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README.md

@@ -1,13 +1,111 @@
 ---
-title: Text To Image Pro
-emoji: 📈
-colorFrom: red
-colorTo: blue
+title: FLUX.1-schnell Image Generator
+emoji: ⚡
+colorFrom: indigo
+colorTo: purple
 sdk: gradio
-sdk_version: 6.12.0
+sdk_version: "6.0.0"
 app_file: app.py
 pinned: false
-license: mit
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ⚡ FLUX.1-schnell Text-to-Image Generator
+
+A production-ready Gradio application that generates stunning AI images from text prompts using [`black-forest-labs/FLUX.1-schnell`](https://huggingface.co/black-forest-labs/FLUX.1-schnell) — one of the fastest and highest-quality open-source text-to-image models available.
+
+---
+
+## ✨ Features
+
+- 🎨 **9 built-in styles** — Realistic, Spirited Away, Cyberpunk, Oil Painting, Anime, Watercolor, Fantasy Epic, Minimalist, and None
+- ⚙️ **Advanced controls** — Guidance scale, custom width & height (512–1344px)
+- 💡 **Example prompts** — One-click examples to get started instantly
+- 🔄 **Retry logic** — Automatically retries on timeouts or model-loading delays
+- 🛡️ **Robust error handling** — Clear messages for 403 (gated model), 429 (rate limit), 503 (model loading), and timeouts
+
+---
+
+## 🚀 Deployment on Hugging Face Spaces
+
+### 1. Fork or Create a Space
+
+Go to [huggingface.co/new-space](https://huggingface.co/new-space) and:
+- Select **Gradio** as the SDK
+- Choose **CPU Basic** (free tier is sufficient — inference runs on HF servers)
+
+### 2. Upload Files
+
+Add these files to your Space repository:
+```
+app.py
+requirements.txt
+README.md
+```
+
+### 3. Set the Secret Token
+
+> ⚠️ This is required — the app will not work without it.
+
+1. Go to your Space → **Settings** → **Variables and Secrets**
+2. Click **New Secret**
+3. Name: `HF_TOKEN`
+4. Value: Your Hugging Face access token from [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
+
+### 4. Accept the Model License
+
+Visit [black-forest-labs/FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell) and click **Agree and access repository** while logged into the HF account whose token you are using.
+
+### 5. Deploy
+
+Push your files — the Space will build automatically and be live in ~1 minute.
+
+---
+
+## 🗂️ Project Structure
+
+```
+├── app.py              # Main Gradio application
+├── requirements.txt    # Python dependencies
+└── README.md           # This file (also configures the HF Space)
+```
+
+---
+
+## 🔧 Key Functions
+
+| Function | Purpose |
+|---|---|
+| `authenticate_client()` | Loads `HF_TOKEN` from env and returns an `InferenceClient` |
+| `apply_style_to_prompt()` | Merges user prompt with style-specific keywords |
+| `generate_image()` | Calls FLUX API with retry logic and full error handling |
+| `build_interface()` | Constructs the Gradio Blocks UI |
+
+---
+
+## ⚠️ Common Errors & Fixes
+
+| Error | Cause | Fix |
+|---|---|---|
+| `🔑 Authentication Error` | `HF_TOKEN` secret not set | Add it in Space Settings → Secrets |
+| `🚫 Access Denied (403)` | Model license not accepted | Visit the model page and accept the license |
+| `🚦 Rate limit (429)` | HF Inference API quota exceeded | Upgrade HF plan or wait for quota reset |
+| `⏳ Model loading (503)` | Cold start | App retries automatically — wait a moment |
+| `⏱️ Timeout` | Slow network or overloaded API | App retries automatically |
+
+---
+
+## 📦 Dependencies
+
+| Package | Purpose |
+|---|---|
+| `gradio` | Web UI framework |
+| `huggingface_hub` | HF Inference API client |
+| `Pillow` | Image processing |
+
+---
+
+## 📄 License
+
+This Space is released under the **Apache 2.0** license.
+The underlying model (`FLUX.1-schnell`) is licensed under its own terms — see the [model card](https://huggingface.co/black-forest-labs/FLUX.1-schnell) for details.

