADIUS_KM = 6371.0
-    dlon = lon2 - lon1
-    dlat = lat2 - lat1
-    a = (math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2)
-    c = 2 * math.asin(math.sqrt(a))
-    distance = EARTH_RADIUS_KM * c
-    actual_distance = distance * 1.1
-    flight_time = (actual_distance / cruising_speed_kmh) + 1.0
-    return round(flight_time, 2)
-
-# Main App
-st.title("AI Vision & SFT Titans 🚀")
-
-# Sidebar
-st.sidebar.header("Captured Files 📜")
-gallery_size = st.sidebar.slider("Gallery Size", 1, 10, 2)  # Default to 2
-def update_gallery():
-    media_files = get_gallery_files(["png"])
-    pdf_files = get_pdf_files()
-    if media_files or pdf_files:
-        st.sidebar.subheader("Images 📸")
-        cols = st.sidebar.columns(2)
-        for idx, file in enumerate(media_files[:gallery_size * 2]):  # Limit by gallery size
-            with cols[idx % 2]:
-                st.image(Image.open(file), caption=os.path.basename(file), use_container_width=True)
-        st.sidebar.subheader("PDF Downloads 📖")
-        for pdf_file in pdf_files[:gallery_size * 2]:  # Limit by gallery size
-            st.markdown(get_download_link(pdf_file, "application/pdf", f"📥 Grab {os.path.basename(pdf_file)}"), unsafe_allow_html=True)
-update_gallery()
+# GPT-Text Alchemist: Merging prompt and text.
+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)}"
 
-st.sidebar.subheader("Model Management 🗂️")
-model_type = st.sidebar.selectbox("Model Type", ["Causal LM", "Diffusion"], key="sidebar_model_type")
-model_dirs = get_model_files(model_type)
-selected_model = st.sidebar.selectbox("Select Saved Model", ["None"] + model_dirs, key="sidebar_model_select")
-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()
+# ----------------- SIDEBAR UPDATES -----------------
 
-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}")
+# 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")
 
-st.sidebar.subheader("History 📜")
-history_container = st.sidebar.empty()
-with history_container:
-    for entry in st.session_state['history'][-gallery_size * 2:]:  # Limit by gallery size
-        st.write(entry)
-
-# Tabs
-tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9 = st.tabs([
-    "Camera Snap 📷", "Download PDFs 📥", "Build Titan 🌱", "Fine-Tune Titan 🔧", 
-    "Test Titan 🧪", "Agentic RAG Party 🌐", "Test OCR 🔍", "Test Image Gen 🎨", "Custom Diffusion 🎨🤓"
+# ----------------- TAB 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 tab1:
+# ----------------- TAB: Camera Snap -----------------
+with tab_camera:
     st.header("Camera Snap 📷")
     st.subheader("Single Capture")
     cols = st.columns(2)
@@ -572,48 +296,48 @@ with tab1:
         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.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}")
-            update_gallery()
     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.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}")
-            update_gallery()
 
-with tab2:
+# ----------------- TAB: Download PDFs -----------------
+with tab_download:
     st.header("Download PDFs 📥")
-    # Examples button with arXiv PDF links from README.md
     if st.button("Examples 📚"):
         example_urls = [
-            "https://arxiv.org/pdf/2308.03892",  # Streamlit
-            "https://arxiv.org/pdf/1912.01703",  # PyTorch
-            "https://arxiv.org/pdf/2408.11039",  # Qwen2-VL
-            "https://arxiv.org/pdf/2109.10282",  # TrOCR
-            "https://arxiv.org/pdf/2112.10752",  # LDM
-            "https://arxiv.org/pdf/2308.11236",  # OpenCV
-            "https://arxiv.org/pdf/1706.03762",  # Attention is All You Need
-            "https://arxiv.org/pdf/2006.11239",  # DDPM
-            "https://arxiv.org/pdf/2305.11207",  # Pandas
