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Image_Generator_for_HTML_Games

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LeafCat79 commited on 25 days ago

Commit

e721410

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1 Parent(s): a3281e2

Use HF Inference Providers

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Files changed (1) hide show

app.py +35 -41

app.py CHANGED Viewed

@@ -17,6 +17,11 @@ from urllib.request import Request, urlopen
 import gradio as gr
 from PIL import Image, ImageDraw, ImageEnhance, ImageFilter, ImageFont
 STARTER_HTML = """<!DOCTYPE html>
 <html>
@@ -99,9 +104,8 @@ class StylePlan:
 HF_TOKEN = os.environ.get("HF_TOKEN", "")
 HF_IMAGE_MODEL = os.environ.get("HF_IMAGE_MODEL", "black-forest-labs/FLUX.1-schnell")
-HF_IMAGE_ENDPOINT = f"https://api-inference.huggingface.co/models/{HF_IMAGE_MODEL}"
-HF_PROMPT_MODEL = os.environ.get("HF_PROMPT_MODEL", "Qwen/Qwen2.5-Coder-7B-Instruct")
-HF_PROMPT_ENDPOINT = f"https://api-inference.huggingface.co/models/{HF_PROMPT_MODEL}"
 def slugify(value: str) -> str:
@@ -277,16 +281,16 @@ def hf_prompt_json(html_code: str, role_lines: list[tuple[str, str]], style_hint
         f"Asset roles:\n{role_block}\n\n"
         "Return JSON only."
     )
-    prompt = f"<|im_start|>system\n{instruction}<|im_end|>\n<|im_start|>user\n{user_text}<|im_end|>\n<|im_start|>assistant\n"
     payload = {
-        "inputs": prompt,
-        "parameters": {
-            "max_new_tokens": 900,
-            "temperature": 0.55,
-            "top_p": 0.9,
-            "return_full_text": False,
-        },
-        "options": {"wait_for_model": True},
     }
     request = Request(
         HF_PROMPT_ENDPOINT,
@@ -301,7 +305,10 @@ def hf_prompt_json(html_code: str, role_lines: list[tuple[str, str]], style_hint
         with urlopen(request, timeout=90) as response:
             raw = response.read().decode("utf-8", errors="replace")
         parsed = json.loads(raw)
-        if isinstance(parsed, list) and parsed:
             text = parsed[0].get("generated_text", "") if isinstance(parsed[0], dict) else str(parsed[0])
         elif isinstance(parsed, dict):
             text = parsed.get("generated_text", parsed.get("text", ""))
@@ -974,36 +981,23 @@ def placeholder_png_bytes(role: str, width: int, height: int) -> bytes:
 def hf_image_png(spec: AssetSpec, index: int, run_id: int) -> tuple[bytes | None, str | None]:
     if not HF_TOKEN:
         return None, "HF_TOKEN is not visible to the Space runtime"
-    payload = {
-        "inputs": spec.prompt,
-        "parameters": {
-            "width": spec.width,
-            "height": spec.height,
-            "num_inference_steps": 4,
-            "guidance_scale": 0.0,
-            "seed": abs(hash(f"{spec.role}|{spec.prompt}|{index}|{run_id}")) % 2147483647,
-        },
-        "options": {"wait_for_model": True},
-    }
-    request = Request(
-        HF_IMAGE_ENDPOINT,
-        data=json.dumps(payload).encode("utf-8"),
-        headers={
-            "Authorization": f"Bearer {HF_TOKEN}",
-            "Content-Type": "application/json",
-            "Accept": "image/png",
-        },
-        method="POST",
-    )
     try:
-        with urlopen(request, timeout=90) as response:
-            content_type = response.headers.get("content-type", "")
-            content = response.read()
-        if "image" not in content_type:
-            preview = content.decode("utf-8", errors="replace")[:500]
-            return None, f"{HF_IMAGE_MODEL} returned {content_type or 'non-image response'}: {preview}"
-        return image_to_png_bytes(content, spec.width, spec.height), None
     except Exception as exc:
         return None, short_error(exc)

 import gradio as gr
 from PIL import Image, ImageDraw, ImageEnhance, ImageFilter, ImageFont
+try:
+    from huggingface_hub import InferenceClient
+except Exception:
+    InferenceClient = None
 STARTER_HTML = """<!DOCTYPE html>
 <html>
 HF_TOKEN = os.environ.get("HF_TOKEN", "")
 HF_IMAGE_MODEL = os.environ.get("HF_IMAGE_MODEL", "black-forest-labs/FLUX.1-schnell")
+HF_PROMPT_MODEL = os.environ.get("HF_PROMPT_MODEL", "Qwen/Qwen2.5-Coder-7B-Instruct:fastest")
+HF_PROMPT_ENDPOINT = os.environ.get("HF_PROMPT_ENDPOINT", "https://router.huggingface.co/v1/chat/completions")
 def slugify(value: str) -> str:
         f"Asset roles:\n{role_block}\n\n"
         "Return JSON only."
     )
     payload = {
+        "model": HF_PROMPT_MODEL,
+        "messages": [
+            {"role": "system", "content": instruction},
+            {"role": "user", "content": user_text},
+        ],
+        "max_tokens": 900,
+        "temperature": 0.55,
+        "top_p": 0.9,
+        "stream": False,
     }
     request = Request(
         HF_PROMPT_ENDPOINT,
         with urlopen(request, timeout=90) as response:
             raw = response.read().decode("utf-8", errors="replace")
         parsed = json.loads(raw)
+        if isinstance(parsed, dict) and parsed.get("choices"):
+            message = parsed["choices"][0].get("message", {})
+            text = message.get("content", "") if isinstance(message, dict) else str(message)
+        elif isinstance(parsed, list) and parsed:
             text = parsed[0].get("generated_text", "") if isinstance(parsed[0], dict) else str(parsed[0])
         elif isinstance(parsed, dict):
             text = parsed.get("generated_text", parsed.get("text", ""))
 def hf_image_png(spec: AssetSpec, index: int, run_id: int) -> tuple[bytes | None, str | None]:
     if not HF_TOKEN:
         return None, "HF_TOKEN is not visible to the Space runtime"
+    if InferenceClient is None:
+        return None, "huggingface_hub is not installed in this Space"
     try:
+        client = InferenceClient(api_key=HF_TOKEN)
+        image = client.text_to_image(
+            prompt=spec.prompt,
+            model=HF_IMAGE_MODEL,
+            width=spec.width,
+            height=spec.height,
+            num_inference_steps=4,
+            guidance_scale=0.0,
+            seed=abs(hash(f"{spec.role}|{spec.prompt}|{index}|{run_id}")) % 2147483647,
+        )
+        out = io.BytesIO()
+        image.save(out, format="PNG")
+        return image_to_png_bytes(out.getvalue(), spec.width, spec.height), None
     except Exception as exc:
         return None, short_error(exc)