app.py

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+"""
+FHDR pro Text-to-Image Generator
+Production-ready Gradio application using black-forest-labs/FLUX.1-schnell
+via the Hugging Face Inference API.
+"""
+
+import os
+import io
+import logging
+import time
+from typing import Optional
+
+import gradio as gr
+from huggingface_hub import InferenceClient
+from huggingface_hub.utils import HfHubHTTPError
+from PIL import Image
+
+# ── Logging ────────────────────────────────────────────────────────────────────
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+# ── Constants ──────────────────────────────────────────────────────────────────
+MODEL_ID = "kpsss34/FHDR_Uncensored"
+MAX_RETRIES = 3
+RETRY_DELAY = 5  # seconds between retries
+
+STYLES: dict[str, str] = {
+    "None": "",
+    "Realistic": (
+        "photorealistic, ultra-detailed, 8K resolution, RAW photo, "
+        "sharp focus, natural lighting, Canon EOS R5"
+    ),
+    "Spirited Away": (
+        "Studio Ghibli style, Hayao Miyazaki, hand-drawn animation, "
+        "soft watercolor palette, whimsical, magical, fantastical landscape"
+    ),
+    "Cyberpunk": (
+        "cyberpunk, neon-lit, dystopian cityscape, rain-slicked streets, "
+        "holographic signs, dark atmosphere, Blade Runner aesthetic, 4K"
+    ),
+    "Oil Painting": (
+        "oil painting, impasto technique, rich brushstrokes, museum-quality, "
+        "Renaissance lighting, chiaroscuro, canvas texture"
+    ),
+    "Anime": (
+        "anime style, vibrant colors, cel-shaded, dynamic composition, "
+        "detailed linework, Makoto Shinkai inspired"
+    ),
+    "Watercolor": (
+        "soft watercolor painting, delicate washes, paper texture, "
+        "loose brushwork, pastel tones, dreamy atmosphere"
+    ),
+    "Fantasy Epic": (
+        "epic fantasy, dramatic lighting, highly detailed, "
+        "cinematic composition, concept art, ArtStation trending"
+    ),
+    "Minimalist": (
+        "minimalist, clean lines, flat design, limited color palette, "
+        "geometric shapes, negative space, modern aesthetic"
+    ),
+}
+
+EXAMPLE_PROMPTS = [
+    ["A dragon soaring over a misty mountain range at dawn", "Fantasy Epic", 7.5, 1024, 1024],
+    ["A lone samurai standing in a bamboo forest during rainfall", "Anime", 7.0, 1024, 1024],
+    ["A futuristic Tokyo street market at night", "Cyberpunk", 8.0, 1024, 1024],
+    ["A young girl riding a giant cat bus through the clouds", "Spirited Away", 7.0, 1024, 1024],
+    ["Portrait of an astronaut on Mars, golden hour", "Realistic", 7.5, 1024, 1024],
+    ["A cozy bookshop in autumn rain, warm light inside", "Oil Painting", 7.0, 1024, 1024],
+]
+
+
+# ── Core Functions ─────────────────────────────────────────────────────────────
+
+def authenticate_client() -> InferenceClient:
+    """
+    Load the Hugging Face token from environment variables and
+    return an authenticated InferenceClient.
+
+    Raises:
+        EnvironmentError: If HF_TOKEN is not set.
+    """
+    token = os.environ.get("HF_TOKEN")
+    if not token:
+        raise EnvironmentError(
+            "HF_TOKEN environment variable is not set. "
+            "Please add your Hugging Face token to the Space secrets."
+        )
+    logger.info("HF_TOKEN loaded successfully. Authenticating client...")
+    return InferenceClient(token=token)
+
+
+def apply_style_to_prompt(user_prompt: str, style_name: str) -> str:
+    """
+    Combine the user's prompt with pre-defined style keywords.
+
+    Args:
+        user_prompt: The raw text prompt from the user.
+        style_name:  The name of the style to apply (must be a key in STYLES).
+
+    Returns:
+        A fully composed prompt string ready for the model.