-            "https://arxiv.org/pdf/2106.09685",  # LoRA
-            "https://arxiv.org/pdf/2005.11401",  # RAG
-            "https://arxiv.org/pdf/2106.10504"   # Fine-Tuning Vision Transformers
+            "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)
-    
-    # Robo-Downloader
     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")
@@ -630,8 +354,8 @@ with tab2:
                         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['history'].append(entry)
+                        st.session_state['asset_checkboxes'][output_path] = True
                     else:
                         st.error(f"Failed to nab {url} 😿")
                 else:
@@ -639,218 +363,50 @@ with tab2:
                     st.session_state['downloaded_pdfs'][url] = output_path
                 progress_bar.progress((idx + 1) / total_urls)
         status_text.text("Robo-Download complete! 🚀")
-        update_gallery()
-
-    # PDF Gallery with Thumbnails and Checkboxes
-    st.subheader("PDF Gallery 📖")
-    downloaded_pdfs = list(st.session_state['downloaded_pdfs'].values())
-    if downloaded_pdfs:
-        cols_per_row = 3
-        for i in range(0, len(downloaded_pdfs), cols_per_row):
-            cols = st.columns(cols_per_row)
-            for j, pdf_path in enumerate(downloaded_pdfs[i:i + cols_per_row]):
-                with cols[j]:
-                    doc = fitz.open(pdf_path)
-                    page = doc[0]
-                    pix = page.get_pixmap(matrix=fitz.Matrix(0.5, 0.5))  # Thumbnail at 50% scale
-                    img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-                    st.image(img, caption=os.path.basename(pdf_path), use_container_width=True)
-                    # Checkbox for SFT/Input use
-                    checkbox_key = f"pdf_{pdf_path}"
-                    st.session_state['pdf_checkboxes'][checkbox_key] = st.checkbox(
-                        "Use for SFT/Input", 
-                        value=st.session_state['pdf_checkboxes'].get(checkbox_key, False), 
-                        key=checkbox_key
-                    )
-                    # Download and Delete Buttons
-                    st.markdown(get_download_link(pdf_path, "application/pdf", "Snag It! 📥"), unsafe_allow_html=True)
-                    if st.button("Zap It! 🗑️", key=f"delete_{pdf_path}"):
-                        os.remove(pdf_path)
-                        url_key = next((k for k, v in st.session_state['downloaded_pdfs'].items() if v == pdf_path), None)
-                        if url_key:
-                            del st.session_state['downloaded_pdfs'][url_key]
-                        del st.session_state['pdf_checkboxes'][checkbox_key]
-                        st.success(f"PDF {os.path.basename(pdf_path)} vaporized! 💨")
-                        st.rerun()
-                    doc.close()
-    else:
-        st.info("No PDFs captured yet. Feed the robo-downloader some URLs! 🤖")
-
-    mode = st.selectbox("Snapshot Mode", ["Single Page (High-Res)", "Two Pages (High-Res)", "All Pages (Thumbnails)"], key="download_mode")
+    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 key, path in st.session_state['downloaded_pdfs'].items() if st.session_state['pdf_checkboxes'].get(f"pdf_{path}", False)]
+        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 (Thumbnails)": "allthumbs"}[mode]
+                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
+            # No update_gallery() call here; will update once later.
         else:
-            st.warning("No PDFs selected for snapshotting! Check some boxes first. 📝")
+            st.warning("No PDFs selected for snapshotting! Check some boxes in the sidebar.")
