Deploy: hf-inference-proxy — via Anushka AI

README.md

@@ -1,13 +1,15 @@
 ---
-title: Hf Inference Proxy
-emoji: 😻
-colorFrom: red
-colorTo: gray
+title: hf-inference-proxy
+emoji: 🖼️
+colorFrom: indigo
+colorTo: purple
 sdk: gradio
-sdk_version: 6.15.2
-python_version: '3.13'
+sdk_version: 5.31.0
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# HuggingFace Inference Image Generator
+FLUX.1 · SDXL · Realistic Vision · Dreamshaper · Anything v5
+
+Built for Anushka AI — AJ's autonomous assistant.

app.py

@@ -0,0 +1,126 @@
+"""
+HF Inference Proxy — Routes image generation requests to HF Inference API
+Deployed as a HF Space so it runs on HF's own servers (bypasses Oracle firewall)
+The Oracle server calls THIS space, which calls HF Inference internally.
+
+Supports: SDXL, FLUX.1, Realistic Vision, Dreamshaper, Anything v5
+"""
+import os, requests, base64, time
+from io import BytesIO
+from PIL import Image
+import gradio as gr
+
+HF_TOKEN = os.environ.get("HF_TOKEN", "")
+
+MODELS = {
+    "FLUX.1 Schnell (Fast)":         "black-forest-labs/FLUX.1-schnell",
+    "FLUX.1 Dev (Best Quality)":     "black-forest-labs/FLUX.1-dev",
+    "Stable Diffusion XL":           "stabilityai/stable-diffusion-xl-base-1.0",
+    "Realistic Vision v6":           "SG161222/Realistic_Vision_V6.0_B1_noVAE",
+    "Dreamshaper XL":                "Lykon/dreamshaper-xl-lightning",
+    "Anything v5 (Anime)":           "stablediffusionapi/anything-v5",
+}
+
+
+def generate(prompt: str, model_name: str, width: int = 512,
+             height: int = 512, steps: int = 20) -> tuple:
+    if not prompt.strip():
+        return None, "Enter a prompt"
+    if not HF_TOKEN:
+        return None, "HF_TOKEN secret not configured on this Space"
+
+    model_id = MODELS.get(model_name, "black-forest-labs/FLUX.1-schnell")
+    t0 = time.time()
+
+    # FLUX models use different parameter names
+    is_flux = "FLUX" in model_name
+    payload = {"inputs": prompt}
+    if not is_flux:
+        payload["parameters"] = {
+            "num_inference_steps": steps,
+            "width": width,
+            "height": height,
+        }
+
+    try:
+        r = requests.post(
+            f"https://api-inference.huggingface.co/models/{model_id}",
+            headers={"Authorization": f"Bearer {HF_TOKEN}"},
+            json=payload,
+            timeout=120,
+        )
+
+        if r.status_code == 503:
+            # Model loading, wait and retry
+            time.sleep(25)
+            r = requests.post(
+                f"https://api-inference.huggingface.co/models/{model_id}",
+                headers={"Authorization": f"Bearer {HF_TOKEN}"},
+                json=payload,
+                timeout=120,
+            )
+
+        if r.status_code == 200 and "image" in r.headers.get("Content-Type", ""):
+            img = Image.open(BytesIO(r.content))
+            elapsed = time.time() - t0
+            return img, f"✅ {model_name} | {img.size[0]}×{img.size[1]} | {elapsed:.1f}s"
+
+        err = ""
+        try:
+            err = r.json().get("error", r.text[:200])
+        except Exception:
+            err = r.text[:200]
+        return None, f"❌ HTTP {r.status_code}: {err}"
+
+    except Exception as e:
+        return None, f"❌ Error: {str(e)[:150]}"
+
+
+def generate_api(prompt: str, model_name: str = "FLUX.1 Schnell (Fast)",
+                 width: int = 512, height: int = 512) -> dict:
+    """
+    API endpoint — called by Anushka's server via HTTP.
+    Returns base64-encoded image or error.
+    """
+    img, status = generate(prompt, model_name, width, height)
+    if img:
+        buf = BytesIO()
+        img.save(buf, format="PNG")
+        b64 = base64.b64encode(buf.getvalue()).decode()
+        return {"success": True, "image_b64": b64, "status": status}
+    return {"success": False, "error": status}
+
+
+# Gradio UI
+with gr.Blocks(title="HF Inference Proxy", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🖼️ HuggingFace Inference API — Image Generator
+    High quality AI images via FLUX.1, SDXL, Realistic Vision, and more.
+    *Note: First generation may take 30-60s while model loads.*
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", lines=3,
+                               placeholder="Describe your image in detail...")
+            neg = gr.Textbox(label="Negative Prompt", lines=2,
+                            value="ugly, blurry, low quality, watermark",
+                            placeholder="What to avoid...")
+            model = gr.Dropdown(list(MODELS.keys()),
+                               value="FLUX.1 Schnell (Fast)", label="Model")
+            with gr.Row():
+                w = gr.Slider(256, 1024, 512, step=64, label="Width")
+                h = gr.Slider(256, 1024, 768, step=64, label="Height")
+            steps = gr.Slider(10, 50, 20, step=5, label="Steps")
+            btn = gr.Button("🎨 Generate", variant="primary")
+            status = gr.Textbox(label="Status", interactive=False)
+
+        with gr.Column():
+            output = gr.Image(label="Result", type="pil", height=550)
+
+    btn.click(generate, inputs=[prompt, model, w, h, steps],
+              outputs=[output, status])
+    prompt.submit(generate, inputs=[prompt, model, w, h, steps],
+                  outputs=[output, status])
+
+demo.launch()

requirements.txt

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+gradio
+requests
+Pillow