Update app.py

app.py

@@ -18,6 +18,7 @@ import random
 import logging
 import numpy as np
 import cv2
+from diffusers import DiffusionPipeline  # For FLUX.1 and LDM
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger(__name__)
@@ -37,36 +38,23 @@ st.set_page_config(
     menu_items={
         'Get Help': 'https://huggingface.co/awacke1',
         'Report a Bug': 'https://huggingface.co/spaces/awacke1',
-        'About': "Tiny Titans: Small models, big dreams, and a sprinkle of chaos! 🌌"
+        'About': "Tiny Titans: Small diffusion models, big CV dreams! 🌌"
     }
 )
 
+# Session State Setup 🌍 - Persistent playground for our tiny titans!
 if 'captured_images' not in st.session_state:
     st.session_state['captured_images'] = []
-if 'nlp_builder' not in st.session_state:
-    st.session_state['nlp_builder'] = None
 if 'cv_builder' not in st.session_state:
     st.session_state['cv_builder'] = None
-if 'nlp_loaded' not in st.session_state:
-    st.session_state['nlp_loaded'] = False
 if 'cv_loaded' not in st.session_state:
     st.session_state['cv_loaded'] = False
 if 'active_tab' not in st.session_state:
     st.session_state['active_tab'] = "Build Titan 🌱"
 
-@dataclass
-class ModelConfig:
-    name: str
-    base_model: str
-    size: str
-    domain: Optional[str] = None
-    model_type: str = "causal_lm"
-    @property
-    def model_path(self):
-        return f"models/{self.name}"
-
 @dataclass
 class DiffusionConfig:
+    """Config for our diffusion heroes 🦸‍♂️ - Keeps the blueprint snappy!"""
     name: str
     base_model: str
     size: str
@@ -74,28 +62,9 @@ class DiffusionConfig:
     def model_path(self):
         return f"diffusion_models/{self.name}"
 
-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}
-
+# Datasets 🎲 - Feeding our titans with pixel snacks and text treats!
 class DiffusionDataset(Dataset):
+    """Pixel party platter 🍕 - Images and text for diffusion delight!"""
     def __init__(self, images, texts):
         self.images = images
         self.texts = texts
@@ -104,126 +73,167 @@ class DiffusionDataset(Dataset):
     def __getitem__(self, idx):
         return {"image": self.images[idx], "text": self.texts[idx]}
 
-class ModelBuilder:
+class MicroDiffusionBuilder:
+    """Tiny titan of diffusion 🐣 - Small but mighty for quick demos!"""
     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. ☕"]
-    def load_model(self, model_path: str, config: Optional[ModelConfig] = None):
+        self.pipeline = None
+        self.jokes = ["Micro but mighty! 💪", "Small pixels, big dreams! 🌟"]
+    def load_model(self, model_path: str, config: Optional[DiffusionConfig] = None):
         try:
-            with st.spinner(f"Loading {model_path}... ⏳ (Patience, young padawan!)"):
-                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
+            with st.spinner(f"Loading {model_path}... ⏳ (Tiny titan powering up!)"):
+                # Micro Diffusion isn’t on HF yet; use a small U-Net placeholder from diffusers
+                self.pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="small_diffusion")
+                self.pipeline.to("cuda" if torch.cuda.is_available() else "cpu")
                 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)}")
-            logger.info(f"Successfully loaded Causal LM model: {model_path}")
-        except torch.cuda.OutOfMemoryError as e:
-            st.error(f"GPU memory error loading {model_path}: {str(e)} 💥 (Out of GPU juice!)")
-            logger.error(f"GPU memory error loading {model_path}: {str(e)}")
-            raise
-        except MemoryError as e:
-            st.error(f"CPU memory error loading {model_path}: {str(e)} 💥 (RAM ran away!)")
-            logger.error(f"CPU memory error loading {model_path}: {str(e)}")
-            raise
+            logger.info(f"Loaded Micro Diffusion: {model_path}")
         except Exception as e:
-            st.error(f"Failed to load {model_path}: {str(e)} 💥 (Something broke—check the logs!)")
+            st.error(f"Failed to load {model_path}: {str(e)} 💥 (Tiny titan tripped!)")
             logger.error(f"Failed to load {model_path}: {str(e)}")
             raise
         return self
-    def fine_tune_sft(self, csv_path: str, epochs: int = 3, batch_size: int = 4):
+    def fine_tune_sft(self, images, texts, epochs=3):
         try:
-            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()
+            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()
             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}... ⚙️ (The AI is lifting weights!)"):
+                with st.spinner(f"Epoch {epoch + 1}/{epochs}... ⚙️ (Micro titan flexing!)"):
                     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
+                        image = batch["image"][0].to(device)
+                        text = batch["text"][0]
+                        latents = self.pipeline.vae.encode(torch.tensor(np.array(image)).permute(2, 0, 1).unsqueeze(0).float().to(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(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(f"SFT Fine-tuning completed! 🎉 {random.choice(self.jokes)}")
-            logger.info(f"Successfully fine-tuned Causal LM model: {self.config.name}")
+                    st.write(f"Epoch {epoch + 1} done! Loss: {total_loss / len(dataloader):.4f}")
+            st.success(f"Micro Diffusion tuned! 🎉 {random.choice(self.jokes)}")
+            logger.info(f"Fine-tuned Micro Diffusion: {self.config.name}")
         except Exception as e:
-            st.error(f"Fine-tuning failed: {str(e)} 💥 (Training hit a snag!)")
-            logger.error(f"Fine-tuning failed: {str(e)}")
+            st.error(f"Tuning failed: {str(e)} 💥 (Micro snag!)")
+            logger.error(f"Tuning failed: {str(e)}")
             raise
         return self
     def save_model(self, path: str):
         try:
-            with st.spinner("Saving model... 💾 (Packing the AI’s suitcase!)"):
+            with st.spinner("Saving model... 💾 (Packing tiny pixels!)"):
                 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}! ✅ May the force be with it.")
-            logger.info(f"Model saved at {path}")
+                self.pipeline.save_pretrained(path)
+            st.success(f"Saved at {path}! ✅ Tiny titan secured!")
+            logger.info(f"Saved at {path}")
         except Exception as e:
-            st.error(f"Failed to save model: {str(e)} 💥 (Save operation crashed!)")
-            logger.error(f"Failed to save model: {str(e)}")
+            st.error(f"Save failed: {str(e)} 💥 (Packing mishap!)")
+            logger.error(f"Save failed: {str(e)}")
             raise
-    def evaluate(self, prompt: str, status_container=None):
-        self.model.eval()
-        if status_container:
-            status_container.write("Preparing to evaluate... 🧠 (Titan’s warming up its circuits!)")
-        logger.info(f"Evaluating prompt: {prompt}")
+    def generate(self, prompt: str):
         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)
-                result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-                logger.info(f"Generated response: {result}")
-                return result
+            return self.pipeline(prompt, num_inference_steps=20).images[0]
         except Exception as e:
-            logger.error(f"Evaluation error: {str(e)}")
-            if status_container:
-                status_container.error(f"Oops! Something broke: {str(e)} 💥 (Titan tripped over a wire!)")
-            return f"Error: {str(e)}"
+            st.error(f"Generation failed: {str(e)} 💥 (Pixel oopsie!)")
+            logger.error(f"Generation failed: {str(e)}")
+            raise
 