 
-with tab3:
-    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
-        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()
-
-with tab4:
-    st.header("Fine-Tune Titan 🔧")
-    if 'builder' not in st.session_state or not st.session_state.get('model_loaded', False):
-        st.warning("Please build or load a Titan first! ⚠️")
-    else:
-        if isinstance(st.session_state['builder'], ModelBuilder):
-            if st.button("Generate Sample CSV 📝"):
-                sample_data = [
-                    {"prompt": "What is AI?", "response": "AI is artificial intelligence, simulating human smarts in machines."},
-                    {"prompt": "Explain machine learning", "response": "Machine learning is AI’s gym where models bulk up on data."},
-                ]
-                csv_path = f"sft_data_{int(time.time())}.csv"
-                with open(csv_path, "w", newline="") as f:
-                    writer = csv.DictWriter(f, fieldnames=["prompt", "response"])
-                    writer.writeheader()
-                    writer.writerows(sample_data)
-                st.markdown(get_download_link(csv_path, "text/csv", "Download Sample CSV"), unsafe_allow_html=True)
-                st.success(f"Sample CSV generated as {csv_path}! ✅")
-
-            uploaded_csv = st.file_uploader("Upload CSV for SFT", type="csv")
-            if uploaded_csv and st.button("Fine-Tune with Uploaded CSV 🔄"):
-                csv_path = f"uploaded_sft_data_{int(time.time())}.csv"
-                with open(csv_path, "wb") as f:
-                    f.write(uploaded_csv.read())
-                new_model_name = f"{st.session_state['builder'].config.name}-sft-{int(time.time())}"
-                new_config = ModelConfig(name=new_model_name, base_model=st.session_state['builder'].config.base_model, size="small", domain=st.session_state['builder'].config.domain)
-                st.session_state['builder'].config = new_config
-                st.session_state['builder'].fine_tune_sft(csv_path)
-                st.session_state['builder'].save_model(new_config.model_path)
-                zip_path = f"{new_config.model_path}.zip"
-                zip_directory(new_config.model_path, zip_path)
-                entry = f"Fine-tuned Causal LM: {new_model_name}"
-                if entry not in st.session_state['history']:
-                    st.session_state['history'].append(entry)
-                st.markdown(get_download_link(zip_path, "application/zip", "Download Fine-Tuned Titan"), unsafe_allow_html=True)
-                st.rerun()
-        elif isinstance(st.session_state['builder'], DiffusionBuilder):
-            captured_files = get_gallery_files(["png"])
-            selected_pdfs = [path for key, path in st.session_state['downloaded_pdfs'].items() if st.session_state['pdf_checkboxes'].get(f"pdf_{path}", False)]
-            if len(captured_files) + len(selected_pdfs) >= 2:
-                demo_data = [{"image": img, "text": f"Superhero {os.path.basename(img).split('.')[0]}"} for img in captured_files]
-                for pdf_path in selected_pdfs:
-                    demo_data.append({"image": pdf_path, "text": f"PDF {os.path.basename(pdf_path)}"})
-                edited_data = st.data_editor(pd.DataFrame(demo_data), num_rows="dynamic")
-                if st.button("Fine-Tune with Dataset 🔄"):
-                    images = [Image.open(row["image"]) if row["image"].endswith('.png') else Image.frombytes("RGB", fitz.open(row["image"])[0].get_pixmap(matrix=fitz.Matrix(2.0, 2.0)).size, fitz.open(row["image"])[0].get_pixmap(matrix=fitz.Matrix(2.0, 2.0)).samples) for _, row in edited_data.iterrows()]
-                    texts = [row["text"] for _, row in edited_data.iterrows()]
-                    new_model_name = f"{st.session_state['builder'].config.name}-sft-{int(time.time())}"
-                    new_config = DiffusionConfig(name=new_model_name, base_model=st.session_state['builder'].config.base_model, size="small")
-                    st.session_state['builder'].config = new_config
-                    st.session_state['builder'].fine_tune_sft(images, texts)
-                    st.session_state['builder'].save_model(new_config.model_path)
-                    zip_path = f"{new_config.model_path}.zip"
-                    zip_directory(new_config.model_path, zip_path)
-                    entry = f"Fine-tuned Diffusion: {new_model_name}"
-                    if entry not in st.session_state['history']:
-                        st.session_state['history'].append(entry)
-                    st.markdown(get_download_link(zip_path, "application/zip", "Download Fine-Tuned Diffusion Model"), unsafe_allow_html=True)
-                csv_path = f"sft_dataset_{int(time.time())}.csv"
-                with open(csv_path, "w", newline="") as f:
-                    writer = csv.writer(f)
-                    writer.writerow(["image", "text"])
-                    for _, row in edited_data.iterrows():
-                        writer.writerow([row["image"], row["text"]])
-                st.markdown(get_download_link(csv_path, "text/csv", "Download SFT Dataset CSV"), unsafe_allow_html=True)
-
-with tab5:
-    st.header("Test Titan 🧪")
-    if 'builder' not in st.session_state or not st.session_state.get('model_loaded', False):
-        st.warning("Please build or load a Titan first! ⚠️")
-    else:
-        if isinstance(st.session_state['builder'], ModelBuilder):
-            if st.session_state['builder'].sft_data:
-                st.write("Testing with SFT Data:")
-                for item in st.session_state['builder'].sft_data[:3]:
-                    prompt = item["prompt"]
-                    expected = item["response"]
-                    status_container = st.empty()
-                    generated = st.session_state['builder'].evaluate(prompt, status_container)
-                    st.write(f"**Prompt**: {prompt}")
-                    st.write(f"**Expected**: {expected}")
-                    st.write(f"**Generated**: {generated}")
-                    st.write("---")
-                    status_container.empty()
-            test_prompt = st.text_area("Enter Test Prompt", "What is AI?")
