Added image generation to blog writer

backend/services/llm_providers/image_generation/hf_provider.py

@@ -0,0 +1,73 @@
+from __future__ import annotations
+
+import io
+import os
+from typing import Optional, Dict, Any
+
+from PIL import Image
+from huggingface_hub import InferenceClient
+
+from .base import ImageGenerationOptions, ImageGenerationResult, ImageGenerationProvider
+from utils.logger_utils import get_service_logger
+
+
+logger = get_service_logger("image_generation.huggingface")
+
+
+DEFAULT_HF_MODEL = os.getenv(
+    "HF_IMAGE_MODEL",
+    "black-forest-labs/FLUX.1-Krea-dev",
+)
+
+
+class HuggingFaceImageProvider(ImageGenerationProvider):
+    """Hugging Face Inference Providers (fal-ai) backed image generation.
+
+    API doc: https://huggingface.co/docs/inference-providers/en/tasks/text-to-image
+    """
+
+    def __init__(self, api_key: Optional[str] = None, provider: str = "fal-ai") -> None:
+        self.api_key = api_key or os.getenv("HF_TOKEN")
+        if not self.api_key:
+            raise RuntimeError("HF_TOKEN is required for Hugging Face image generation")
+        self.provider = provider
+        self.client = InferenceClient(provider=self.provider, api_key=self.api_key)
+        logger.info("HuggingFaceImageProvider initialized (provider=%s)", self.provider)
+
+    def generate(self, options: ImageGenerationOptions) -> ImageGenerationResult:
+        model = options.model or DEFAULT_HF_MODEL
+        params: Dict[str, Any] = {}
+        if options.guidance_scale is not None:
+            params["guidance_scale"] = options.guidance_scale
+        if options.steps is not None:
+            params["num_inference_steps"] = options.steps
+        if options.negative_prompt:
+            params["negative_prompt"] = options.negative_prompt
+        if options.seed is not None:
+            params["seed"] = options.seed
+
+        # The HF InferenceClient returns a PIL Image
+        logger.debug("HF generate: model=%s width=%s height=%s params=%s", model, options.width, options.height, params)
+        img: Image.Image = self.client.text_to_image(
+            options.prompt,
+            model=model,
+            width=options.width,
+            height=options.height,
+            **params,
+        )
+
+        with io.BytesIO() as buf:
+            img.save(buf, format="PNG")
+            image_bytes = buf.getvalue()
+
+        return ImageGenerationResult(
+            image_bytes=image_bytes,
+            width=img.width,
+            height=img.height,
+            provider="huggingface",
+            model=model,
+            seed=options.seed,
+            metadata={"provider": self.provider},
+        )
+
+