Update app.py

app.py

@@ -4,8 +4,6 @@ import random
 import spaces
 import torch
 from diffusers import DiffusionPipeline
-import boto3
-from io import BytesIO
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -15,47 +13,28 @@ pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", tor
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-# Initialize S3 client
-s3_client = boto3.client('s3')
-BUCKET_NAME = 'your-s3-bucket-name'  # Replace with your S3 bucket name
-
-def upload_to_s3(image, image_name):
-    """Upload an image to S3 bucket."""
-    buffer = BytesIO()
-    image.save(buffer, format="PNG")
-    buffer.seek(0)
-    s3_client.put_object(Bucket=BUCKET_NAME, Key=image_name, Body=buffer, ContentType='image/png')
-    return f"https://{BUCKET_NAME}.s3.amazonaws.com/{image_name}"
-
 @spaces.GPU()
 def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
-            prompt=prompt, 
-            width=width,
-            height=height,
-            num_inference_steps=num_inference_steps, 
-            generator=generator,
+            prompt = prompt, 
+            width = width,
+            height = height,
+            num_inference_steps = num_inference_steps, 
+            generator = generator,
             guidance_scale=0.0
     ).images[0] 
-    
-    # Generate a unique name for the image
-    image_name = f"{seed}_{prompt[:10]}.png"
-    
-    # Upload image to S3
-    s3_url = upload_to_s3(image, image_name)
-    
-    return image, seed, s3_url
-
+    return image, seed
+ 
 examples = [
     "a tiny astronaut hatching from an egg on the moon",
     "a cat holding a sign that says hello world",
     "an anime illustration of a wiener schnitzel",
 ]
 
-css = """
+css="""
 #col-container {
     margin: 0 auto;
     max-width: 520px;
@@ -83,7 +62,6 @@ with gr.Blocks(css=css) as demo:
             run_button = gr.Button("Run", scale=0)
         
         result = gr.Image(label="Result", show_label=False)
-        s3_link = gr.Text(label="S3 URL", show_label=False)
         
         with gr.Accordion("Advanced Settings", open=False):
             
@@ -117,6 +95,7 @@ with gr.Blocks(css=css) as demo:
             
             with gr.Row():
                 
+  
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -126,18 +105,18 @@ with gr.Blocks(css=css) as demo:
                 )
         
         gr.Examples(
-            examples=examples,
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result, seed, s3_link],
+            examples = examples,
+            fn = infer,
+            inputs = [prompt],
+            outputs = [result, seed],
             cache_examples="lazy"
         )
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
-        outputs=[result, seed, s3_link]
+        fn = infer,
+        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
+        outputs = [result, seed]
     )
 
-demo.launch()
+demo.launch()