Update app.py

app.py

@@ -1,11 +1,53 @@
 import gradio as gr
-import time
-import logging
+import torch
+from diffusers import DiffusionPipeline
+import os
 
-# Set up logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
+# Ensure GPU hardware is selected in Space settings!
+print("Loading diffusion pipeline...")
+pipe = DiffusionPipeline.from_pretrained(
+    "John6666/the-araminta-fv1-sdxl",
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+    token=os.environ.get("HF_TOKEN") # Use token from secrets or OAuth
+)
+if torch.cuda.is_available():
+    pipe = pipe.to("cuda")
+else:
+    print("WARNING: No CUDA GPU available, using CPU.")
 
-demo = gr.load("models/John6666/the-araminta-fv1-sdxl")
+def generate_image(prompt, negative_prompt="", guidance_scale=7.5, num_steps=30):
+    print(f"Generating image for prompt: {prompt}")
+    try:
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=int(num_steps) # Ensure steps is int
+        ).images[0]
+        print("Image generation successful.")
+        return image
+    except Exception as e:
+        print(f"Error during image generation: {e}")
+        raise gr.Error(f"Failed to generate image: {e}")
 
+with gr.Blocks() as demo:
+    gr.Markdown("# The Araminta FV1 SDXL")
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Prompt")
+            neg_prompt_input = gr.Textbox(label="Negative Prompt", value="")
+            gs_input = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, step=0.5, value=7.5)
+            steps_input = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=30)
+            submit_btn = gr.Button("Generate")
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image")
+
+    submit_btn.click(
+        fn=generate_image,
+        inputs=[prompt_input, neg_prompt_input, gs_input, steps_input],
+        outputs=output_image
+    )
+
+print("Launching Gradio interface...")
 demo.launch()