Please work

app.py

@@ -1,92 +1,20 @@
-import os, gradio as gr, torch, threading
-from pathlib import Path
-from diffusers import AutoPipelineForText2Image
-
-# Persist caches across restarts
-os.environ.setdefault("HF_HOME", "/data/.huggingface")
-os.environ.setdefault("HF_HUB_CACHE", "/data/.huggingface/hub")
-os.environ.setdefault("TRANSFORMERS_CACHE", "/data/.transformers")
-os.environ.setdefault("DIFFUSERS_CACHE", "/data/.diffusers")
-
-DEVICE = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
-DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
-
-# Small pipeline cache so switching models is instant after first load
-_PIPE_CACHE = {}
-_LOCK = threading.Lock()
-
-def get_pipe(model_id: str):
-    with _LOCK:
-        if model_id in _PIPE_CACHE:
-            return _PIPE_CACHE[model_id]
-        pipe = AutoPipelineForText2Image.from_pretrained(
-            model_id, torch_dtype=DTYPE
-        ).to(DEVICE)
-        # SDXL-Turbo prefers guidance ~0; SDXL-Base likes ~5-8. We'll expose a slider.
-        _PIPE_CACHE[model_id] = pipe
-        return pipe
-
-def generate(model_choice, prompt, steps, guidance, width, height, seed):
-    if not prompt or not prompt.strip():
-        return None, "Please enter a prompt."
-    model_id = {
-        "SDXL-Turbo (fast)": "stabilityai/sdxl-turbo",
-        "SDXL-Base 1.0 (quality)": "stabilityai/stable-diffusion-xl-base-1.0",
-    }[model_choice]
-    pipe = get_pipe(model_id)
-
-    gen = None
-    if seed is not None and seed >= 0:
-        # pick the device the pipeline actually executes on
-        exec_device = pipe._execution_device if hasattr(pipe, "_execution_device") else DEVICE
-        gen = torch.Generator(device=exec_device).manual_seed(int(seed))
-
-    out = pipe(
-        prompt=prompt,
-        num_inference_steps=int(steps),
-        guidance_scale=float(guidance),
-        width=int(width),
-        height=int(height),
-        generator=gen
-    )
-    return out.images[0], f"Model: {model_choice} | Steps: {steps} | Guidance: {guidance} | Size: {width}x{height} | Seed: {seed}"
-
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🌸 Flowerfy — Text → Flower Arrangement")
-    gr.Markdown("Type a description and generate a floral arrangement image. Toggle **Turbo** for speed or **Base** for quality.")
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            model_choice = gr.Radio(
-                ["SDXL-Turbo (fast)", "SDXL-Base 1.0 (quality)"],
-                value="SDXL-Turbo (fast)", label="Model"
-            )
-            prompt = gr.Textbox(
-                label="Prompt",
-                value="airy garden-style flower arrangement, pastel roses and eucalyptus, soft natural light"
-            )
-            with gr.Row():
-                steps = gr.Slider(1, 40, step=1, value=4, label="Inference steps (Turbo: 1–4, Base: ~30)")
-                guidance = gr.Slider(0.0, 10.0, step=0.1, value=0.0, label="Guidance (Turbo≈0, Base≈6)")
-            with gr.Row():
-                width = gr.Slider(512, 1024, step=64, value=1024, label="Width")
-                height = gr.Slider(512, 1024, step=64, value=1024, label="Height")
-            seed = gr.Number(value=42, label="Seed (≥0 makes it repeatable)")
-            go = gr.Button("Generate 🌼", variant="primary")
-
-        with gr.Column(scale=1):
-            out_img = gr.Image(label="Result", type="pil")
-            meta = gr.Textbox(label="Run details", interactive=False)
-
-    def tweak_defaults(choice):
-        # Convenience: set sensible defaults when the model is toggled
-        if choice.startswith("SDXL-Turbo"):
-            return 4, 0.0
-        # SDXL-Base
-        return 30, 6.0
-
-    model_choice.change(fn=tweak_defaults, inputs=model_choice, outputs=[steps, guidance])
-    go.click(generate, inputs=[model_choice, prompt, steps, guidance, width, height, seed],
-             outputs=[out_img, meta], api_name="generate")
-
-demo.queue(concurrency_count=1, max_size=12).launch()
+import gradio as gr
+
+def greet(name):
+    """
+    A simple function that takes a name as input and returns a greeting.
+    """
+    return "Hello " + name + "!"
+
+# Create a Gradio Interface
+# - fn: The Python function to wrap with a UI
+# - inputs: The type of input component (e.g., "text" for a text box)
+# - outputs: The type of output component (e.g., "text" for a text box)
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+# Launch the Gradio application
+# When deploying to Hugging Face Spaces, demo.launch() is often not explicitly called in app.py
+# as Spaces handles the launching automatically based on the presence of gr.Interface.
+# However, for local testing, demo.launch() is necessary.
+if __name__ == "__main__":
+    demo.launch()