-class DiffusionBuilder:
+class LatentDiffusionBuilder:
+    """Scaled-down dreamer 🌙 - Latent magic for efficient artistry!"""
     def __init__(self):
         self.config = None
         self.pipeline = None
+        self.jokes = ["Latent vibes only! 🌀", "Small scale, big style! 🎨"]
     def load_model(self, model_path: str, config: Optional[DiffusionConfig] = None):
-        from diffusers import StableDiffusionPipeline
         try:
-            with st.spinner(f"Loading diffusion model {model_path}... ⏳"):
-                self.pipeline = StableDiffusionPipeline.from_pretrained(model_path)
+            with st.spinner(f"Loading {model_path}... ⏳ (Latent titan rising!)"):
+                self.pipeline = DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
+                self.pipeline.unet = torch.nn.Sequential(*list(self.pipeline.unet.children())[:2])  # Scale down U-Net
                 self.pipeline.to("cuda" if torch.cuda.is_available() else "cpu")
                 if config:
                     self.config = config
-            st.success(f"Diffusion model loaded! 🎨")
-            logger.info(f"Successfully loaded Diffusion model: {model_path}")
-        except torch.cuda.OutOfMemoryError as e:
-            st.error(f"GPU memory error loading {model_path}: {str(e)} 💥 (Out of GPU juice!)")
-            logger.error(f"GPU memory error loading {model_path}: {str(e)}")
+            st.success(f"Model loaded! 🎉 {random.choice(self.jokes)}")
+            logger.info(f"Loaded Latent Diffusion: {model_path}")
+        except Exception as e:
+            st.error(f"Failed to load {model_path}: {str(e)} 💥 (Latent hiccup!)")
+            logger.error(f"Failed to load {model_path}: {str(e)}")
             raise
-        except MemoryError as e:
-            st.error(f"CPU memory error loading {model_path}: {str(e)} 💥 (RAM ran away!)")
-            logger.error(f"CPU memory error loading {model_path}: {str(e)}")
+        return self
+    def fine_tune_sft(self, images, texts, epochs=3):
+        try:
+            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()
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            for epoch in range(epochs):
+                with st.spinner(f"Epoch {epoch + 1}/{epochs}... ⚙️ (Latent titan shaping up!)"):
+                    total_loss = 0
+                    for batch in dataloader:
+                        optimizer.zero_grad()
+                        image = batch["image"][0].to(device)
+                        text = batch["text"][0]
+                        latents = self.pipeline.vae.encode(torch.tensor(np.array(image)).permute(2, 0, 1).unsqueeze(0).float().to(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(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} done! Loss: {total_loss / len(dataloader):.4f}")
+            st.success(f"Latent Diffusion tuned! 🎉 {random.choice(self.jokes)}")
+            logger.info(f"Fine-tuned Latent Diffusion: {self.config.name}")
+        except Exception as e:
+            st.error(f"Tuning failed: {str(e)} 💥 (Latent snag!)")
+            logger.error(f"Tuning failed: {str(e)}")
             raise
+        return self
+    def save_model(self, path: str):
+        try:
+            with st.spinner("Saving model... 💾 (Packing latent dreams!)"):
+                os.makedirs(os.path.dirname(path), exist_ok=True)
+                self.pipeline.save_pretrained(path)
+            st.success(f"Saved at {path}! ✅ Latent titan stashed!")
+            logger.info(f"Saved at {path}")
         except Exception as e:
-            st.error(f"Failed to load {model_path}: {str(e)} 💥 (Something broke—check the logs!)")
+            st.error(f"Save failed: {str(e)} 💥 (Dreamy mishap!)")
+            logger.error(f"Save failed: {str(e)}")
+            raise
+    def generate(self, prompt: str):
+        try:
+            return self.pipeline(prompt, num_inference_steps=30).images[0]
+        except Exception as e:
+            st.error(f"Generation failed: {str(e)} 💥 (Latent oopsie!)")
+            logger.error(f"Generation failed: {str(e)}")
+            raise
+
+class FluxDiffusionBuilder:
+    """Distilled dynamo ⚡ - High-quality pixels in a small package!"""
+    def __init__(self):
+        self.config = None
+        self.pipeline = None
+        self.jokes = ["Flux-tastic! ✨", "Small size, big wow! 🎇"]
+    def load_model(self, model_path: str, config: Optional[DiffusionConfig] = None):
+        try:
+            with st.spinner(f"Loading {model_path}... ⏳ (Flux titan charging!)"):
+                self.pipeline = DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
+                self.pipeline.to("cuda" if torch.cuda.is_available() else "cpu")
+                if config:
+                    self.config = config
+            st.success(f"Model loaded! 🎉 {random.choice(self.jokes)}")
+            logger.info(f"Loaded FLUX.1 Distilled: {model_path}")
+        except Exception as e:
+            st.error(f"Failed to load {model_path}: {str(e)} 💥 (Flux fizzle!)")
             logger.error(f"Failed to load {model_path}: {str(e)}")
             raise
         return self
@@ -235,7 +245,7 @@ class DiffusionBuilder:
             self.pipeline.unet.train()
             device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
             for epoch in range(epochs):
-                with st.spinner(f"Training diffusion epoch {epoch + 1}/{epochs}... ⚙️"):
+                with st.spinner(f"Epoch {epoch + 1}/{epochs}... ⚙️ (Flux titan powering up!)"):
                     total_loss = 0
                     for batch in dataloader:
                         optimizer.zero_grad()
@@ -251,34 +261,35 @@ class DiffusionBuilder:
                         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! 🎨")
-            logger.info(f"Successfully fine-tuned Diffusion model: {self.config.name}")
+                    st.write(f"Epoch {epoch + 1} done! Loss: {total_loss / len(dataloader):.4f}")
+            st.success(f"FLUX Diffusion tuned! 🎉 {random.choice(self.jokes)}")
+            logger.info(f"Fine-tuned FLUX.1 Distilled: {self.config.name}")
         except Exception as e:
-            st.error(f"Fine-tuning failed: {str(e)} 💥 (Training hit a snag!)")
-            logger.error(f"Fine-tuning failed: {str(e)}")
+            st.error(f"Tuning failed: {str(e)} 💥 (Flux snag!)")
+            logger.error(f"Tuning failed: {str(e)}")
             raise
         return self
     def save_model(self, path: str):
         try:
-            with st.spinner("Saving diffusion model... 💾"):
+            with st.spinner("Saving model... 💾 (Packing flux magic!)"):
                 os.makedirs(os.path.dirname(path), exist_ok=True)
                 self.pipeline.save_pretrained(path)
-            st.success(f"Diffusion model saved at {path}! ✅")
-            logger.info(f"Diffusion model saved at {path}")
+            st.success(f"Saved at {path}! ✅ Flux titan secured!")
+            logger.info(f"Saved at {path}")
         except Exception as e:
-            st.error(f"Failed to save model: {str(e)} 💥 (Save operation crashed!)")
-            logger.error(f"Failed to save model: {str(e)}")
+            st.error(f"Save failed: {str(e)} 💥 (Fluxy mishap!)")
+            logger.error(f"Save failed: {str(e)}")
             raise
     def generate(self, prompt: str):
         try:
             return self.pipeline(prompt, num_inference_steps=50).images[0]
         except Exception as e:
-            st.error(f"Image generation failed: {str(e)} 💥 (Pixel party pooper!)")
-            logger.error(f"Image generation failed: {str(e)}")
+            st.error(f"Generation failed: {str(e)} 💥 (Flux oopsie!)")
+            logger.error(f"Generation failed: {str(e)}")
             raise
 
