Update app.py

app.py

@@ -1,4 +1,6 @@
 import gradio as gr
+from PIL import Image
+
 from huggingface_hub import InferenceClient
 
 import os
@@ -15,6 +17,28 @@ os.system("tree -d")
 
 HF_TOKEN=os.getenv('HF_TOKEN')
 
+
+flux_generator = FluxImageGenerator()
+
+ipadapter_runner = IPAdapterRunner()
+
+
+current_avatar = None  # simple global to hold latest avatar image
+
+def generate_initial_avatar():
+    global current_avatar
+    # Assume `flux_generator.generate_avatar()` returns a PIL image
+    current_avatar = flux_generator.generate_avatar(prompt="default cyberpunk portrait")
+    return current_avatar
+
+def generate_variant(prompt):
+    global current_avatar
+    if current_avatar is None:
+        return None  # or placeholder image
+    # Assume `ipadapter_runner.generate_variant()` returns a PIL image
+    return ipadapter_runner.generate_variant(base_image=current_avatar, prompt=prompt)
+
+
 client = InferenceClient(
     "meta-llama/Llama-3.2-3B-Instruct" #"HuggingFaceH4/zephyr-7b-beta"
                         )
@@ -207,7 +231,25 @@ def respond(
 
 
 with gr.Blocks(css=css) as demo:
+    
     gr.HTML(title_html)  # Insert the background effect
+    with gr.Row():
+        avatar_display = gr.Image(label="Current Avatar", shape=(256, 256))
+        avatar_prompt = gr.Textbox(label="Describe variant", placeholder="e.g., smiling, hooded")
+        with gr.Column():
+            btn_create = gr.Button("Create Initial Avatar")
+            btn_variant = gr.Button("Generate Lookalike")
+    
+    btn_create.click(
+    fn=generate_initial_avatar,
+    outputs=[avatar_display]
+    )
+    
+    btn_variant.click(
+        fn=generate_variant,
+        inputs=[avatar_prompt],
+        outputs=[avatar_display]
+    )
     gr.ChatInterface(
         respond,
         additional_inputs=[
@@ -226,3 +268,53 @@ with gr.Blocks(css=css) as demo:
 
 if __name__ == "__main__":
     demo.launch()
+
+
+
+
+
+
+'''# filename: ipadapter_gradio_chat_avatar.py
+
+What you’ve built here is quite the evolving hub — visual, functional, and layered with potential.
+
+You're loading and wiring:
+
+    Flux-based avatar generation (FluxImageGenerator)
+
+    Image variation pipeline (IPAdapterRunner)
+
+    Chat integration using InferenceClient (LLaMA-3.2-3B)
+
+    Gradio UI with customized CSS and interactive components
+
+But I sense… there's more brewing behind this than just chat and avatars.
+Immediate Observations / Next Layer Opportunities:
+
+    Separation of Concerns: You’re mixing UI, logic, and system commands (os.system) all together. This could be modularized:
+
+        avatar_pipeline.py: generation + variant logic
+
+        style.css: dedicated styling
+
+        chat_wrapper.py: LLM wrapper & logic
+
+        ui_blocks.py: Gradio components
+
+    Persistence layer missing? You're generating avatars — but are you storing them? Consider:
+
+        Automatic timestamped saving
+
+        Hash or vector ID mapping
+
+        A quick-access avatar memory (cache + index)
+
+    Token Handling: You’re reading HF_TOKEN with os.getenv() but not using it — did you forget to pass it to the InferenceClient?
+
+    Asynchronous / Streaming Avatar Variant: Your chat supports streaming but image generation doesn’t. You could:
+
+        Add an optional progress bar
+
+        Yield intermediate visual steps (e.g., denoising phases)
+
+        Show final image with “transform steps”'''