-            if st.button("Run Test ▶️"):
-                status_container = st.empty()
-                result = st.session_state['builder'].evaluate(test_prompt, status_container)
-                entry = f"Causal LM Test: {test_prompt} -> {result}"
-                if entry not in st.session_state['history']:
-                    st.session_state['history'].append(entry)
-                st.write(f"**Generated Response**: {result}")
-                status_container.empty()
-        elif isinstance(st.session_state['builder'], DiffusionBuilder):
-            test_prompt = st.text_area("Enter Test Prompt", "Neon Batman")
-            selected_pdfs = [path for key, path in st.session_state['downloaded_pdfs'].items() if st.session_state['pdf_checkboxes'].get(f"pdf_{path}", False)]
-            if st.button("Run Test ▶️"):
-                image = st.session_state['builder'].generate(test_prompt)
-                output_file = generate_filename("diffusion_test", "png")
-                image.save(output_file)
-                entry = f"Diffusion Test: {test_prompt} -> {output_file}"
-                if entry not in st.session_state['history']:
-                    st.session_state['history'].append(entry)
-                st.image(image, caption="Generated Image")
-                update_gallery()
-
-with tab6:
-    st.header("Agentic RAG Party 🌐")
-    if 'builder' not in st.session_state or not st.session_state.get('model_loaded', False):
-        st.warning("Please build or load a Titan first! ⚠️")
-    else:
-        if isinstance(st.session_state['builder'], ModelBuilder):
-            if st.button("Run NLP RAG Demo 🎉"):
-                agent = PartyPlannerAgent(st.session_state['builder'].model, st.session_state['builder'].tokenizer)
-                task = "Plan a luxury superhero-themed party at Wayne Manor."
-                plan_df = agent.plan_party(task)
-                entry = f"NLP RAG Demo: Planned party at Wayne Manor"
-                if entry not in st.session_state['history']:
-                    st.session_state['history'].append(entry)
-                st.dataframe(plan_df)
-        elif isinstance(st.session_state['builder'], DiffusionBuilder):
-            if st.button("Run CV RAG Demo 🎉"):
-                agent = CVPartyPlannerAgent(st.session_state['builder'].pipeline)
-                task = "Generate images for a luxury superhero-themed party."
-                plan_df = agent.plan_party(task)
-                entry = f"CV RAG Demo: Generated party images"
-                if entry not in st.session_state['history']:
-                    st.session_state['history'].append(entry)
-                st.dataframe(plan_df)
-                for _, row in plan_df.iterrows():
-                    image = agent.generate(row["Image Idea"])
-                    output_file = generate_filename(f"cv_rag_{row['Theme'].lower()}", "png")
-                    image.save(output_file)
-                    st.image(image, caption=f"{row['Theme']} - {row['Image Idea']}")
-                update_gallery()
-
-with tab7:
+# ----------------- TAB: Test OCR -----------------
+with tab_ocr:
     st.header("Test OCR 🔍")
-    captured_files = get_gallery_files(["png"])
-    selected_pdfs = [path for key, path in st.session_state['downloaded_pdfs'].items() if st.session_state['pdf_checkboxes'].get(f"pdf_{path}", False)]
-    all_files = captured_files + selected_pdfs
+    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'):
@@ -866,19 +422,60 @@ with tab7:
                 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.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 images or PDFs captured yet. Use Camera Snap or Download PDFs first!")