 def generate_filename(sequence, ext="png"):
+    """Time-stamped snapshots ⏰ - Keeps our pics organized with cam flair!"""
     from datetime import datetime
     import pytz
     central = pytz.timezone('US/Central')
@@ -286,26 +297,29 @@ def generate_filename(sequence, ext="png"):
     return f"{dt.strftime('%m-%d-%Y-%I-%M-%S-%p')}-{sequence}.{ext}"
 
 def get_download_link(file_path, mime_type="text/plain", label="Download"):
+    """Magic link maker 🔗 - Snag your files with a click!"""
     try:
         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>'
     except Exception as e:
-        logger.error(f"Failed to generate download link for {file_path}: {str(e)}")
+        logger.error(f"Failed to generate link for {file_path}: {str(e)}")
         return f"Error: Could not generate link for {file_path}"
 
 def zip_files(files, zip_path):
+    """Zip zap zoo 🎒 - Bundle up your goodies!"""
     try:
         with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
             for file in files:
                 zipf.write(file, os.path.basename(file))
         logger.info(f"Created ZIP file: {zip_path}")
     except Exception as e:
-        logger.error(f"Failed to create ZIP file {zip_path}: {str(e)}")
+        logger.error(f"Failed to create ZIP {zip_path}: {str(e)}")
         raise
 
 def delete_files(files):
+    """Trash titan 🗑️ - Clear the stage for new stars!"""
     try:
         for file in files:
             os.remove(file)
@@ -315,14 +329,16 @@ def delete_files(files):
         logger.error(f"Failed to delete files: {str(e)}")
         raise
 
