Update README.md

README.md

@@ -1,13 +1,14 @@
 ---
-title: REST API with Gradio and Huggingface Spaces
-emoji: 👩‍💻
+title: Dynamic Tab Loading Examples
+emoji: 🏢
 colorFrom: blue
-colorTo: green
+colorTo: indigo
 sdk: gradio
 sdk_version: 5.34.2
 app_file: app.py
-pinned: false
-license: openrail
+pinned: true
+license: apache-2.0
+short_description: Exploring different loading methods for a HF Space
 ---
 
 # Dynamic Space Loading
@@ -64,4 +65,209 @@ license: openrail
 
 ---
 
-**If you want a code example for tab-to-tab data sharing, or want to explore advanced iframe communication (with custom JS), let me know!**
+This is a very insightful and advanced question! Here’s a breakdown of what’s possible, what’s not, and what’s practical with Gradio, Hugging Face Spaces, and Python environments:
+
+---
+
+## 2. **GPU Spaces (transformers/diffusers) Loading/Unloading**
+
+### **A. In a Single Python Process (One Space, One App)**
+- **You can load multiple models/pipelines in one Gradio app.**
+  - You can have a dropdown or tabs to select which model/task/pipeline to use.
+  - You can load/unload models on demand (though loading large models is slow).
+  - You can keep all models in memory (if you have enough GPU RAM), or load/unload as needed.
+- **You cannot have truly separate environments** (e.g., different Python dependencies, CUDA versions, or isolated memory) in a single Space.
+  - All code runs in the same Python process/environment.
+  - All models share the same GPU/CPU memory pool.
+
+#### **Example:**
+```python
+from transformers import pipeline
+import gradio as gr
+
+# Preload or lazy-load multiple pipelines
+pipe1 = pipeline("text-generation", model="gpt2")
+pipe2 = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+def run_model(input, model_choice):
+    if model_choice == "Text Generation":
+        return pipe1(input)
+    elif model_choice == "Image Classification":
+        return pipe2(input)
+    # ... more models
+
+gr.Interface(
+    fn=run_model,
+    inputs=[gr.Textbox(), gr.Dropdown(["Text Generation", "Image Classification"])],
+    outputs="auto"
+).launch()
+```
+- You can use tabs or dropdowns to switch between models/tasks.
+
+---
+
+### **B. Multiple Gradio Apps in One Space**
+- You can define multiple Gradio interfaces in one script and show/hide them with tabs or dropdowns.
+- **But:** They still share the same Python process and memory.
+
+---
+
+### **C. True Isolation (Multiple Environments)**
+- **Not possible in a single Hugging Face Space.**
+  - You cannot have multiple Python environments, different dependency sets, or isolated GPU memory pools in one Space.
+  - Each Space is a single container/process.
+
+---
+
+### **D. What About Docker or Subprocesses?**
+- Hugging Face Spaces (hosted) do not support running multiple containers or true subprocess isolation with different environments.
+- On your own infrastructure, you could use Docker or subprocesses, but this is not supported on Spaces.
+
+---
+
+## 3. **Best Practices for Multi-Model/Multi-Task Apps**
+
+- **Lazy-load models:** Only load a model when its tab is selected, and unload it when switching (if memory is a concern).
+- **Use a single environment:** Install all dependencies needed for all models in your `requirements.txt`.
+- **Warn users about memory:** If users switch between large models, GPU memory may fill up and require manual cleanup (e.g., `torch.cuda.empty_cache()`).
+
+---
+
+## 4. **Summary Table**
+
+| Approach                        | Isolation | Multiple Models | Multiple Envs | GPU Sharing | Supported on Spaces |
+|----------------------------------|:---------:|:--------------:|:-------------:|:-----------:|:------------------:|
+| Single Gradio app, many models   |   No      |      Yes       |      No       |    Yes      |        Yes         |
+| Multiple Gradio apps in one file |   No      |      Yes       |      No       |    Yes      |        Yes         |
