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import os
from huggingface_hub import InferenceClient
from PIL import Image
import gradio as gr
from typing import Union
# Load environment variables (useful for local testing)
from dotenv import load_dotenv
load_dotenv()
# --- Model Configuration ---
MODEL_ID = "tencent/HunyuanImage-3.0"
PROVIDER = "fal-ai"
BILL_TO = "huggingface"
# Initialize client
HF_TOKEN = os.environ.get("HF_TOKEN")
CLIENT: Union[InferenceClient, None] = None
if HF_TOKEN:
try:
# Note: FAL AI provider uses HF_TOKEN as its api_key
CLIENT = InferenceClient(
provider=PROVIDER,
api_key=HF_TOKEN,
bill_to=BILL_TO,
)
print(f"✅ InferenceClient initialized for {MODEL_ID} via {PROVIDER}")
except Exception as e:
print(f"❌ Error initializing InferenceClient: {e}")
CLIENT = None
else:
print("⚠️ HF_TOKEN environment variable not set. Client will be unavailable.")
def generate_image(prompt: str) -> Image.Image:
"""
Generates an image from a text prompt using the Hugging Face Inference Client.
"""
if not CLIENT:
raise gr.Error("API client not available. Please ensure HF_TOKEN is set correctly.")
if not prompt:
raise gr.Error("Please provide a prompt.")
print(f"Generating image for prompt: '{prompt[:50]}...'")
try:
# The output is a PIL.Image object directly
image = CLIENT.text_to_image(
prompt,
model=MODEL_ID,
)
return image
except Exception as e:
print(f"Error during image generation: {e}")
if "Authentication failed" in str(e):
raise gr.Error("Authentication failed. Check your HF_TOKEN.")
if "limit reached" in str(e) or "quota" in str(e):
raise gr.Error("Rate limit or quota reached for this API endpoint.")
raise gr.Error(f"Generation failed: {str(e)}") |