-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_model_files():
+    """Model treasure hunt 🗺️ - Find our diffusion gems!"""
+    return [d for d in glob.glob("diffusion_models/*") if os.path.isdir(d)]
 
 def get_gallery_files(file_types):
+    """Gallery curator 🖼️ - Showcase our pixel masterpieces!"""
     return sorted(list(set(f for ext in file_types for f in glob.glob(f"*.{ext}"))))
 
 def update_gallery():
+    """Gallery refresh 🌟 - Keep the art flowing!"""
     media_files = get_gallery_files(["png"])
     if media_files:
         cols = st.sidebar.columns(2)
@@ -332,8 +348,8 @@ def update_gallery():
                 st.markdown(get_download_link(file, "image/png", "Download Snap 📸"), unsafe_allow_html=True)
 
 def get_available_video_devices():
-    # Assuming 6 cameras as per your setup; adjust if needed
-    video_devices = [f"Camera {i} 🎥" for i in range(6)]
+    """Camera roll call 🎥 - Who’s ready to shine?"""
+    video_devices = [f"Camera {i} 🎥" for i in range(6)]  # 6 cams as per your setup
     try:
         detected = []
         for i in range(10):
@@ -345,74 +361,15 @@ def get_available_video_devices():
                 logger.info(f"Detected camera at index {i}")
                 cap.release()
         if detected:
-            video_devices = detected[:6]  # Limit to 6 as per your spec
+            video_devices = detected[:6]  # Cap at 6
     except Exception as e:
         logger.error(f"Error detecting cameras: {str(e)}")
     return video_devices
 
-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."
-
-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_search("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)}
-        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"}
-        data = [
-            {"Location": "New York", "Travel Time (hrs)": travel_times["New York"], "Luxury Idea": "Gold-plated Batman statues"},
-            {"Location": "Wayne Manor", "Travel Time (hrs)": 0.0, "Luxury Idea": "VR superhero battles"}
-        ]
-        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=50).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("SFT Tiny Titans 🚀 (Small but Mighty!)")
+st.title("SFT Tiny Titans 🚀 (Small Diffusion Delight!)")
 
 st.sidebar.header("Media Gallery 🎨")
-gallery_size = st.sidebar.slider("Gallery Size 📸", 1, 10, 4, help="Adjust how many epic captures you see! 🌟")
+gallery_size = st.sidebar.slider("Gallery Size 📸", 1, 10, 4, help="How many snaps to flaunt? 🌟")
 update_gallery()
 
 col1, col2 = st.sidebar.columns(2)
@@ -423,7 +380,7 @@ with col1:
             zip_path = f"snapshot_collection_{int(time.time())}.zip"
             zip_files(media_files, zip_path)
             st.sidebar.markdown(get_download_link(zip_path, "application/zip", "Download All Snaps 📦"), unsafe_allow_html=True)
-            st.sidebar.success("Snapshots zipped! 🎉 Grab your loot!")
+            st.sidebar.success("Snaps zipped! 🎉 Grab your loot!")
         else:
             st.sidebar.warning("No snaps to zip! 📸 Snap some first!")
 with col2:
@@ -444,20 +401,6 @@ if uploaded_files:
             f.write(uploaded_file.getvalue())
         logger.info(f"Uploaded file: {filename}")
 
-st.sidebar.subheader("Audio Gallery 🎵")
-audio_files = get_gallery_files(["mp3"])
-if audio_files:
-    for file in audio_files[:gallery_size]:
-        st.sidebar.audio(file, format="audio/mp3")
-        st.sidebar.markdown(get_download_link(file, "audio/mp3", f"Grab Tune 🎵"), unsafe_allow_html=True)
-
-st.sidebar.subheader("Video Gallery 🎥")
-video_files = get_gallery_files(["mp4"])
-if video_files:
-    for file in video_files[:gallery_size]:
-        st.sidebar.video(file, format="video/mp4")
-        st.sidebar.markdown(get_download_link(file, "video/mp4", f"Snag Clip 🎬"), unsafe_allow_html=True)
-
 st.sidebar.subheader("Image Gallery 🖼️")
 image_files = get_gallery_files(["png", "jpeg"])
 if image_files:
@@ -467,50 +410,30 @@ if image_files:
             st.image(Image.open(file), caption=file, use_container_width=True)
             st.markdown(get_download_link(file, "image/png" if file.endswith(".png") else "image/jpeg", f"Save Pic 🖼️"), unsafe_allow_html=True)
 
-st.sidebar.subheader("Markdown Gallery 📝")
-md_files = get_gallery_files(["md"])
-if md_files:
-    for file in md_files[:gallery_size]:
-        with open(file, "r") as f:
-            st.sidebar.markdown(f.read())
-        st.sidebar.markdown(get_download_link(file, "text/markdown", f"Get Note 📝"), unsafe_allow_html=True)
-
-st.sidebar.subheader("Document Gallery 📜")
-doc_files = get_gallery_files(["pdf", "docx"])
-if doc_files:
-    for file in doc_files[:gallery_size]:
-        mime_type = "application/pdf" if file.endswith(".pdf") else "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-        st.sidebar.markdown(get_download_link(file, mime_type, f"Fetch Doc 📜"), unsafe_allow_html=True)
-
 st.sidebar.subheader("Model Management 🗂️")
-model_type = st.sidebar.selectbox("Model Type", ["Causal LM", "Diffusion"])
-model_dirs = get_model_files("causal_lm" if model_type == "Causal LM" else "diffusion")
+model_dirs = get_model_files()
 selected_model = st.sidebar.selectbox("Select Saved Model", ["None"] + model_dirs)
+model_type = st.sidebar.selectbox("Diffusion Type", ["Micro Diffusion", "Latent Diffusion", "FLUX.1 Distilled"])
 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 = {
+        "Micro Diffusion": MicroDiffusionBuilder,
+        "Latent Diffusion": LatentDiffusionBuilder,
+        "FLUX.1 Distilled": FluxDiffusionBuilder
+    }[model_type]()
+    config = DiffusionConfig(name=os.path.basename(selected_model), base_model="unknown", size="small")
     try:
         builder.load_model(selected_model, config)
-        if model_type == "Causal LM":
-            st.session_state['nlp_builder'] = builder
-            st.session_state['nlp_loaded'] = True
-        else:
-            st.session_state['cv_builder'] = builder
-            st.session_state['cv_loaded'] = True
+        st.session_state['cv_builder'] = builder
+        st.session_state['cv_loaded'] = True
         st.rerun()
     except Exception as e:
         st.error(f"Model load failed: {str(e)} 💥 (Check logs for details!)")
 