+| Multiple Spaces (one per app)    |   Yes     |      Yes       |     Yes       |   Isolated  |        Yes         |
+| Docker/subprocess isolation      |   Yes     |      Yes       |     Yes       |   Isolated  |   No (on Spaces)   |
+
+---
+
+## 4. **What’s Practical?**
+
+- **For most use cases:**  
+  - Use a single app with tabs/dropdowns to select the model/task.
+  - Lazy-load and unload models as needed to manage memory.
+- **For true isolation:**  
+  - Use multiple Spaces (one per app/model) or host your own infrastructure with Docker.
+
+---
+
+## 5. **Properly Unloading Models, Weights, and Freeing Memory in PyTorch/Diffusers**
+
+When working with large models (especially on GPU), it's important to:
+- **Delete references to the model and pipeline**
+- **Call `gc.collect()`** to trigger Python's garbage collector
+- **Call `torch.cuda.empty_cache()`** (if using CUDA) to free GPU memory
+
+### **Best Practice Pattern**
+
+Here’s a robust pattern for loading and unloading models in a multi-model Gradio app:
+
+```python
+import torch
+import gc
+from diffusers import DiffusionPipeline
+
+model_cache = {}
+
+def load_diffusion_model(model_id, dtype=torch.float32, device="cpu"):
+    pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype)
+    pipe = pipe.to(device)
+    pipe.enable_attention_slicing()
+    return pipe
+
+def unload_model(model_key):
+    # Remove from cache
+    if model_key in model_cache:
+        del model_cache[model_key]
+    # Run Python garbage collection
+    gc.collect()
+    # Free GPU memory if using CUDA
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+```
+
+### **How to Use in a Gradio Tab**
+
+```python
+import gradio as gr
+
+model_id = "LPX55/FLUX.1-merged_lightning_v2"
+model_key = "flux"
+device = "cpu"  # or "cuda" if available and desired
+
+def do_load():
+    if model_key not in model_cache:
+        model_cache[model_key] = load_diffusion_model(model_id, torch.float32, device)
+    return "Model loaded!"
+
+def do_unload():
+    unload_model(model_key)
+    return "Model unloaded!"
+
+def run_inference(prompt, width, height, steps):
+    if model_key not in model_cache:
+        return None, "Model not loaded!"
+    pipe = model_cache[model_key]
+    image = pipe(
+        prompt=prompt,
+        width=width,
+        height=height,
+        num_inference_steps=steps,
+    ).images[0]
+    return image, "Success!"
+
+with gr.Blocks() as demo:
+    status = gr.Markdown("Model not loaded.")
+    load_btn = gr.Button("Load Model")
+    unload_btn = gr.Button("Unload Model")
+    prompt = gr.Textbox(label="Prompt", value="A cat holding a sign that says hello world")
+    width = gr.Slider(256, 1536, value=768, step=64, label="Width")
+    height = gr.Slider(256, 1536, value=1152, step=64, label="Height")
+    steps = gr.Slider(1, 50, value=8, step=1, label="Inference Steps")
+    run_btn = gr.Button("Generate Image")
+    output_img = gr.Image(label="Output Image")
+    output_msg = gr.Textbox(label="Status", interactive=False)
+
+    load_btn.click(do_load, None, status)
+    unload_btn.click(do_unload, None, status)
+    run_btn.click(run_inference, [prompt, width, height, steps], [output_img, output_msg])
+
+demo.launch()
+```
+
+---
+
+### **Key Points**
+- **Always delete the model from your cache/dictionary.**
+- **Call `gc.collect()` after deleting the model.**
+- **Call `torch.cuda.empty_cache()` if using CUDA.**
+- **Do this every time you switch models or want to free memory.**
+
+---
+
+### **Advanced: Unloading All Models**
+
+If you want to ensure all models are unloaded (e.g., when switching tabs):
+
+```python
+def unload_all_models():
+    model_cache.clear()
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+```
+
+---
+
+### **Summary Table**
+
+| Step                | CPU | GPU (CUDA) |
+|---------------------|-----|------------|
+| Delete model object | ✅  | ✅         |
+| `gc.collect()`      | ✅  | ✅         |
+| `torch.cuda.empty_cache()` | ❌  | ✅         |
+
+---