Update app.py from anycoder

app.py

@@ -1,153 +1,264 @@
 import gradio as gr
-import torch
+import requests
+from PIL import Image
+from io import BytesIO
 import random
-from utils import load_pipeline
 
-# Configuration
-# NOTE: If "Z-Image Turbo" is a specific custom model, replace the ID below.
-# Common fast models: "stabilityai/sdxl-turbo", "stabilityai/sd-turbo"
-MODEL_ID = "stabilityai/sdxl-turbo"
+# --- Configuration ---
+# Using SDXL Turbo as the engine for high-speed generation
+MODEL_API_URL = "https://api-inference.huggingface.co/models/stabilityai/sdxl-turbo"
 
-# Load the pipeline once at startup to save memory and time
-print(f"Loading model: {MODEL_ID}...")
-pipe = load_pipeline(MODEL_ID)
-print("Model loaded successfully!")
+# --- Helper Functions ---
+
+def query_huggingface_api(payload, token):
+    """
+    Sends a request to the Hugging Face Inference API.
+    """
+    headers = {"Authorization": f"Bearer {token}"}
+    response = requests.post(MODEL_API_URL, headers=headers, json=payload)
+    
+    if response.status_code != 200:
+        error_data = {}
+        try:
+            error_data = response.json()
+        except:
+            pass
+        
+        if response.status_code == 503:
+            raise gr.Error("Model is warming up. Please try again in a moment.")
+        elif response.status_code == 401:
+            raise gr.Error("Invalid Access Token. Please check your credentials.")
+        elif response.status_code == 429:
+            raise gr.Error("Rate limit exceeded. Please wait a moment.")
+        else:
+            error_msg = error_data.get("error", f"Error {response.status_code}")
+            raise gr.Error(f"API Error: {error_msg}")
+            
+    return response.content
 
 def generate_image(
     prompt: str,
-    negative_prompt: str,
-    seed: int,
-    num_inference_steps: int
+    token: str,
+    negative_prompt: str = "",
+    steps: int = 2,
+    guidance_scale: float = 0.0,
+    progress=gr.Progress()
 ):
     """
-    Generates an image based on the text prompt using the loaded pipeline.
+    Main generation function.
     """
+    if not token or not token.startswith("hf_"):
+        raise gr.Error("Please enter a valid Hugging Face Access Token (starts with 'hf_').")
+    
     if not prompt:
         raise gr.Error("Please enter a text prompt.")
+
+    progress(0.1, desc="Initializing...")
     
-    # Set seed for reproducibility
-    generator = torch.Generator(device=pipe.device).manual_seed(seed)
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "negative_prompt": negative_prompt,
+            "num_inference_steps": int(steps),
+            "guidance_scale": float(guidance_scale)
+        }
+    }
     
+    progress(0.5, desc="Generating...")
     try:
-        # SDXL Turbo works best with 1-4 steps and guidance_scale=0.0
-        # Adjust these if using a different model
-        result = pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            num_inference_steps=num_inference_steps,
-            generator=generator,
-            guidance_scale=0.0
-        )
-        
-        # Return the first image
-        return result.images[0]
-        
+        image_bytes = query_huggingface_api(payload, token)
+        image = Image.open(BytesIO(image_bytes))
+        progress(1.0, desc="Done!")
+        return image
     except Exception as e:
-        raise gr.Error(f"Error during generation: {str(e)}")
+        if isinstance(e, gr.Error):
+            raise e
+        raise gr.Error(f"An unexpected error occurred: {str(e)}")
 
-def set_random_seed():
-    """Helper to generate a random seed for the UI."""
-    return random.randint(0, 2147483647)
+def get_random_prompt():
+    """Returns a random creative prompt."""
+    prompts = [
+        "A cinematic shot of a futuristic astronaut standing on Mars, golden hour lighting, hyperrealistic, 8k",
+        "A cute robot painting a canvas in a garden, watercolor style, soft colors, highly detailed",
+        "Cyberpunk street food vendor in Tokyo, neon rain reflections, volumetric lighting",
+        "A majestic lion made of clouds in a blue sky, fantasy art, dreamlike atmosphere",
+        "Portrait of a warrior with intricate golden armor, digital painting, dramatic lighting",
+        "A cozy library inside a giant tree, magical atmosphere, fireflies, warm lighting",
+        "Futuristic sports car concept, sleek design, neon trim, motion blur, dark city background"
+    ]
+    return random.choice(prompts)
+
+# --- Gradio 6 Application ---
+
+# Custom CSS to mimic the dark/tech vibe of the original HTML
+custom_css = """
+body {
+    background-color: #0f172a;
+    color: #f8fafc;
+}
+.gradio-container {
+    background: linear-gradient(135deg, #0f172a 0%, #1e293b 100%);
+    max-width: 1400px !important;
+}
+#header-text {
+    font-family: 'Courier New', monospace;
+    font-weight: bold;
+    color: #6366f1;
+    text-transform: uppercase;
+    letter-spacing: 2px;
+}
+"""
 