 st.sidebar.subheader("Model Status 🚦")
-st.sidebar.write(f"**NLP Model**: {'Loaded' if st.session_state['nlp_loaded'] else 'Not Loaded'} {'(Active)' if st.session_state['nlp_loaded'] and isinstance(st.session_state.get('nlp_builder'), ModelBuilder) else ''}")
-st.sidebar.write(f"**CV Model**: {'Loaded' if st.session_state['cv_loaded'] else 'Not Loaded'} {'(Active)' if st.session_state['cv_loaded'] and isinstance(st.session_state.get('cv_builder'), DiffusionBuilder) else ''}")
+st.sidebar.write(f"**CV Model**: {'Loaded' if st.session_state['cv_loaded'] else 'Not Loaded'} {'(Active)' if st.session_state['cv_loaded'] and isinstance(st.session_state.get('cv_builder'), (MicroDiffusionBuilder, LatentDiffusionBuilder, FluxDiffusionBuilder)) else ''}")
 
-tabs = [
-    "Build Titan 🌱", "Camera Snap 📷", 
-    "Fine-Tune Titan (NLP) 🔧", "Test Titan (NLP) 🧪", "Agentic RAG Party (NLP) 🌐",
-    "Fine-Tune Titan (CV) 🔧", "Test Titan (CV) 🧪", "Agentic RAG Party (CV) 🌐"
-]
-tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8 = st.tabs(tabs)
+tabs = ["Build Titan 🌱", "Camera Snap 📷", "Fine-Tune Titan (CV) 🔧", "Test Titan (CV) 🧪", "Agentic RAG Party (CV) 🌐"]
+tab1, tab2, tab3, tab4, tab5 = st.tabs(tabs)
 
 for i, tab in enumerate(tabs):
     if st.session_state['active_tab'] != tab and st.session_state.get(f'tab{i}_active', False):
@@ -520,24 +443,22 @@ for i, tab in enumerate(tabs):
 
 with tab1:
     st.header("Build Titan 🌱")
-    model_type = st.selectbox("Model Type", ["Causal LM", "Diffusion"], key="build_type")
+    model_type = st.selectbox("Diffusion Type", ["Micro Diffusion", "Latent Diffusion", "FLUX.1 Distilled"], key="build_type")
     base_model = st.selectbox("Select Tiny Model", 
-        ["HuggingFaceTB/SmolLM-135M", "HuggingFaceTB/SmolLM-360M", "Qwen/Qwen1.5-0.5B-Chat"] if model_type == "Causal LM" else 
-        ["stabilityai/stable-diffusion-2-base", "runwayml/stable-diffusion-v1-5"])
+        ["sony/micro-diffusion" if model_type == "Micro Diffusion" else "runwayml/stable-diffusion-v1-5" if model_type == "Latent Diffusion" else "black-forest-labs/flux.1-distilled"])
     model_name = st.text_input("Model Name", f"tiny-titan-{int(time.time())}")
-    domain = st.text_input("Target Domain", "general", help="Where will your Titan flex its muscles? 💪") if model_type == "Causal LM" else None
     if st.button("Download Model ⬇️"):
-        config = ModelConfig(name=model_name, base_model=base_model, size="small", domain=domain) if model_type == "Causal LM" else DiffusionConfig(name=model_name, base_model=base_model, size="small")
-        builder = ModelBuilder() if model_type == "Causal LM" else DiffusionBuilder()
+        config = DiffusionConfig(name=model_name, base_model=base_model, size="small")
+        builder = {
+            "Micro Diffusion": MicroDiffusionBuilder,
+            "Latent Diffusion": LatentDiffusionBuilder,
+            "FLUX.1 Distilled": FluxDiffusionBuilder
+        }[model_type]()
         try:
             builder.load_model(base_model, config)
             builder.save_model(config.model_path)
-            if model_type == "Causal LM":
-                st.session_state['nlp_builder'] = builder
-                st.session_state['nlp_loaded'] = True
-            else:
-                st.session_state['cv_builder'] = builder
-                st.session_state['cv_loaded'] = True
+            st.session_state['cv_builder'] = builder
+            st.session_state['cv_loaded'] = True
             st.rerun()
         except Exception as e:
             st.error(f"Model build failed: {str(e)} 💥 (Check logs for details!)")
@@ -612,177 +533,131 @@ with tab2:
         st.info("🚨 Single shots only—craft your masterpiece! 🎨")
 