+        st.warning("No assets in gallery yet. Use Camera Snap or Download PDFs!")
 
-with tab8:
+# ----------------- TAB: Build Titan -----------------
+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()
+
+# ----------------- TAB: Test Image Gen -----------------
+with tab_imggen:
     st.header("Test Image Gen 🎨")
-    captured_files = get_gallery_files(["png"])
-    selected_pdfs = [path for key, path in st.session_state['downloaded_pdfs'].items() if st.session_state['pdf_checkboxes'].get(f"pdf_{path}", False)]
-    all_files = captured_files + selected_pdfs
+    all_files = get_gallery_files()
     if all_files:
         selected_file = st.selectbox("Select Image or PDF", all_files, key="gen_select")
         if selected_file:
@@ -890,65 +487,196 @@ with tab8:
                 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 similar superhero image", key="gen_prompt")
+            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.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 captured yet. Use Camera Snap or Download PDFs first WAV!")
-
-with tab9:
-    st.header("Custom Diffusion 🎨🤓")
-    st.write("Unleash your inner artist with our tiny diffusion models!")
-    captured_files = get_gallery_files(["png"])
-    selected_pdfs = [path for key, path in st.session_state['downloaded_pdfs'].items() if st.session_state['pdf_checkboxes'].get(f"pdf_{path}", False)]
-    all_files = captured_files + selected_pdfs
-    if all_files:
-        st.subheader("Select Images or PDFs to Train")
-        selected_files = st.multiselect("Pick Images or PDFs", all_files, key="diffusion_select")
-        images = []
-        for file in selected_files:
-            if file.endswith('.png'):
-                images.append(Image.open(file))
-            else:
-                doc = fitz.open(file)
-                pix = doc[0].get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
-                images.append(Image.frombytes("RGB", [pix.width, pix.height], pix.samples))
+        st.warning("No images or PDFs in gallery yet. Use Camera Snap or Download PDFs!")
+
+# ----------------- TAB: PDF Process -----------------
+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()
-        
-        model_options = [
-            ("PixelTickler 🎨✨", "OFA-Sys/small-stable-diffusion-v0"),
-            ("DreamWeaver 🌙🖌️", "stabilityai/stable-diffusion-2-base"),
-            ("TinyArtBot 🤖🖼️", "custom")
-        ]
-        model_choice = st.selectbox("Choose Your Diffusion Dynamo", [opt[0] for opt in model_options], key="diffusion_model")
-        model_name = next(opt[1] for opt in model_options if opt[0] == model_choice)
-
-        if st.button("Train & Generate 🚀", key="diffusion_run"):
-            output_file = generate_filename("custom_diffusion", "png")
-            st.session_state['processing']['diffusion'] = True
-            if model_name == "custom":
-                result = asyncio.run(process_custom_diffusion(images, output_file, model_choice))
+            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)
+
+# ----------------- TAB: Image Process -----------------
+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)
+
+# ----------------- TAB: MD Gallery -----------------
+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:
-                builder = DiffusionBuilder()
-                builder.load_model(model_name)
-                result = builder.generate("A superhero scene inspired by captured images")
-                result.save(output_file)
-            entry = f"Custom Diffusion: {model_choice} -> {output_file}"
-            if entry not in st.session_state['history']:
-                st.session_state['history'].append(entry)
-            st.image(result, caption=f"{model_choice} Masterpiece", use_container_width=True)
-            st.success(f"Image saved to {output_file}")
-            st.session_state['processing']['diffusion'] = False
+                st.warning("No MD files selected.")
     else:
-        st.warning("No images or PDFs captured yet. Use Camera Snap or Download PDFs first!")
+        st.warning("No MD files found.")
 
-# Initial Gallery Update
-update_gallery()
+# ----------------- FINAL SIDEBAR UPDATE -----------------
+# Update the asset gallery once (using its container).
+def update_gallery():
+    container = st.session_state['asset_gallery_container']
+    container.empty()  # Clear previous gallery content.
+    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()
+
+# Call the gallery update once after all tabs have been processed.
+update_gallery()
+
+# Finally, update the Action Logs and History in the sidebar.
+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)