-# Gradio 6 Application
-with gr.Blocks() as demo:
+# Define the Theme
+custom_theme = gr.themes.Soft(
+    primary_hue="indigo",
+    secondary_hue="pink",
+    neutral_hue="slate",
+    font=gr.themes.GoogleFont("Outfit"),
+    text_size="lg",
+    spacing_size="lg",
+    radius_size="md"
+).set(
+    button_primary_background_fill="linear-gradient(90deg, #6366f1 0%, #ec4899 100%)",
+    button_primary_background_fill_hover="linear-gradient(90deg, #4f46e5 0%, #db2777 100%)",
+    button_primary_text_color="white",
+    block_background_fill="*neutral_950",
+    block_border_color="*neutral_800",
+    block_shadow="0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06)",
+    block_title_text_weight="600",
+    block_title_text_color="*primary_300",
+)
+
+with gr.Blocks(theme=custom_theme, css=custom_css) as demo:
     
-    # Header Section
+    # Header
     gr.HTML("""
-        <div style="text-align: center; margin-bottom: 20px;">
-            <h1>🖼️ Z-Image Turbo Generator</h1>
-            <p style="color: #666;">Generate high-quality images from text in seconds using fast diffusion models.</p>
-            <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="text-decoration: none; color: #007bff; font-weight: bold;">
-                Built with anycoder
+        <div style="display: flex; justify-content: space-between; align-items: center; padding: 20px; border-bottom: 1px solid rgba(255,255,255,0.1);">
+            <div id="header-text" style="font-size: 1.5rem;">
+                <i class="fa-solid fa-bolt"></i> Z-Image Turbo
+            </div>
+            <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color: #94a3b8; text-decoration: none; font-size: 0.9rem; border: 1px solid rgba(255,255,255,0.1); padding: 8px 16px; border-radius: 20px; transition: all 0.3s;">
+                Built with anycoder <i class="fa-solid fa-arrow-up-right-from-square" style="font-size: 0.7em;"></i>
             </a>
         </div>
     """)
     
+    gr.Markdown("Generate high-quality images in seconds using the SDXL Turbo engine.", elem_classes="text-center")
+    
     with gr.Row():
-        # Input Column (Controls)
+        # --- Left Column: Controls ---
         with gr.Column(scale=1):
-            gr.Markdown("### ⚙️ Settings")
-            
-            prompt_input = gr.Textbox(
-                label="Prompt",
-                placeholder="Describe the image you want to generate...",
-                lines=3,
-                interactive=True
-            )
-            
-            negative_prompt_input = gr.Textbox(
-                label="Negative Prompt",
-                placeholder="Things to avoid in the image...",
-                lines=2,
-                value="blurry, bad quality, distorted",
-                interactive=True
-            )
-            
-            with gr.Accordion("Advanced Options", open=False):
-                seed_input = gr.Number(
-                    label="Seed",
-                    value=42,
-                    precision=0,
-                    info="Set to -1 for random seed"
+            with gr.Group():
+                gr.Markdown("### 🛠️ Configuration")
+                
+                # Authentication
+                token_input = gr.Textbox(
+                    label="Hugging Face Access Token",
+                    placeholder="hf_...",
+                    type="password",
+                    info="Your token is required to access the API. It is not stored on the server.",
+                    scale=9
                 )
                 
-                random_seed_btn = gr.Button("🎲 Randomize Seed", size="sm")
+                # Prompts
+                prompt_input = gr.Textbox(
+                    label="Prompt",
+                    placeholder="Describe the image you want to generate...",
+                    lines=3,
+                    info="Be descriptive for better results."
+                )
                 