 with tab3:
-    st.header("Fine-Tune Titan (NLP) 🔧 (Teach Your Word Wizard Some Tricks!)")
-    if not st.session_state['nlp_loaded'] or not isinstance(st.session_state['nlp_builder'], ModelBuilder):
-        st.warning("Please build or load an NLP Titan first! ⚠️ (No word wizard, no magic!)")
-    else:
-        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."},
-                {"prompt": "What is a neural network?", "response": "A neural network is a brainy AI mimicking human noggins."},
-            ]
-            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}! ✅ (Fresh from the data oven!)")
-        uploaded_csv = st.file_uploader("Upload CSV for SFT 📜", type="csv", help="Feed your Titan some tasty prompt-response pairs! 🍽️")
-        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['nlp_builder'].config.name}-sft-{int(time.time())}"
-            new_config = ModelConfig(name=new_model_name, base_model=st.session_state['nlp_builder'].config.base_model, size="small", domain=st.session_state['nlp_builder'].config.domain)
-            st.session_state['nlp_builder'].config = new_config
-            with st.status("Fine-tuning NLP Titan... ⏳ (Whipping words into shape!)", expanded=True) as status:
-                st.session_state['nlp_builder'].fine_tune_sft(csv_path)
-                st.session_state['nlp_builder'].save_model(new_config.model_path)
-                status.update(label="Fine-tuning completed! 🎉 (Wordsmith Titan unleashed!)", state="complete")
-            zip_path = f"{new_config.model_path}.zip"
-            zip_files([new_config.model_path], zip_path)
-            st.markdown(get_download_link(zip_path, "application/zip", "Download Fine-Tuned NLP Titan 📦"), unsafe_allow_html=True)
-
-with tab4:
-    st.header("Test Titan (NLP) 🧪 (Put Your Word Wizard to the Test!)")
-    if not st.session_state['nlp_loaded'] or not isinstance(st.session_state['nlp_builder'], ModelBuilder):
-        st.warning("Please build or load an NLP Titan first! ⚠️ (No word wizard, no test drive!)")
-    else:
-        if st.session_state['nlp_builder'].sft_data:
-            st.write("Testing with SFT Data:")
-            with st.spinner("Running SFT data tests... ⏳ (Titan’s flexing its word muscles!)"):
-                for item in st.session_state['nlp_builder'].sft_data[:3]:
-                    prompt = item["prompt"]
-                    expected = item["response"]
-                    status_container = st.empty()
-                    generated = st.session_state['nlp_builder'].evaluate(prompt, status_container)
-                    st.write(f"**Prompt**: {prompt}")
-                    st.write(f"**Expected**: {expected}")
-                    st.write(f"**Generated**: {generated} (Titan says: '{random.choice(['Bleep bloop!', 'I am groot!', '42!'])}')")
-                    st.write("---")
-                    status_container.empty()
-        test_prompt = st.text_area("Enter Test Prompt 🗣️", "What is AI?", help="Ask your Titan anything—it’s ready to chat! 😜")
-        if st.button("Run Test ▶️"):
-            with st.spinner("Testing your prompt... ⏳ (Titan’s pondering deeply!)"):
-                status_container = st.empty()
-                result = st.session_state['nlp_builder'].evaluate(test_prompt, status_container)
-                st.write(f"**Generated Response**: {result} (Titan’s wisdom unleashed!)")
-                status_container.empty()
-
-with tab5:
-    st.header("Agentic RAG Party (NLP) 🌐 (Party Like It’s 2099!)")
-    st.write("This demo uses your SFT-tuned NLP Titan to plan a superhero party with mock retrieval!")
-    if not st.session_state['nlp_loaded'] or not isinstance(st.session_state['nlp_builder'], ModelBuilder):
-        st.warning("Please build or load an NLP Titan first! ⚠️ (No word wizard, no party!)")
-    else:
-        if st.button("Run NLP RAG Demo 🎉"):
-            with st.spinner("Loading your SFT-tuned NLP Titan... ⏳ (Titan’s suiting up!)"):
-                agent = PartyPlannerAgent(st.session_state['nlp_builder'].model, st.session_state['nlp_builder'].tokenizer)
-                st.write("Agent ready! 🦸���♂️ (Time to plan an epic bash!)")
-            task = """
-            Plan a luxury superhero-themed party at Wayne Manor (42.3601° N, 71.0589° W). 
-            Use mock search results for the latest superhero party trends, refine for luxury elements 
-            (decorations, entertainment, catering), and calculate cargo travel times from key locations 
-            (New York: 40.7128° N, 74.0060° W; LA: 34.0522° N, 118.2437° W; London: 51.5074° N, 0.1278° W) 
-            to Wayne Manor. Create a plan with at least 6 entries in a pandas dataframe.
-            """
-            with st.spinner("Planning the ultimate superhero bash... ⏳ (Calling all caped crusaders!)"):
-                try:
-                    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"}
-                    search_result = mock_search("superhero party trends")
-                    prompt = f"""
-                    Given this context from a search: "{search_result}"
-                    Plan a luxury superhero-themed party at Wayne Manor. Suggest luxury decorations, entertainment, and catering ideas.
-                    """
-                    plan_text = agent.generate(prompt)
-                    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)},
-                    ]
-                    plan_df = pd.DataFrame(data)
-                    st.write("Agentic RAG Party Plan:")
-                    st.dataframe(plan_df)
-                    st.write("Party on, Wayne! 🦸‍♂️🎉")
-                except Exception as e:
-                    st.error(f"Error planning party: {str(e)} (Even Superman has kryptonite days!)")
-                    logger.error(f"Error in NLP RAG demo: {str(e)}")
-
-with tab6:
-    st.header("Fine-Tune Titan (CV) 🔧 (Paint Your Titan’s Masterpiece!)")
-    if not st.session_state['cv_loaded'] or not isinstance(st.session_state['cv_builder'], DiffusionBuilder):
+    st.header("Fine-Tune Titan (CV) 🔧 (Sculpt Your Pixel Prodigy!)")
+    if not st.session_state['cv_loaded'] or not isinstance(st.session_state['cv_builder'], (MicroDiffusionBuilder, LatentDiffusionBuilder, FluxDiffusionBuilder)):
         st.warning("Please build or load a CV Titan first! ⚠️ (No artist, no canvas!)")
     else:
         captured_images = get_gallery_files(["png"])
         if len(captured_images) >= 2:
-            demo_data = [{"image": img, "text": f"Superhero {os.path.basename(img).split('.')[0]}"} for img in captured_images[:min(len(captured_images), 10)]]
-            edited_data = st.data_editor(pd.DataFrame(demo_data), num_rows="dynamic", help="Craft your image-text pairs like a superhero artist! 🎨")
-            if st.button("Fine-Tune with Dataset 🔄"):
-                images = [Image.open(row["image"]) for _, row in edited_data.iterrows()]
-                texts = [row["text"] for _, row in edited_data.iterrows()]
-                new_model_name = f"{st.session_state['cv_builder'].config.name}-sft-{int(time.time())}"
+            # Use Case 1: Denoising (Micro Diffusion)
+            st.subheader("Use Case 1: Denoise Snapshots 🌟")
+            denoising_data = [{"image": img, "text": f"Denoised {os.path.basename(img).split('-')[4]} snap"} for img in captured_images[:min(len(captured_images), 10)]]
+            denoising_edited = st.data_editor(pd.DataFrame(denoising_data), num_rows="dynamic", help="Craft denoising pairs! 🌟")
+            if st.button("Fine-Tune Denoising 🔄"):
+                images = [Image.open(row["image"]) for _, row in denoising_edited.iterrows()]
+                texts = [row["text"] for _, row in denoising_edited.iterrows()]
+                new_model_name = f"{st.session_state['cv_builder'].config.name}-denoise-{int(time.time())}"
                 new_config = DiffusionConfig(name=new_model_name, base_model=st.session_state['cv_builder'].config.base_model, size="small")
                 st.session_state['cv_builder'].config = new_config
-                with st.status("Fine-tuning CV Titan... ⏳ (Brushing up those pixels!)", expanded=True) as status:
+                with st.status("Fine-tuning for denoising... ⏳ (Polishing pixels!)", expanded=True) as status:
                     st.session_state['cv_builder'].fine_tune_sft(images, texts)
                     st.session_state['cv_builder'].save_model(new_config.model_path)
-                    status.update(label="Fine-tuning completed! 🎉 (Pixel Titan unleashed!)", state="complete")
+                    status.update(label="Denoising tuned! 🎉 (Pixel shine unleashed!)", state="complete")
                 zip_path = f"{new_config.model_path}.zip"
                 zip_files([new_config.model_path], zip_path)
-                st.markdown(get_download_link(zip_path, "application/zip", "Download Fine-Tuned CV Titan 📦"), unsafe_allow_html=True)
-            csv_path = f"sft_dataset_{int(time.time())}.csv"
-            with open(csv_path, "w", newline="") as f:
+                st.markdown(get_download_link(zip_path, "application/zip", "Download Denoised Titan 📦"), unsafe_allow_html=True)
+            denoising_csv = f"denoise_dataset_{int(time.time())}.csv"
+            with open(denoising_csv, "w", newline="") as f:
                 writer = csv.writer(f)
                 writer.writerow(["image", "text"])
-                for _, row in edited_data.iterrows():
+                for _, row in denoising_edited.iterrows():
                     writer.writerow([row["image"], row["text"]])
-            st.markdown(get_download_link(csv_path, "text/csv", "Download SFT Dataset CSV 📜"), unsafe_allow_html=True)
+            st.markdown(get_download_link(denoising_csv, "text/csv", "Download Denoising CSV 📜"), unsafe_allow_html=True)
+
+            # Use Case 2: Stylization (Latent Diffusion)
+            st.subheader("Use Case 2: Stylize Snapshots 🎨")
+            stylize_data = [{"image": img, "text": f"Neon {os.path.basename(img).split('-')[4]} style"} for img in captured_images[:min(len(captured_images), 10)]]
+            stylize_edited = st.data_editor(pd.DataFrame(stylize_data), num_rows="dynamic", help="Craft stylized pairs! 🎨")
+            if st.button("Fine-Tune Stylization 🔄"):
+                images = [Image.open(row["image"]) for _, row in stylize_edited.iterrows()]
+                texts = [row["text"] for _, row in stylize_edited.iterrows()]
+                new_model_name = f"{st.session_state['cv_builder'].config.name}-stylize-{int(time.time())}"
+                new_config = DiffusionConfig(name=new_model_name, base_model=st.session_state['cv_builder'].config.base_model, size="small")
+                st.session_state['cv_builder'].config = new_config
+                with st.status("Fine-tuning for stylization... ⏳ (Painting pixels!)", expanded=True) as status:
+                    st.session_state['cv_builder'].fine_tune_sft(images, texts)
+                    st.session_state['cv_builder'].save_model(new_config.model_path)
+                    status.update(label="Stylization tuned! 🎉 (Pixel art unleashed!)", state="complete")
+                zip_path = f"{new_config.model_path}.zip"
+                zip_files([new_config.model_path], zip_path)
+                st.markdown(get_download_link(zip_path, "application/zip", "Download Stylized Titan 📦"), unsafe_allow_html=True)
+            stylize_md = f"stylize_dataset_{int(time.time())}.md"
+            with open(stylize_md, "w") as f:
+                f.write("# Stylization Dataset\n\n")
+                for _, row in stylize_edited.iterrows():
+                    f.write(f"- `{row['image']}`: {row['text']}\n")
+            st.markdown(get_download_link(stylize_md, "text/markdown", "Download Stylization MD 📝"), unsafe_allow_html=True)
+
+            # Use Case 3: Multi-Angle Generation (FLUX.1)
+            st.subheader("Use Case 3: Multi-Angle Snapshots 🌐")
+            multiangle_data = [{"image": img, "text": f"View from {os.path.basename(img).split('-')[4]}"} for img in captured_images[:min(len(captured_images), 10)]]
+            multiangle_edited = st.data_editor(pd.DataFrame(multiangle_data), num_rows="dynamic", help="Craft multi-angle pairs! 🌐")
+            if st.button("Fine-Tune Multi-Angle 🔄"):
+                images = [Image.open(row["image"]) for _, row in multiangle_edited.iterrows()]
+                texts = [row["text"] for _, row in multiangle_edited.iterrows()]
+                new_model_name = f"{st.session_state['cv_builder'].config.name}-multiangle-{int(time.time())}"
+                new_config = DiffusionConfig(name=new_model_name, base_model=st.session_state['cv_builder'].config.base_model, size="small")
+                st.session_state['cv_builder'].config = new_config
+                with st.status("Fine-tuning for multi-angle... ⏳ (Spinning pixels!)", expanded=True) as status:
+                    st.session_state['cv_builder'].fine_tune_sft(images, texts)
+                    st.session_state['cv_builder'].save_model(new_config.model_path)
+                    status.update(label="Multi-angle tuned! 🎉 (Pixel views unleashed!)", state="complete")
+                zip_path = f"{new_config.model_path}.zip"
+                zip_files([new_config.model_path], zip_path)
+                st.markdown(get_download_link(zip_path, "application/zip", "Download Multi-Angle Titan 📦"), unsafe_allow_html=True)
+            multiangle_csv = f"multiangle_dataset_{int(time.time())}.csv"
+            with open(multiangle_csv, "w", newline="") as f:
+                writer = csv.writer(f)
+                writer.writerow(["image", "text"])
+                for _, row in multiangle_edited.iterrows():
+                    writer.writerow([row["image"], row["text"]])
+            st.markdown(get_download_link(multiangle_csv, "text/csv", "Download Multi-Angle CSV 📜"), unsafe_allow_html=True)
 
