Update app.py

app.py

@@ -4,7 +4,6 @@ import glob
 import base64
 import time
 import shutil
-import streamlit as st
 import pandas as pd
 import torch
 import torch.nn as nn
@@ -28,6 +27,7 @@ import zipfile
 import math
 import random
 import re
+import gradio as gr
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger(__name__)
@@ -39,33 +39,7 @@ class LogCaptureHandler(logging.Handler):
 
 logger.addHandler(LogCaptureHandler())
 
-st.set_page_config(
-    page_title="AI Vision & SFT Titans 🚀",
-    page_icon="🤖",
-    layout="wide",
-    initial_sidebar_state="expanded",
-    menu_items={
-        'Get Help': 'https://huggingface.co/awacke1',
-        'Report a Bug': 'https://huggingface.co/spaces/awacke1',
-        'About': "AI Vision & SFT Titans: PDFs, OCR, Image Gen, Line Drawings, Custom Diffusion, and SFT on CPU! 🌌"
-    }
-)
-
-if 'history' not in st.session_state:
-    st.session_state['history'] = []
-if 'builder' not in st.session_state:
-    st.session_state['builder'] = None
-if 'model_loaded' not in st.session_state:
-    st.session_state['model_loaded'] = False
-if 'processing' not in st.session_state:
-    st.session_state['processing'] = {}
-if 'asset_checkboxes' not in st.session_state:
-    st.session_state['asset_checkboxes'] = {}
-if 'downloaded_pdfs' not in st.session_state:
-    st.session_state['downloaded_pdfs'] = {}
-if 'unique_counter' not in st.session_state:
-    st.session_state['unique_counter'] = 0  # For generating unique keys
-
+# Data Classes and Models (unchanged from your original code)
 @dataclass
 class ModelConfig:
     name: str
@@ -108,23 +82,6 @@ class SFTDataset(Dataset):
             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]
-
 class TinyUNet(nn.Module):
     def __init__(self, in_channels=3, out_channels=3):
         super(TinyUNet, self).__init__()
@@ -139,7 +96,6 @@ class TinyUNet(nn.Module):
     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
@@ -191,11 +147,13 @@ class TinyDiffusion:
         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())
+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]
 
 class ModelBuilder:
     def __init__(self):
@@ -203,17 +161,14 @@ class ModelBuilder:
         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. ☕"]
     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)}")
+        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")
         return self
     def fine_tune_sft(self, csv_path: str, epochs: int = 3, batch_size: int = 4):
         self.sft_data = []
@@ -228,115 +183,54 @@ class ModelBuilder:
         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)}")
+            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()
+            logger.info(f"Epoch {epoch + 1} completed. Average loss: {total_loss / len(dataloader):.4f}")
         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):
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        self.model.save_pretrained(path)
+        self.tokenizer.save_pretrained(path)
+    def evaluate(self, prompt: str):
         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)}"
+        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)
 
 class DiffusionBuilder:
     def __init__(self):
         self.config = None
         self.pipeline = None
     def load_model(self, model_path: str, config: Optional[DiffusionConfig] = None):
-        with st.spinner(f"Loading diffusion model {model_path}... ⏳"):
-            self.pipeline = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float32).to("cpu")
-            if config:
-                self.config = config
-        st.success(f"Diffusion model loaded! 🎨")
-        return self
-    def 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! 🎨")
+        self.pipeline = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float32).to("cpu")
+        if config:
+            self.config = config
         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]
 
+# Utility Functions
 def generate_filename(sequence, ext="png"):
-    timestamp = time.strftime("%d%m%Y%H%M%S")
+    timestamp = time.strftime("%d%m%Y%HM%S")
     return f"{sequence}_{timestamp}.{ext}"
 
 def pdf_url_to_filename(url):
     safe_name = re.sub(r'[<>:"/\\|?*]', '_', url)
     return f"{safe_name}.pdf"
 
-def get_download_link(file_path, mime_type="application/pdf", label="Download"):
-    with open(file_path, 'rb') as f:
-        data = f.read()
-    b64 = base64.b64encode(data).decode()
-    return f'<a href="data:{mime_type};base64,{b64}" download="{os.path.basename(file_path)}">{label}</a>'
-
-def zip_directory(directory_path, zip_path):
-    with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
-        for root, _, files in os.walk(directory_path):
-            for file in files:
-                zipf.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), os.path.dirname(directory_path)))
-
-def get_model_files(model_type="causal_lm"):
-    path = "models/*" if model_type == "causal_lm" else "diffusion_models/*"
-    return [d for d in glob.glob(path) if os.path.isdir(d)]
-
 def get_gallery_files(file_types=["png", "pdf"]):
     return sorted(list(set([f for ext in file_types for f in glob.glob(f"*.{ext}")])))  # Deduplicate files
 
