Update app.py

app.py

@@ -1,13 +1,12 @@
-
-# from huggingface_hub import login
-# # Login com token via Secrets
-# login(os.environ["HF_TOKEN"])
 import gradio as gr
 import numpy as np
 import random
 import spaces
 import torch
 from diffusers import DiffusionPipeline
+import os
+import uuid
+from datetime import datetime
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -17,28 +16,46 @@ pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", tor
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
+# Diretório para salvar imagens (use /tmp para Hugging Face Spaces)
+OUTPUT_DIR = "/tmp"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
 @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)
+    
+    # Gerar a imagem
     image = pipe(
-            prompt = prompt, 
-            width = width,
-            height = height,
-            num_inference_steps = num_inference_steps, 
-            generator = generator,
-            guidance_scale=0.0
-    ).images[0] 
-    return image, seed
- 
+        prompt=prompt, 
+        width=width,
+        height=height,
+        num_inference_steps=num_inference_steps, 
+        generator=generator,
+        guidance_scale=0.0
+    ).images[0]
+    
+    # Gerar nome de arquivo único
+    filename = f"image_{uuid.uuid4().hex}.png"
+    filepath = os.path.join(OUTPUT_DIR, filename)
+    
+    # Salvar a imagem
+    image.save(filepath)
+    
+    # Construir o URL público (ajuste conforme a URL do seu Space)
+    space_url = "https://eder0782-flux-image-generator.hf.space"
+    image_url = f"{space_url}/file/{filename}"
+    
+    return {"image_url": image_url, "seed": 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;
@@ -46,7 +63,6 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""# FLUX.1 [schnell]
 12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
@@ -54,7 +70,6 @@ with gr.Blocks(css=css) as demo:
         """)
         
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -62,13 +77,11 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
             run_button = gr.Button("Run", scale=0)
         
         result = gr.Image(label="Result", show_label=False)
         
         with gr.Accordion("Advanced Settings", open=False):
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -76,11 +89,8 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -88,7 +98,6 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
@@ -96,31 +105,32 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-            
-            with gr.Row():
-                
-  
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=4,
-                )
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=1,
+                maximum=50,
+                step=1,
+                value=4,
+            )
         
         gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
+            examples=examples,
+            fn=infer,
+            inputs=[prompt],
+            outputs=[result, seed],
             cache_examples="lazy"
         )
 
+    # Ajustar a saída para a interface
+    def format_output(output):
+        return output["image_url"], output["seed"]
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
-        outputs = [result, seed]
+        fn=infer,
+        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
+        outputs=[result, seed],
+        _js=format_output
     )
 
-demo.launch()
+demo.launch()