Update app.py

app.py

@@ -1,57 +1,40 @@
-import gradio as gr
-import numpy as np
-import random
-from optimum.intel import OVStableDiffusionXLPipeline
+import requests
+import io
+import base64
+from PIL import Image
 
-# Load the model specifically for CPU optimization (OpenVINO)
-model_repo_id = "stabilityai/sdxl-turbo"
-
-# 'export=True' converts the model to OpenVINO format on-the-fly
-# If you have already exported it, point model_id to your local folder
-print("Loading OpenVINO model (this may take a minute on first run)...")
-pipe = OVStableDiffusionXLPipeline.from_pretrained(
-    model_repo_id, 
-    export=True, 
-    compile=True # Pre-compiles for CPU speed
-)
-
-# Speed optimization: Static Reshaping
-# This tells the CPU exactly what size to expect so it can optimize the math paths
-pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
+# ... (rest of your SDXL-Turbo setup) ...
 
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-    if randomize_seed:
-        seed = random.randint(0, 2**32 - 1)
-    
-    # Standard CPU generation
-    # Note: Width/Height must match the .reshape() values above for max speed
-    image = pipe(
+    # 1. Generate the raw image
+    result = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
-        width=512,  # Locked to 512 for CPU stability
-        height=512,
-        latents=None
-    ).images[0]
+        width=width,
+        height=height,
+    )
+    
+    raw_image = result.images[0]
 
-    return image, seed
+    # 2. Save image to a "buffer" (in memory, not on disk)
+    img_byte_arr = io.BytesIO()
+    raw_image.save(img_byte_arr, format='PNG')
+    img_byte_arr = img_byte_arr.getvalue()
 
-# UI Setup (Simplified for CPU Speed)
-with gr.Blocks() as demo:
-    gr.Markdown("# ❄️ CPU-Optimized SDXL Turbo (OpenVINO)")
-    with gr.Row():
-        prompt = gr.Text(label="Prompt", placeholder="A fast CPU cat")
-        run_btn = gr.Button("Run")
+    # 3. Upload to ImgBB (Free API)
+    # Get a free key from https://api.imgbb.com/
+    IMGBB_API_KEY = "YOUR_IMGBB_API_KEY_HERE"
     
-    result = gr.Image()
+    response = requests.post(
+        "https://api.imgbb.com/1/upload",
+        data={
+            "key": IMGBB_API_KEY,
+            "image": base64.b64encode(img_byte_arr),
+        }
+    )
     
-    # Hide complex settings to prevent CPU crashes
-    with gr.Accordion("Settings", open=False):
-        steps = gr.Slider(1, 4, value=1, label="Steps (Keep at 1 for CPU)")
-        seed = gr.Number(value=42, label="Seed")
-
-    run_btn.click(infer, [prompt, gr.State(""), seed, gr.State(False), gr.State(512), gr.State(512), gr.State(0.0), steps], [result, seed])
-
-if __name__ == "__main__":
-    demo.launch()
+    # 4. Return the URL instead of the raw image
+    upload_data = response.json()
+    return upload_data["data"]["url"]