Update app.py

app.py

@@ -4,18 +4,16 @@ from PIL import Image, ImageDraw, ImageFont
 import gradio as gr
 
 # ==============================
-# Secrets / Config
+# Config / Secrets
 # ==============================
-HF_TOKEN = os.getenv("HF_TOKEN")  # optional, via Repo Secrets
-# Try these Inference API models in order; skip silently on 404/403/5xx
+HF_TOKEN = os.getenv("HF_TOKEN")  # optional
+# Try these Inference API model IDs first (will skip on 404/403/5xx)
 INFERENCE_CANDIDATES = [
     "stabilityai/stable-diffusion-2-1",
     "runwayml/stable-diffusion-v1-5",
-    "stabilityai/sd-turbo",  # may 404 for some accounts
 ]
-# Public Space as last resort (no token). If they change UI, we still fail gracefully.
+# Public Space fallback (no token). We'll DISCOVER a valid api_name at runtime.
 PUBLIC_SPACE_ID = "black-forest-labs/FLUX.1-schnell"
-PUBLIC_SPACE_APIS = ["/predict", "/run"]
 
 # ==============================
 # Fonts
@@ -34,10 +32,8 @@ def get_font(size: int):
 # Utils
 # ==============================
 def i(v):
-    try:
-        return int(round(float(v)))
-    except Exception:
-        return int(v)
+    try: return int(round(float(v)))
+    except Exception: return int(v)
 
 def gradient_from_prompt(prompt: str, w=768, h=768) -> Image.Image:
     w, h = i(w), i(h)
@@ -98,8 +94,7 @@ PRESETS = {
 }
 def smart_split_text(prompt: str):
     p = (prompt or "").strip()
-    if not p:
-        return "TOP TEXT", "BOTTOM TEXT"
+    if not p: return "TOP TEXT", "BOTTOM TEXT"
     for sep in ["|", " - ", " — ", ":", ";"]:
         if sep in p:
             a, b = p.split(sep, 1)
@@ -115,52 +110,80 @@ def smart_split_text(prompt: str):
 # ==============================
 def call_inference_api(model_id: str, prompt: str, width: int, height: int) -> Image.Image:
     if not HF_TOKEN:
-        raise RuntimeError("HF token not set")
+        raise RuntimeError("no-token")
     url = f"https://api-inference.huggingface.co/models/{model_id}"
     headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-    payload = {
-        "inputs": prompt,
-        "options": {"wait_for_model": True},
-        "parameters": {"width": int(width), "height": int(height)}
-    }
+    payload = {"inputs": prompt, "options": {"wait_for_model": True},
+               "parameters": {"width": int(width), "height": int(height)}}
     r = requests.post(url, headers=headers, json=payload, timeout=180)
     if r.status_code != 200:
-        raise RuntimeError(f"{r.status_code}:{r.text[:160]}")
+        raise RuntimeError(f"{model_id}:{r.status_code}")
     return Image.open(BytesIO(r.content)).convert("RGB")
 
-def call_public_space(prompt: str, width: int, height: int) -> Image.Image:
-    # no token required
+def call_public_space_dynamic(prompt: str, width: int, height: int) -> Image.Image:
+    # Discover an api_name and feed only fields we can infer.
     from gradio_client import Client
     client = Client(PUBLIC_SPACE_ID)
-    # try multiple API names to survive UI tweaks
+    info = client.view_api(all_endpoints=True)
     last_err = None
-    for api in PUBLIC_SPACE_APIS:
+    for ep in info:
+        api = ep.get("api_name")
+        params = ep.get("parameters", [])
+        # Build arg list by index
+        args = [None] * len(params)
+        bad = False
+        for idx, p in enumerate(params):
+            label = (p.get("label") or "").lower()
+            # Skip endpoints that clearly require an image upload
+            if "image" in label and "prompt" not in label:
+                bad = True
+                break
+            if "prompt" in label or label in ("prompt", "text", "caption"):
+                args[idx] = prompt
+            elif "width" in label:
+                args[idx] = int(width)
+            elif "height" in label:
+                args[idx] = int(height)
+            elif "negative" in label:
+                args[idx] = ""
+            elif "steps" in label or "num_inference_steps" in label:
+                args[idx] = 4
+            elif "guidance" in label or "cfg" in label:
+                args[idx] = 3.5
+            elif "seed" in label:
+                args[idx] = 42
+            else:
+                # leave None; gradio_client will fill defaults
+                pass
+        if bad:  # requires an image input etc.
+            continue
         try:
-            res = client.predict(prompt, width, height, api_name=api)
+            res = client.predict(*args, api_name=api)
             if isinstance(res, list): res = res[0]
             return Image.open(res).convert("RGB")
         except Exception as e:
             last_err = e
             continue
-    raise RuntimeError(f"public-space-fail:{last_err}")
+    raise RuntimeError(f"space-no-endpoint:{last_err}")
 
 def generate_image_auto(prompt: str, width: int, height: int):
-    # 1) try HF Inference API candidates
     tried = []
+    # 1) Inference API candidates (if token present)
     if HF_TOKEN:
         for mid in INFERENCE_CANDIDATES:
             try:
                 img = call_inference_api(mid, prompt, width, height)
                 return img, f"✅ Inference API: **{mid}** (token present)"
             except Exception as e:
-                tried.append(f"{mid}→{str(e)[:80]}")
-    # 2) try public Space (no token)
+                tried.append(f"{mid}→{str(e)}")
+                continue
+    # 2) Public Space dynamic
     try:
-        img = call_public_space(prompt, width, height)
-        return img, f"✅ Public Space: **{PUBLIC_SPACE_ID}**"
+        img = call_public_space_dynamic(prompt, width, height)
+        return img, f"✅ Public Space: **{PUBLIC_SPACE_ID}** (discovered endpoint)"
     except Exception as e:
-        tried.append(f"{PUBLIC_SPACE_ID}→{str(e)[:80]}")
-    # 3) fallback gradient
+        tried.append(f"{PUBLIC_SPACE_ID}→{str(e)}")
+    # 3) Gradient
     return gradient_from_prompt(prompt, w=width, h=height), f"⚠️ Fallback gradient | tried: {', '.join(tried)}"
 
 # ==============================
@@ -184,7 +207,7 @@ def generate_and_meme(
     else:
         img, status = gradient_from_prompt(gen_prompt, w=width, h=height), "ℹ️ AI generator is OFF"
 
-    # text
+    # Text
     if use_prompt_for_text:
         top_text, bottom_text = smart_split_text(base)
     else: