Update app.py

app.py

@@ -1,14 +1,13 @@
 import gradio as gr
 import numpy as np
 import random
-import spaces
 import torch
-from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
+from diffusers import DiffusionPipeline
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 import boto3
-import os
 from io import BytesIO
 import time
+import os
 
 # S3 Configuration
 S3_BUCKET = "afri"
@@ -29,22 +28,17 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
 def save_image_to_s3(image):
-    # Convert PIL Image to bytes
     img_byte_arr = BytesIO()
     image.save(img_byte_arr, format='PNG')
     img_byte_arr = img_byte_arr.getvalue()
 
-    # Generate a unique filename
     filename = f"generated_image_{int(time.time())}.png"
 
-    # Upload to S3
     s3_client.put_object(Bucket=S3_BUCKET, 
                          Key=filename, 
                          Body=img_byte_arr, 
-                         ContentType='image/png',
-                         ACL='public-read')
+                         ContentType='image/png')
 
-    # Generate a public URL
     url = f"https://{S3_BUCKET}.s3.{S3_REGION}.amazonaws.com/{filename}"
     return url
 
@@ -61,7 +55,6 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
         guidance_scale=guidance_scale
     ).images[0]
     
-    # Save image to S3 and get URL
     image_url = save_image_to_s3(image)
     
     return image_url, seed
@@ -152,4 +145,4 @@ with gr.Blocks(css=css) as demo:
         outputs=[result, seed]
     )
 
-demo.launch()
+demo.launch(share=True)