-with tab7:
+with tab4:
     st.header("Test Titan (CV) 🧪 (Unleash Your Pixel Power!)")
-    if not st.session_state['cv_loaded'] or not isinstance(st.session_state['cv_builder'], DiffusionBuilder):
+    if not st.session_state['cv_loaded'] or not isinstance(st.session_state['cv_builder'], (MicroDiffusionBuilder, LatentDiffusionBuilder, FluxDiffusionBuilder)):
         st.warning("Please build or load a CV Titan first! ⚠️ (No artist, no masterpiece!)")
     else:
-        test_prompt = st.text_area("Enter Test Prompt 🎨", "Neon Batman", help="Dream up a wild image—your Titan’s got the brush! 🖌️")
-        if st.button("Run Test ▶️"):
-            with st.spinner("Painting your masterpiece... ⏳ (Titan’s mixing colors!)"):
+        st.subheader("Test Your Titan 🎨")
+        test_prompt = st.text_area("Prompt 🎤", "Neon glow from cam0", help="Dream up a wild image—your Titan’s ready to paint! 🖌️")
+        if st.button("Generate ▶️"):
+            with st.spinner("Crafting your masterpiece... ⏳ (Titan’s mixing pixels!)"):
                 image = st.session_state['cv_builder'].generate(test_prompt)
-                st.image(image, caption="Generated Image", use_container_width=True)
+                st.image(image, caption=f"Generated: {test_prompt}", use_container_width=True)
 
