Update app.py

app.py

@@ -6,6 +6,7 @@ from diffusers import DiffusionPipeline
 import random
 import os
 import json
+import io
 from gradio_client import Client as client_gradio
 from supabase import create_client, Client
 from datetime import datetime
@@ -41,6 +42,26 @@ except Exception as e:
 # Define seed máximo
 MAX_SEED = 2**32 - 1
 
+def upload_image_to_supabase(image, filename):
+    """ Faz upload da imagem gerada para o Supabase Storage e retorna a URL pública. """
+    # Converte a imagem para bytes
+    img_bytes = io.BytesIO()
+    image.save(img_bytes, format="PNG")
+    img_bytes.seek(0)
+
+    # Define o caminho do arquivo no Supabase Storage
+    storage_path = f"images/{filename}"
+
+    # Faz upload para o bucket "images"
+    response = supabase.storage.from_("images").upload(storage_path, img_bytes, {"content-type": "image/png"})
+
+    if response.get("error"):
+        raise Exception(f"Erro ao salvar no Supabase: {response['error']}")
+
+    # Retorna a URL pública da imagem
+    base_url = f"{url}/storage/v1/object/public/images"
+    return f"{base_url}/{filename}"
+
 @spaces.GPU(duration=80)
 def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale_1, lora_scale_2, progress=gr.Progress(track_tqdm=True)):
     # Define uma seed aleatória se necessário
@@ -86,6 +107,28 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
         generator=generator
     ).images[0]
 
+    # Define um nome único para a imagem
+    filename = f"image_{seed}_{datetime.utcnow().strftime('%Y%m%d%H%M%S')}.png"
+
+    # Faz upload da imagem para o Supabase Storage
+    try:
+        image_url = upload_image_to_supabase(image, filename)
+        print(f"✅ Imagem salva no Supabase: {image_url}")
+    except Exception as e:
+        print(f"❌ Erro ao fazer upload da imagem: {e}")
+        image_url = None
+
+    # Salva os metadados no banco de dados Supabase
+    supabase.table("images").insert({
+        "prompt": prompt,
+        "cfg_scale": cfg_scale,
+        "steps": steps,
+        "seed": seed,
+        "lora_scale_1": lora_scale_1,
+        "lora_scale_2": lora_scale_2,
+        "image_url": image_url
+    }).execute()
+
     return image, seed
 
 # Interface Gradio
@@ -98,15 +141,15 @@ with gr.Blocks(theme=gr_theme) as app:
             generate_button = gr.Button("Gerar")
             cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
             steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=25)
-            width = gr.Slider(label="Width", minimum=256, maximum=1024, step=64, value=768)  # Reduzido para evitar falta de VRAM
-            height = gr.Slider(label="Height", minimum=256, maximum=1024, step=64, value=768)  # Reduzido para evitar falta de VRAM
+            width = gr.Slider(label="Width", minimum=256, maximum=1024, step=64, value=768)  
+            height = gr.Slider(label="Height", minimum=256, maximum=1024, step=64, value=1024)  
             randomize_seed = gr.Checkbox(False, label="Randomize seed")
             seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=556215326)
-            lora_scale_1 = gr.Slider(label="LoRA Scale (AndroFlux)", minimum=0, maximum=1, step=0.01, value=0.7)
+            lora_scale_1 = gr.Slider(label="LoRA Scale (AndroFlux)", minimum=0, maximum=1, step=0.01, value=0.5)
             lora_scale_2 = gr.Slider(label="LoRA Scale (VitorCollos)", minimum=0, maximum=1, step=0.01, value=1)
         with gr.Column(scale=1):
             result = gr.Image(label="Generated Image")
-            gr.Markdown("Gere imagens usando Androflux LoRA e um prompt de texto.\n[[Licença não comercial, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]")
+            gr.Markdown("Gere imagens usando Collos e um prompt de texto.")
 
     generate_button.click(
         run_lora,