+    """
+    style_keywords = STYLES.get(style_name, "")
+    if style_keywords:
+        composed = f"{user_prompt.strip()}, {style_keywords}"
+    else:
+        composed = user_prompt.strip()
+
+    logger.info("Composed prompt: %s", composed)
+    return composed
+
+
+def generate_image(
+    user_prompt: str,
+    style_name: str,
+    guidance_scale: float,
+    width: int,
+    height: int,
+    progress: gr.Progress = gr.Progress(),
+) -> tuple[Optional[Image.Image], str]:
+    """
+    Generate an image from a text prompt using FHDR-schnell.
+
+    Args:
+        user_prompt:    The raw user prompt.
+        style_name:     Style to blend into the prompt.
+        guidance_scale: How closely the model follows the prompt (1.0–10.0).
+        width:          Output image width in pixels.
+        height:         Output image height in pixels.
+        progress:       Gradio progress tracker.
+
+    Returns:
+        A tuple of (PIL Image or None, status message string).
+    """
+    # ── Input validation ───────────────────────────────────────────────────────
+    if not user_prompt or not user_prompt.strip():
+        return None, "⚠️ Please enter a prompt before generating."
+
+    # ── Authenticate ───────────────────────────────────────────────────────────
+    try:
+        client = authenticate_client()
+    except EnvironmentError as e:
+        logger.error("Authentication failed: %s", e)
+        return None, f"🔑 Authentication Error: {e}"
+
+    # ── Build prompt ───────────────────────────────────────────────────────────
+    final_prompt = apply_style_to_prompt(user_prompt, style_name)
+
+    # ── Generate with retry logic ──────────────────────────────────────────────
+    for attempt in range(1, MAX_RETRIES + 1):
+        try:
+            progress(0.1 * attempt, desc=f"Sending request to FLUX (attempt {attempt}/{MAX_RETRIES})...")
+            logger.info("Generation attempt %d/%d", attempt, MAX_RETRIES)
+
+            image_bytes: bytes = client.text_to_image(
+                prompt=final_prompt,
+                model=MODEL_ID,
+                guidance_scale=guidance_scale,
+                width=width,
+                height=height,
+            )
+
+            progress(0.9, desc="Processing image...")
+            image = Image.open(io.BytesIO(image_bytes)) if isinstance(image_bytes, bytes) else image_bytes
+            progress(1.0, desc="Done!")
+            logger.info("Image generated successfully on attempt %d.", attempt)
+            return image, f"✅ Image generated successfully with style: **{style_name}**"
+
+        except HfHubHTTPError as e:
+            status_code = getattr(e.response, "status_code", None)
+
+            if status_code == 403:
+                msg = (
+                    "🚫 Access Denied (403): The model is gated. "
+                    "Please visit https://huggingface.co/black-forest-labs/FHDR-schnell "
+                    "and accept the license agreement with your HF account."
+                )
+                logger.error(msg)
+                return None, msg
+
+            elif status_code == 503:
+                msg = f"⏳ Model is loading (503). Retrying in {RETRY_DELAY}s... (attempt {attempt}/{MAX_RETRIES})"
+                logger.warning(msg)
+                if attempt < MAX_RETRIES:
+                    time.sleep(RETRY_DELAY)
+                    continue
+                return None, "❌ Model failed to load after multiple retries. Please try again later."
+
+            elif status_code == 429:
+                msg = (
+                    "🚦 Rate limit exceeded (429). "
+                    "You have exceeded your Inference API quota. "
+                    "Please check your HF plan at https://huggingface.co/settings/billing"
+                )
+                logger.error(msg)
+                return None, msg
+
+            else:
+                logger.error("HfHubHTTPError: %s", e)
+                if attempt < MAX_RETRIES:
+                    time.sleep(RETRY_DELAY)
+                    continue
+                return None, f"❌ API Error: {e}"
+
+        except TimeoutError:
+            logger.warning("Request timed out on attempt %d.", attempt)
+            if attempt < MAX_RETRIES:
+                time.sleep(RETRY_DELAY)
+                continue
+            return None, "⏱️ Request timed out after multiple attempts. Please try again."
