Deploy Gradio app with multiple files

app.py

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+import gradio as gr
+from models import generate_image, MODEL_ID
+
+def create_ui():
+    with gr.Blocks(title=f"Tencent HunyuanImage-3.0 Demo") as demo:
+        gr.HTML(
+            f"<div style='text-align: center; max-width: 700px; margin: 0 auto;'>"
+            f"<h1>Tencent {MODEL_ID.split('/')[-1]}</h1>"
+            f"<p>Generate images using Tencent's state-of-the-art model hosted by FAL AI.</p>"
+            f"Built with <a href='https://huggingface.co/spaces/akhaliq/anycoder' target='_blank'>anycoder</a>"
+            f"</div>"
+        )
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                prompt_input = gr.Textbox(
+                    label="Prompt",
+                    placeholder="e.g., Astronaut riding a horse, 4K, realistic photo, cinematic lighting",
+                    lines=4
+                )
+                generate_btn = gr.Button("🎨 Generate Image", variant="primary")
+                
+            with gr.Column(scale=1):
+                output_image = gr.Image(
+                    label="Generated Image",
+                    height=512,
+                    width=512,
+                    interactive=False,
+                    show_download_button=True
+                )
+
+        # Set up the event listener
+        generate_btn.click(
+            fn=generate_image,
+            inputs=[prompt_input],
+            outputs=[output_image],
+            # Use queue and concurrency for robustness when dealing with external APIs
+            queue=True
+        )
+        
+        # Example usage guidance
+        gr.Examples(
+            examples=[
+                "A dramatic black and white photo of a futuristic motorcycle gang leader in a rainy city street.",
+                "High quality photorealistic close-up portrait of an elderly wizard, highly detailed, dramatic lighting.",
+                "A detailed watercolor painting of a small red fox sleeping on a pile of autumn leaves."
+            ],
+            inputs=prompt_input,
+            outputs=output_image,
+            fn=generate_image,
+            cache_examples=False,
+        )
+
+    return demo
+
+if __name__ == "__main__":
+    app = create_ui()
+    app.queue()
+    app.launch()

models.py

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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:
+        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,
+            # Optional parameters for better quality/control might be added here
+            # e.g., negative_prompt="bad quality, low resolution",
+        )
+        return image
+    except Exception as e:
+        print(f"Error during image generation: {e}")
+        # Check for common API errors
+        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)}")

requirements.txt

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+gradio
+huggingface-hub
+Pillow
+python-dotenv