Update app.py from anycoder

app.py

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+import gradio as gr
+import torch
+import random
+import time
+from datetime import datetime
+from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
+import json
+
+# --- Configuration & Model Loading ---
+# Replicating the logic of your ComfyUI JSON:
+# - CheckpointLoaderSimple -> runwayml/stable-diffusion-v1-5
+# - KSampler -> EulerAncestralDiscreteScheduler
+# - EmptyLatentImage -> 512x512
+
+MODEL_ID = "runwayml/stable-diffusion-v1-5"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load the pipeline (simulating the backend compute)
+print(f"Loading model {MODEL_ID} on {DEVICE}...")
+try:
+    pipe = StableDiffusionPipeline.from_pretrained(
+        MODEL_ID, 
+        torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
+        safety_checker=None # Disable for speed/demo purposes
+    )
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    if DEVICE == "cuda":
+        pipe.to(DEVICE)
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    pipe = None
+
+# --- Core Logic Functions ---
+
+def generate_image_with_memory(
+    prompt, 
+    negative_prompt, 
+    steps, 
+    cfg, 
+    seed, 
+    memory_db
+):
+    """
+    Generates an image and updates the 'Long Term Memory' database.
+    """
+    if pipe is None:
+        return None, memory_db, "Error: Model not loaded. Check GPU availability."
+
+    start_time = time.time()
+    
+    # Handle Seed (0 = random)
+    if seed == 0:
+        seed = random.randint(0, 2147483647)
+    
+    generator = torch.Generator(device=DEVICE).manual_seed(seed)
+    
+    # Generate Image (Replicating KSampler + VAE Decode)
+    try:
+        result = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            num_inference_steps=int(steps),
+            guidance_scale=float(cfg),
+            height=512,
+            width=512,
+            generator=generator
+        )
+        image = result.images[0]
+    except Exception as e:
+        return None, memory_db, f"Generation Failed: {str(e)}"
+
+    # Update Memory Database
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    new_entry = {
+        "timestamp": timestamp,
+        "prompt": prompt,
+        "negative": negative_prompt,
+        "seed": seed,
+        "steps": steps,
+        "cfg": cfg
+    }
+    
+    # Prepend new entry to memory
+    updated_memory = [new_entry] + memory_db
+    
+    processing_time = f"{time.time() - start_time:.2f}s"
+    
+    return image, updated_memory, f"Generated in {processing_time} | Seed: {seed}"
+
+def recall_prompt(memory_db, evt: gr.SelectData):
+    """
+    Recalls a specific prompt from history based on user selection.
+    """
+    if memory_db is None or len(memory_db) == 0:
+        return "", "", 0, 20, 7.0
+    
+    index = evt.index[0]
+    selected = memory_db[index]
+    
+    return (
+        selected["prompt"], 
+        selected["negative"], 
+        int(selected["seed"]), 
+        int(selected["steps"]), 
+        float(selected["cfg"])
+    )
+
+def export_memory(memory_db):
+    """
+    Exports the memory database to a downloadable JSON file.
+    """
+    if not memory_db:
+        return None
+    json_str = json.dumps(memory_db, indent=4)
+    return json_str
+
+# --- Gradio 6 UI Construction ---
+
+with gr.Blocks() as demo:
+    
+    # Header
+    gr.HTML("""
+    <div style="text-align: center; margin-bottom: 20px;">
+        <h1>🧠 AI-Native Memory Core</h1>
+        <p>Automated Long Term Super Intelligent Human Recall | Persistent Database</p>
+        <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color: #007bff; text-decoration: none;">Built with anycoder</a>
+    </div>
+    """)
+    
+    with gr.Row():
+        # --- Sidebar: Controls & Memory Database ---
+        with gr.Sidebar(width=350):
+            gr.Markdown("### ⚙️ Generation Parameters")
+            
+            with gr.Accordion("Advanced Settings", open=True):
+                steps = gr.Slider(minimum=1, maximum=150, value=20, step=1, label="Sampling Steps", info="Higher = more detail")
+                cfg = gr.Slider(minimum=1, maximum=30, value=7.0, step=0.5, label="CFG Scale", info="Prompt adherence")
+                seed = gr.Number(value=0, label="Seed (0 = Random)", precision=0)
+            
+            gr.Markdown("---")
+            gr.Markdown("### 💾 Persistent Memory Database")
+            gr.Markdown("Click a row to recall prompt settings.")