-def get_pdf_files():
-    return sorted(glob.glob("*.pdf"))
-
 def download_pdf(url, output_path):
     try:
         response = requests.get(url, stream=True, timeout=10)
@@ -350,585 +244,107 @@ def download_pdf(url, output_path):
     return False
 
 async def process_pdf_snapshot(pdf_path, mode="single"):
-    start_time = time.time()
-    status = st.empty()
-    status.text(f"Processing PDF Snapshot ({mode})... (0s)")
-    try:
-        doc = fitz.open(pdf_path)
-        output_files = []
-        if mode == "single":
-            page = doc[0]
-            pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
-            output_file = generate_filename("single", "png")
-            pix.save(output_file)
-            output_files.append(output_file)
-        elif mode == "twopage":
-            for i in range(min(2, len(doc))):
-                page = doc[i]
-                pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
-                output_file = generate_filename(f"twopage_{i}", "png")
-                pix.save(output_file)
-                output_files.append(output_file)
-        elif mode == "allpages":
-            for i in range(len(doc)):
-                page = doc[i]
-                pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
-                output_file = generate_filename(f"page_{i}", "png")
-                pix.save(output_file)
-                output_files.append(output_file)
-        doc.close()
-        elapsed = int(time.time() - start_time)
-        status.text(f"PDF Snapshot ({mode}) completed in {elapsed}s!")
-        update_gallery()
-        return output_files
-    except Exception as e:
-        status.error(f"Failed to process PDF: {str(e)}")
-        return []
-
-async def process_ocr(image, output_file):
-    start_time = time.time()
-    status = st.empty()
-    status.text("Processing GOT-OCR2_0... (0s)")
-    tokenizer = AutoTokenizer.from_pretrained("ucaslcl/GOT-OCR2_0", trust_remote_code=True)
-    model = AutoModel.from_pretrained("ucaslcl/GOT-OCR2_0", trust_remote_code=True, torch_dtype=torch.float32).to("cpu").eval()
-    result = model.chat(tokenizer, image, ocr_type='ocr')
-    elapsed = int(time.time() - start_time)
-    status.text(f"GOT-OCR2_0 completed in {elapsed}s!")
-    async with aiofiles.open(output_file, "w") as f:
-        await f.write(result)
-    update_gallery()
-    return result
-
-async def process_image_gen(prompt, output_file):
-    start_time = time.time()
-    status = st.empty()
-    status.text("Processing Image Gen... (0s)")
-    pipeline = StableDiffusionPipeline.from_pretrained("OFA-Sys/small-stable-diffusion-v0", torch_dtype=torch.float32).to("cpu")
-    gen_image = pipeline(prompt, num_inference_steps=20).images[0]
-    elapsed = int(time.time() - start_time)
-    status.text(f"Image Gen completed in {elapsed}s!")
-    gen_image.save(output_file)
-    update_gallery()
-    return gen_image
-
-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
-
-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."
-
-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)},
-        ]
-        return pd.DataFrame(data)
-
-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)
-
-st.title("AI Vision & SFT Titans 🚀")
-
-st.sidebar.header("Captured Files 📜")
-cols = st.sidebar.columns(2)
-with cols[0]:
-    if st.button("Zip All 🤐"):
-        zip_path = f"all_assets_{int(time.time())}.zip"
-        with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
-            for file in get_gallery_files():
-                zipf.write(file, os.path.basename(file))
-        st.sidebar.markdown(get_download_link(zip_path, "application/zip", "Download All Assets"), unsafe_allow_html=True)
-with cols[1]:
-    if st.button("Zap All! 🗑️"):
-        for file in get_gallery_files():
-            os.remove(file)
-        st.session_state['asset_checkboxes'].clear()
-        st.session_state['downloaded_pdfs'].clear()
-        st.sidebar.success("All assets vaporized! 💨")
-        st.rerun()
-
-gallery_size = st.sidebar.slider("Gallery Size", 1, 10, 2)
-def update_gallery():
+    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)
+    doc.close()
+    return output_files
+
+# Gradio Interface Functions
+def update_gallery(history):
     all_files = get_gallery_files()
-    if all_files:
-        st.sidebar.subheader("Asset Gallery 📸📖")
-        cols = st.sidebar.columns(2)
-        for idx, file in enumerate(all_files[:gallery_size * 2]):
-            with cols[idx % 2]:
-                st.session_state['unique_counter'] += 1  # Increment counter for uniqueness
-                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}"  # Unique key with counter
-                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}"):  # Unique key with counter
-                    os.remove(file)
-                    if file in st.session_state['asset_checkboxes']:
-                        del st.session_state['asset_checkboxes'][file]
-                    if file.endswith('.pdf'):
-                        url_key = next((k for k, v in st.session_state['downloaded_pdfs'].items() if v == file), None)
-                        if url_key:
-                            del st.session_state['downloaded_pdfs'][url_key]
-                    st.sidebar.success(f"Asset {os.path.basename(file)} vaporized! 💨")
-                    st.rerun()
-update_gallery()
-
-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 📂"):
+    gallery_content = "\n".join([f"- {f}" for f in all_files[:5]])