-with tab8:
-    st.header("Agentic RAG Party (CV) 🌐 (Party with Pixels!)")
-    st.write("This demo uses your SFT-tuned CV Titan to generate superhero party images with mock retrieval!")
-    if not st.session_state['cv_loaded'] or not isinstance(st.session_state['cv_builder'], DiffusionBuilder):
+with tab5:
+    st.header("Agentic RAG Party (CV) 🌐 (Pixel Party Extravaganza!)")
+    st.write("Generate superhero party vibes from your tuned Titan! 🎉")
+    if not st.session_state['cv_loaded'] or not isinstance(st.session_state['cv_builder'], (MicroDiffusionBuilder, LatentDiffusionBuilder, FluxDiffusionBuilder)):
         st.warning("Please build or load a CV Titan first! ⚠️ (No artist, no party!)")
     else:
-        if st.button("Run CV RAG Demo 🎉"):
-            with st.spinner("Loading your SFT-tuned CV Titan... ⏳ (Titan’s grabbing its paintbrush!)"):
-                agent = CVPartyPlannerAgent(st.session_state['cv_builder'].pipeline)
-                st.write("Agent ready! 🎨 (Time to paint an epic bash!)")
-            task = "Generate images for a luxury superhero-themed party."
+        if st.button("Run RAG Demo 🎉"):
+            with st.spinner("Loading your pixel party titan... ⏳ (Titan’s grabbing its brush!)"):
+                class CVPartyAgent:
+                    def __init__(self, pipeline):
+                        self.pipeline = pipeline
+                    def generate(self, prompt: str) -> Image.Image:
+                        return self.pipeline(prompt, num_inference_steps=50).images[0]
+                    def plan_party(self):
+                        prompts = [
+                            "Gold-plated Batman statue from cam0",
+                            "VR superhero battle scene from cam1",
+                            "Neon-lit Avengers tower from cam2"
+                        ]
+                        data = [{"Theme": f"Scene {i+1}", "Image Idea": prompt} for i, prompt in enumerate(prompts)]
+                        return pd.DataFrame(data)
+                agent = CVPartyAgent(st.session_state['cv_builder'].pipeline)
+                st.write("Party agent ready! 🎨 (Time to paint an epic bash!)")
             with st.spinner("Crafting superhero party visuals... ⏳ (Pixels assemble!)"):
                 try:
-                    plan_df = agent.plan_party(task)
+                    plan_df = agent.plan_party()
                     st.dataframe(plan_df)
                     for _, row in plan_df.iterrows():
                         image = agent.generate(row["Image Idea"])
                         st.image(image, caption=f"{row['Theme']} - {row['Image Idea']}", use_container_width=True)
                 except Exception as e:
-                    st.error(f"Error in CV RAG demo: {str(e)} 💥 (Pixel party crashed!)")
-                    logger.error(f"Error in CV RAG demo: {str(e)}")
+                    st.error(f"Party crashed: {str(e)} 💥 (Pixel oopsie!)")
+                    logger.error(f"RAG demo failed: {str(e)}")
 
 st.sidebar.subheader("Action Logs 📜")
 log_container = st.sidebar.empty()