+            
+            memory_display = gr.Dataframe(
+                label="Prompt History",
+                headers=["Time", "Prompt", "Seed"],
+                datatype=["str", "str", "number"],
+                interactive=False,
+                wrap=True,
+                height=300
+            )
+            
+            with gr.Row():
+                export_btn = gr.Button("💾 Export Memory (JSON)", size="sm")
+                download_file = gr.File(visible=False, label="Download Database")
+
+            gr.Markdown("---")
+            gr.Markdown("### 📱 App Access")
+            # Simulating the APK request via PWA/Install instructions
+            gr.Markdown("""
+            <small>
+            To install on Android:<br>
+            1. Open this in Chrome<br>
+            2. Tap Menu (⋮)<br>
+            3. Tap "Add to Home Screen"
+            </small>
+            """)
+            app_link_btn = gr.Button("📲 Open App View", link="/?view=standalone", variant="primary")
+
+        # --- Main Area: Chat & Generation ---
+        with gr.Column(scale=2):
+            with gr.Group():
+                prompt_input = gr.Textbox(
+                    label="Positive Prompt", 
+                    placeholder="Describe your vision...", 
+                    lines=3,
+                    show_copy_button=True
+                )
+                negative_input = gr.Textbox(
+                    label="Negative Prompt", 
+                    value="low quality, blurry, distorted, bad anatomy",
+                    lines=2,
+                    visible=False # Collapsed for clean UI initially
+                )
+                
+                with gr.Row():
+                    toggle_neg = gr.Button("Show/Hide Negative", size="sm")
+                    generate_btn = gr.Button("✨ Generate & Remember", variant="primary", scale=2)
+            
+            # Output Area
+            with gr.Row():
+                with gr.Column():
+                    result_image = gr.Image(label="Generated Artifact", type="pil", height=512)
+                    status_text = gr.Textbox(label="System Status", interactive=False)
+
+    # --- State Management ---
+    # We use Gradio State to hold the "Memory Database" in the session
+    memory_state = gr.State(value=[])
+
+    # --- Event Listeners ---
+
+    # 1. Toggle Negative Prompt
+    toggle_neg.click(lambda: gr.Textbox(visible=True), None, negative_input)
+
+    # 2. Generate Image & Update Memory
+    generate_btn.click(
+        fn=generate_image_with_memory,
+        inputs=[prompt_input, negative_input, steps, cfg, seed, memory_state],
+        outputs=[result_image, memory_state, status_text],
+        api_visibility="public"
+    )
+
+    # 3. Update Memory Table Display whenever state changes
+    # We format the list of dicts into a dataframe-friendly format
+    def format_memory_for_display(db):
+        if not db:
+            return []
+        return [[row["timestamp"], row["prompt"][:50] + "...", row["seed"]] for row in db]
+
+    # We use a dummy trigger or just chain it. 
+    # In Gradio 6, we can use the `change` event on the state itself or chain outputs.
+    # Here we chain the output of generate to update the view directly.
+    # Note: To keep it simple, we modify the generate function to return the formatted dataframe if needed,
+    # or we can use a separate update trigger. Let's do it inline for efficiency.
+    
+    # Redefining generate to return formatted table for immediate display
+    def generate_with_ui_update(prompt, neg, steps, cfg, seed, db):
+        img, new_db, status = generate_image_with_memory(prompt, neg, steps, cfg, seed, db)
+        formatted_table = format_memory_for_display(new_db)
+        return img, new_db, status, formatted_table
+
+    generate_btn.click(
+        fn=generate_with_ui_update,
+        inputs=[prompt_input, negative_input, steps, cfg, seed, memory_state],
+        outputs=[result_image, memory_state, status_text, memory_display],
+        api_visibility="public"
+    )
+
+    # 4. Recall from Memory (Click on Table)
+    memory_display.select(
+        fn=recall_prompt,
+        inputs=[memory_state],
+        outputs=[prompt_input, negative_input, seed, steps, cfg],
+        api_visibility="private"
+    )
+
+    # 5. Export Memory
+    export_btn.click(
+        fn=export_memory,
+        inputs=[memory_state],
+        outputs=[download_file],
+        api_visibility="private"
+    )
+
+# --- Launch Configuration ---
+# Gradio 6 Syntax: Theme and parameters go in launch(), not Blocks()
+
+demo.launch(
+    theme=gr.themes.Soft(
+        primary_hue="indigo",
+        secondary_hue="blue",
+        neutral_hue="slate",
+        font=gr.themes.GoogleFont("Inter"),
+        text_size="lg",
+        spacing_size="lg",
+        radius_size="md"
+    ).set(
+        button_primary_background_fill="*primary_600",
+        button_primary_background_fill_hover="*primary_700",
+        block_title_text_weight="600",
+        block_border_width="1px",
+        block_border_color="*neutral_200"
+    ),
+    footer_links=[
+        {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"},
+        {"label": "Hugging Face", "url": "https://huggingface.co"}
+    ],
+    share=False # Set to True if you want a public link immediately
+)