-                steps_input = gr.Slider(
-                    label="Inference Steps",
-                    minimum=1,
-                    maximum=10,
-                    step=1,
-                    value=2,
-                    info="Lower is faster (Turbo models work best with 1-4)"
+                with gr.Row():
+                    random_btn = gr.Button("🎲 Surprise Me", size="sm", scale=1)
+                    clear_btn = gr.Button("🗑️ Clear", size="sm", scale=1)
+                
+                negative_prompt_input = gr.Textbox(
+                    label="Negative Prompt",
+                    placeholder="blur, low quality, distortion...",
+                    lines=1,
+                    info="Elements to avoid in the image."
                 )
-            
-            generate_btn = gr.Button("✨ Generate Image", variant="primary", size="lg")
-            
-            gr.Examples(
-                examples=[
-                    ["A futuristic cyberpunk city with neon lights, highly detailed, 8k", "", 12345, 2],
-                    ["A cute robot painting a canvas in a garden, soft lighting", "", 999, 2],
-                    ["An astronaut riding a horse on Mars, photorealistic", "", 42, 2],
-                ],
-                inputs=[prompt_input, negative_prompt_input, seed_input, steps_input],
-            )
+                
+                # Parameters
+                with gr.Accordion("Advanced Settings", open=False):
+                    steps_slider = gr.Slider(
+                        minimum=1,
+                        maximum=4,
+                        step=1,
+                        value=2,
+                        label="Inference Steps",
+                        info="Turbo models work best with 1-4 steps."
+                    )
+                    
+                    guidance_slider = gr.Slider(
+                        minimum=0.0,
+                        maximum=2.0,
+                        step=0.1,
+                        value=0.0,
+                        label="Guidance Scale",
+                        info="Lower values (0.0-1.0) often work best for Turbo."
+                    )
 
-        # Output Column (Result)
-        with gr.Column(scale=1):
-            gr.Markdown("### 🖼️ Result")
-            image_output = gr.Image(
-                label="Generated Image",
-                type="pil",
-                show_download_button=True,
-                show_share_button=True
+            generate_btn = gr.Button(
+                "✨ Generate Image",
+                variant="primary",
+                size="lg"
             )
 
-    # Event Listeners
-    random_seed_btn.click(
-        fn=set_random_seed,
-        outputs=seed_input
+        # --- Right Column: Output ---
+        with gr.Column(scale=2):
+            with gr.Group():
+                gr.Markdown("### 🖼️ Result")
+                
+                output_image = gr.Image(
+                    label="Generated Image",
+                    type="pil",
+                    height=600,
+                    show_label=False,
+                    show_download_button=True,
+                    interactive=False
+                )
+                
+                # Status/Info
+                status_text = gr.Markdown("", visible=False)
+
+    # --- Event Listeners ---
+    
+    # Random Prompt Logic
+    random_btn.click(
+        fn=get_random_prompt,
+        outputs=prompt_input
     )
     
+    # Clear Logic
+    clear_btn.click(
+        fn=lambda: ("", "", None),
+        outputs=[prompt_input, negative_prompt_input, output_image]
+    )
+
+    # Generate Logic
     generate_btn.click(
         fn=generate_image,
-        inputs=[prompt_input, negative_prompt_input, seed_input, steps_input],
-        outputs=image_output,
+        inputs=[
+            prompt_input,
+            token_input,
+            negative_prompt_input,
+            steps_slider,
+            guidance_slider
+        ],
+        outputs=output_image,
+        api_name="generate",
         api_visibility="public"
     )
 
-# Gradio 6 Launch - All app parameters go here!
+# --- Footer Links ---
+footer_links = [
+    {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"},
+    {"label": "Gradio", "url": "https://gradio.app"},
+    {"label": "Hugging Face", "url": "https://huggingface.co"}
+]
+
+# --- Launch ---
 demo.launch(
-    theme=gr.themes.Soft(
-        primary_hue="indigo",
-        secondary_hue="purple",
-        neutral_hue="slate",
-        font=gr.themes.GoogleFont("Inter"),
-        text_size="lg",
-        spacing_size="lg",
-        radius_size="md"
-    ),
-    footer_links=[
-        {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}
-    ]
+    footer_links=footer_links
 )