+    history.append(f"Gallery updated: {len(all_files)} files")
+    return gallery_content, history
+
+def camera_snap(image, history):
+    if image is not None:
+        filename = generate_filename("cam")
+        image.save(filename)
+        history.append(f"Snapshot saved: {filename}")
+        return f"Image saved as {filename}", history
+    return "No image captured", history
+
+def download_pdfs(urls, history):
+    urls = urls.strip().split("\n")
+    downloaded = []
+    for url in urls:
+        if url:
+            output_path = pdf_url_to_filename(url)
+            if download_pdf(url, output_path):
+                downloaded.append(output_path)
+                history.append(f"Downloaded PDF: {output_path}")
+    return f"Downloaded {len(downloaded)} PDFs", history
+
+def build_model(model_type, base_model, model_name, domain, history):
+    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()
-    config = (ModelConfig if model_type == "Causal LM" else DiffusionConfig)(name=os.path.basename(selected_model), base_model="unknown", size="small")
-    builder.load_model(selected_model, config)
-    st.session_state['builder'] = builder
-    st.session_state['model_loaded'] = True
-    st.rerun()
-
-st.sidebar.subheader("Action Logs 📜")
-log_container = st.sidebar.empty()
-with log_container:
-    for record in log_records:
-        st.write(f"{record.asctime} - {record.levelname} - {record.message}")
-
-st.sidebar.subheader("History 📜")
-history_container = st.sidebar.empty()
-with history_container:
-    for entry in st.session_state['history'][-gallery_size * 2:]:
-        st.write(entry)
-
-tab1, tab2, tab3, tab4, 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 🎨🤓"
-])
-
-with tab1:
-    st.header("Camera Snap 📷")
-    st.subheader("Single Capture")
-    cols = st.columns(2)
-    with cols[0]:
-        cam0_img = st.camera_input("Take a picture - Cam 0", key="cam0")
-        if cam0_img:
-            filename = generate_filename("cam0")
-            with open(filename, "wb") as f:
-                f.write(cam0_img.getvalue())
-            entry = f"Snapshot from Cam 0: {filename}"
-            if entry not in st.session_state['history']:
-                st.session_state['history'] = [e for e in st.session_state['history'] if not e.startswith("Snapshot from Cam 0:")] + [entry]
-            st.image(Image.open(filename), caption="Camera 0", use_container_width=True)
-            logger.info(f"Saved snapshot from Camera 0: {filename}")
-            update_gallery()
-    with cols[1]:
-        cam1_img = st.camera_input("Take a picture - Cam 1", key="cam1")
-        if cam1_img:
-            filename = generate_filename("cam1")
-            with open(filename, "wb") as f:
-                f.write(cam1_img.getvalue())
-            entry = f"Snapshot from Cam 1: {filename}"
-            if entry not in st.session_state['history']:
-                st.session_state['history'] = [e for e in st.session_state['history'] if not e.startswith("Snapshot from Cam 1:")] + [entry]
-            st.image(Image.open(filename), caption="Camera 1", use_container_width=True)
-            logger.info(f"Saved snapshot from Camera 1: {filename}")
-            update_gallery()
-
-with tab2:
-    st.header("Download PDFs 📥")
-    if st.button("Examples 📚"):
-        example_urls = [
-            "https://arxiv.org/pdf/2308.03892",
-            "https://arxiv.org/pdf/1912.01703",
-            "https://arxiv.org/pdf/2408.11039",
-            "https://arxiv.org/pdf/2109.10282",
-            "https://arxiv.org/pdf/2112.10752",
-            "https://arxiv.org/pdf/2308.11236",
-            "https://arxiv.org/pdf/1706.03762",
-            "https://arxiv.org/pdf/2006.11239",
-            "https://arxiv.org/pdf/2305.11207",
-            "https://arxiv.org/pdf/2106.09685",
-            "https://arxiv.org/pdf/2005.11401",
-            "https://arxiv.org/pdf/2106.10504"
-        ]
-        st.session_state['pdf_urls'] = "\n".join(example_urls)
-    
-    url_input = st.text_area("Enter PDF URLs (one per line)", value=st.session_state.get('pdf_urls', ""), height=200)
-    if st.button("Robo-Download 🤖"):
-        urls = url_input.strip().split("\n")
-        progress_bar = st.progress(0)
-        status_text = st.empty()
-        total_urls = len(urls)
-        existing_pdfs = get_pdf_files()
-        for idx, url in enumerate(urls):
-            if url:
-                output_path = pdf_url_to_filename(url)
-                status_text.text(f"Fetching {idx + 1}/{total_urls}: {os.path.basename(output_path)}...")
-                if output_path not in existing_pdfs:
-                    if download_pdf(url, output_path):
-                        st.session_state['downloaded_pdfs'][url] = output_path
-                        logger.info(f"Downloaded PDF from {url} to {output_path}")
-                        entry = f"Downloaded PDF: {output_path}"
-                        if entry not in st.session_state['history']:
-                            st.session_state['history'].append(entry)
-                    else:
-                        st.error(f"Failed to nab {url} 😿")
-                else:
-                    st.info(f"Already got {os.path.basename(output_path)}! Skipping... 🐾")
-                    st.session_state['downloaded_pdfs'][url] = output_path
-                progress_bar.progress((idx + 1) / total_urls)
-        status_text.text("Robo-Download complete! 🚀")
-        update_gallery()
-