+
+        except Exception as e:
+            logger.error("Unexpected error: %s", e, exc_info=True)
+            return None, f"❌ Unexpected error: {str(e)}"
+
+    return None, "❌ Generation failed after all retries. Please try again later."
+
+
+# ── Gradio Interface ───────────────────────────────────────────────────────────
+
+def build_interface() -> gr.Blocks:
+    """Construct and return the Gradio Blocks interface."""
+
+    with gr.Blocks(title="FLUX.1 Image Generator") as demo:
+
+        # Header
+        gr.HTML("""
+            <div style="text-align:center; padding: 24px 0 8px;">
+                <h1 style="font-size:2.2rem; font-weight:800; margin:0;">
+                    ⚡ FLUX.1-schnell Image Generator
+                </h1>
+                <p style="color:#6b7280; margin-top:8px; font-size:1rem;">
+                    Generate stunning AI images from text using
+                    <a href="https://huggingface.co/black-forest-labs/FLUX.1-schnell"
+                       target="_blank" style="color:#2563eb;">
+                       black-forest-labs/FLUX.1-schnell
+                    </a>
+                    — one of the fastest open-source text-to-image models.
+                </p>
+            </div>
+        """)
+
+        with gr.Row():
+            # ── Left column: Inputs ────────────────────────────────────────────
+            with gr.Column(scale=1):
+                prompt_input = gr.Textbox(
+                    label="📝 Your Prompt",
+                    placeholder="Describe the image you want to generate...",
+                    lines=4,
+                    max_lines=8,
+                )
+                style_input = gr.Dropdown(
+                    label="🎨 Style",
+                    choices=list(STYLES.keys()),
+                    value="None",
+                    info="Blend a pre-defined aesthetic into your prompt.",
+                )
+                with gr.Accordion("⚙️ Advanced Settings", open=False):
+                    guidance_input = gr.Slider(
+                        label="Guidance Scale",
+                        minimum=1.0,
+                        maximum=10.0,
+                        value=7.5,
+                        step=0.5,
+                        info="Higher = follows prompt more strictly.",
+                    )
+                    with gr.Row():
+                        width_input = gr.Slider(
+                            label="Width (px)",
+                            minimum=512,
+                            maximum=1344,
+                            value=1024,
+                            step=64,
+                        )
+                        height_input = gr.Slider(
+                            label="Height (px)",
+                            minimum=512,
+                            maximum=1344,
+                            value=1024,
+                            step=64,
+                        )
+
+                generate_btn = gr.Button(
+                    "🚀 Generate Image", variant="primary", size="lg"
+                )
+                status_output = gr.Markdown(value="")
+
+            # ── Right column: Output ───────────────────────────────────────────
+            with gr.Column(scale=1):
+                image_output = gr.Image(
+                    label="🖼️ Generated Image",
+                    type="pil",
+                    height=520,
+                )
+
+        # Examples
+        gr.Examples(
+            examples=EXAMPLE_PROMPTS,
+            inputs=[prompt_input, style_input, guidance_input, width_input, height_input],
+            outputs=[image_output, status_output],
+            fn=generate_image,
+            cache_examples=False,
+            label="💡 Example Prompts — click any to try",
+        )
+
+        # Footer
+        gr.HTML("""
+            <div style="text-align:center; color:#9ca3af; font-size:0.8rem; margin-top:20px; border-top:1px solid #e5e7eb; padding-top:12px;">
+                Built with 🤗 Gradio &amp; Hugging Face Inference API ·
+                Model: <code>black-forest-labs/FLUX.1-schnell</code>
+            </div>
+        """)
+
+        # Wire up
+        generate_btn.click(
+            fn=generate_image,
+            inputs=[prompt_input, style_input, guidance_input, width_input, height_input],
+            outputs=[image_output, status_output],
+        )
+
+    return demo
+
+
+# ── Entry Point ────────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    demo = build_interface()
+    demo.launch()

requirements.txt

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+gradio>=6.0.0
+huggingface_hub>=0.24.0
+Pillow>=10.0.0