-    mode = st.selectbox("Snapshot Mode", ["Single Page (High-Res)", "Two Pages (High-Res)", "All Pages (High-Res)"], key="download_mode")
-    if st.button("Snapshot Selected 📸"):
-        selected_pdfs = [path for path in get_gallery_files() if path.endswith('.pdf') and st.session_state['asset_checkboxes'].get(path, False)]
-        if selected_pdfs:
-            for pdf_path in selected_pdfs:
-                mode_key = {"Single Page (High-Res)": "single", "Two Pages (High-Res)": "twopage", "All Pages (High-Res)": "allpages"}[mode]
-                snapshots = asyncio.run(process_pdf_snapshot(pdf_path, mode_key))
-                for snapshot in snapshots:
-                    st.image(Image.open(snapshot), caption=snapshot, use_container_width=True)
-        else:
-            st.warning("No PDFs selected for snapshotting! Check some boxes in the sidebar gallery.")
-
-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):
-            selected_files = [path for path in get_gallery_files() if st.session_state['asset_checkboxes'].get(path, False)]
-            if len(selected_files) >= 2:
-                demo_data = [{"image": file, "text": f"Asset {os.path.basename(file).split('.')[0]}"} for file in selected_files]
-                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", domain=st.session_state['builder'].config.domain)
-                    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")
-            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:
-    st.header("Test OCR 🔍")
-    all_files = [path for path in get_gallery_files() if st.session_state['asset_checkboxes'].get(path, False)]
-    if all_files:
-        selected_file = st.selectbox("Select Image or PDF", all_files, key="ocr_select")
-        if selected_file:
-            if selected_file.endswith('.png'):
-                image = Image.open(selected_file)
-            else:
-                doc = fitz.open(selected_file)
-                pix = doc[0].get_pixmap(matrix=fitz.Matrix(2.0, 2.0))
-                image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-                doc.close()
-            st.image(image, caption="Input Image", use_container_width=True)
-            if st.button("Run OCR 🚀", key="ocr_run"):
-                output_file = generate_filename("ocr_output", "txt")
-                st.session_state['processing']['ocr'] = True
-                result = asyncio.run(process_ocr(image, output_file))
-                entry = f"OCR Test: {selected_file} -> {output_file}"
-                if entry not in st.session_state['history']:
-                    st.session_state['history'].append(entry)
-                st.text_area("OCR Result", result, height=200, key="ocr_result")
-                st.success(f"OCR output saved to {output_file}")
-                st.session_state['processing']['ocr'] = False
-    else:
-        st.warning("No images or PDFs selected yet. Check some boxes in the sidebar gallery!")
-
-with tab8:
-    st.header("Test Image Gen 🎨")
-    all_files = [path for path in get_gallery_files() if st.session_state['asset_checkboxes'].get(path, False)]
-    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 similar superhero image", key="gen_prompt")
-            if st.button("Run Image Gen 🚀", key="gen_run"):
-                output_file = generate_filename("gen_output", "png")
-                st.session_state['processing']['gen'] = True
-                result = asyncio.run(process_image_gen(prompt, output_file))
-                entry = f"Image Gen Test: {prompt} -> {output_file}"
-                if entry not in st.session_state['history']:
-                    st.session_state['history'].append(entry)
-                st.image(result, caption="Generated Image", use_container_width=True)
-                st.success(f"Image saved to {output_file}")
-                st.session_state['processing']['gen'] = False
-    else:
-        st.warning("No images or PDFs selected yet. Check some boxes in the sidebar gallery!")
-
-with tab9:
-    st.header("Custom Diffusion 🎨🤓")
-    st.write("Unleash your inner artist with our tiny diffusion models!")
-    all_files = [path for path in get_gallery_files() if st.session_state['asset_checkboxes'].get(path, False)]
-    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))
-                doc.close()
+    builder.load_model(base_model, config)
+    builder.save_model(config.model_path)
+    history.append(f"Built {model_type} model: {model_name}")
+    return builder, f"Model saved to {config.model_path}", history
+
+def test_model(builder, prompt, history):
+    if builder is None:
+        return "No model loaded", history
+    if isinstance(builder, ModelBuilder):
+        result = builder.evaluate(prompt)
+        history.append(f"Tested Causal LM: {prompt} -> {result}")
+        return result, history
+    elif isinstance(builder, DiffusionBuilder):
+        image = builder.generate(prompt)
+        output_file = generate_filename("diffusion_test")
+        image.save(output_file)
+        history.append(f"Tested Diffusion: {prompt} -> {output_file}")
+        return output_file, history
+
+# Gradio UI
+with gr.Blocks(title="AI Vision & SFT Titans 🚀") as demo:
+    gr.Markdown("# AI Vision & SFT Titans 🚀")
+    history = gr.State(value=[])
+    builder = gr.State(value=None)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("## Captured Files 📜")
+            gallery_output = gr.Textbox(label="Gallery", lines=5)
+            gr.Button("Update Gallery").click(update_gallery, inputs=[history], outputs=[gallery_output, history])
         
-        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))
-            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
-    else:
-        st.warning("No images or PDFs selected yet. Check some boxes in the sidebar gallery!")
-
-update_gallery()
+        with gr.Column(scale=3):
+            with gr.Tabs():
+                with gr.TabItem("Camera Snap 📷"):
+                    camera_input = gr.Image(type="pil", label="Take a Picture")
+                    snap_output = gr.Textbox(label="Status")
+                    gr.Button("Capture").click(camera_snap, inputs=[camera_input, history], outputs=[snap_output, history])
+
+                with gr.TabItem("Download PDFs 📥"):
+                    url_input = gr.Textbox(label="Enter PDF URLs (one per line)", lines=5)
+                    pdf_output = gr.Textbox(label="Status")
+                    gr.Button("Download").click(download_pdfs, inputs=[url_input, history], outputs=[pdf_output, history])
+
+                with gr.TabItem("Build Titan 🌱"):
+                    model_type = gr.Dropdown(["Causal LM", "Diffusion"], label="Model Type")
+                    base_model = gr.Dropdown(
+                        choices=["HuggingFaceTB/SmolLM-135M", "Qwen/Qwen1.5-0.5B-Chat"] if model_type.value == "Causal LM" else ["OFA-Sys/small-stable-diffusion-v0", "stabilityai/stable-diffusion-2-base"],
+                        label="Base Model"
+                    )
+                    model_name = gr.Textbox(label="Model Name", value=f"tiny-titan-{int(time.time())}")
+                    domain = gr.Textbox(label="Domain", value="general")
+                    build_output = gr.Textbox(label="Status")
+                    gr.Button("Build").click(build_model, inputs=[model_type, base_model, model_name, domain, history], outputs=[builder, build_output, history])
+
+                with gr.TabItem("Test Titan 🧪"):
+                    test_prompt = gr.Textbox(label="Test Prompt", value="What is AI?")
+                    test_output = gr.Textbox(label="Result")
+                    gr.Button("Test").click(test_model, inputs=[builder, test_prompt, history], outputs=[test_output, history])
+
+    with gr.Row():
+        gr.Markdown("## History 📜")
+        history_output = gr.Textbox(value="\n".join(history.value), label="History", lines=5, interactive=False)
+
+demo.launch()