Update app.py

app.py

@@ -8,32 +8,37 @@ import os
 import json
 from gradio_client import Client as client_gradio
 from supabase import create_client, Client
+from datetime import datetime
 
-# Initialize supabase
+# Inicializa supabase
 url: str = os.getenv('SUPABASE_URL')
 key: str = os.getenv('SUPABASE_KEY')
 supabase: Client = create_client(url, key)
 
-# Get Hugging Face token from secrets
+# Obtém token da Hugging Face
 hf_token = os.getenv("HF_TOKEN")
 
-# Initialize the base model
+# Inicializa o modelo base
 base_model = "black-forest-labs/FLUX.1-dev"
 pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
 
-# Load the first LoRA model
+# Carrega os adaptadores LoRA
 lora_repo_1 = "markury/AndroFlux"
-pipe.load_lora_weights(lora_repo_1, weight_name="AndroFlux-v19.safetensors")
-
-# Load the second LoRA model
 lora_repo_2 = "vcollos/VitorCollos"
-pipe.load_lora_weights(lora_repo_2, weight_name="Vitor.safetensors")
 
-# Combine LoRA weights manually
+try:
+    pipe.load_lora_weights(lora_repo_1, weight_name="AndroFlux-v19.safetensors")
+    print("✅ Primeiro LoRA carregado")
+
+    pipe.load_lora_weights(lora_repo_2, weight_name="Vitor.safetensors")
+    print("✅ Segundo LoRA carregado")
+except Exception as e:
+    print(f"❌ Erro ao carregar os LoRA adapters: {e}")
+
+# Função para combinar os pesos dos LoRA
 def combine_lora_weights(pipe, weight_1, weight_2):
-    for name, module in pipe.unet.named_modules():
-        if "lora" in name:
-            # Combine the weights of the two LoRA models
+    for name, module in pipe.named_modules():  # Percorre os módulos do pipeline
+        if hasattr(module, "weight") and module.weight is not None:
             module.weight.data = weight_1 * module.weight.data + weight_2 * module.weight.data
 
 pipe.to("cuda")
@@ -42,12 +47,12 @@ MAX_SEED = 2**32 - 1
 
 @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)):
-    # Set random seed for reproducibility
+    # Define uma seed aleatória se necessário
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device="cuda").manual_seed(seed)
 
-    # Moderation
+    # Moderação de texto
     moderation_client = client_gradio("duchaba/Friendly_Text_Moderation")
     result = moderation_client.predict(
         msg=f"{prompt}",
@@ -56,20 +61,20 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
     )
 
     if float(json.loads(result[1])['sexual_minors']) > 0.03:
-        print('Minors')
+        print('🔴 Conteúdo não permitido')
         response_data = (supabase.table("requests")
         .insert({"prompt": prompt, "cfg_scale": cfg_scale, "steps": steps, "randomized_seed": randomize_seed, "seed": seed, "lora_scale_1": lora_scale_1, "lora_scale_2": lora_scale_2, "moderated": 'true'})
         .execute()
         )
-        raise gr.Error("Unauthorized request 💥!")
+        raise gr.Error("🚫 Requisição não autorizada!")
 
-    # Update progress bar (0% saat mulai)
-    progress(0, "Starting image generation...")
+    # Atualiza barra de progresso (0% no início)
+    progress(0, "Iniciando a geração de imagem...")
 
-    # Combine LoRA weights
+    # Combina os LoRA weights corretamente
     combine_lora_weights(pipe, lora_scale_1, lora_scale_2)
 
-    # Generate image using the pipeline
+    # Gera imagem com o pipeline
     image = pipe(
         prompt=f"{prompt}",
         num_inference_steps=steps,
@@ -80,55 +85,55 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
         max_sequence_length=512
     ).images[0]
 
-    # Save the image to a file with a unique name in /tmp directory
+    # Salva a imagem em um diretório temporário
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
     image_filename = f"generated_image_{timestamp}.png"
     image_path = os.path.join("/tmp/gradio", image_filename)
 
-    # Add Metadata
-    new_metadata_string = f"{prompt}\nNegative prompt: none\nSteps: {steps}, CFG scale: {cfg_scale}, Seed: {seed}, Lora hashes: AndroFlux-v19: c44afd41ece1, VitorCollos: <hash>"
+    # Adiciona metadados à imagem
+    new_metadata_string = f"{prompt}\nNegative prompt: none\nSteps: {steps}, CFG scale: {cfg_scale}, Seed: {seed}, Lora hashes: AndroFlux-v19, VitorCollos"
     metadata = PngImagePlugin.PngInfo()
     metadata.add_text("parameters", new_metadata_string)
 
-    # Save the tmp image
+    # Salva a imagem gerada
     image.save(image_path, pnginfo=metadata)
 
-    # Log queries
+    # Registra a imagem no Supabase
     try:
         if "girl" not in prompt and "woman" not in prompt:
-            # Save image in supabase
+            # Salva a imagem no Supabase Storage
             response = supabase.storage.from_('generated_images').upload(image_filename, image_path, file_options={"content-type": "image/png;charset=UTF-8"})
             print(response.dict)
-            # Log request in supabase
+            
+            # Registra a requisição no Supabase
             response_data = (supabase.table("requests")
             .insert({"prompt": prompt, "cfg_scale": cfg_scale, "steps": steps, "randomized_seed": randomize_seed, "seed": seed, "lora_scale_1": lora_scale_1, "lora_scale_2": lora_scale_2, "image_url": response.full_path})
             .execute()
             )
-
     except Exception as error:
-        # handle the exception
-        print("An exception occurred:", error)
+        print("⚠️ Erro ao salvar no Supabase:", error)
 
     yield image, seed
 
+# Interface Gradio
 gr_theme = os.getenv("THEME")
 with gr.Blocks(theme=gr_theme) as app:
     gr.Markdown("# Androflux Image Generator")
     with gr.Row():
         with gr.Column(scale=3):
-            prompt = gr.TextArea(label="Prompt", placeholder="Type a prompt of max 77 characters", lines=3)
-            generate_button = gr.Button("Generate")
-            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)  # Use um valor padrão
-            steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=25)  # Use um valor padrão
-            width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=896)  # Use um valor padrão
-            height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1152)  # Use um valor padrão
+            prompt = gr.TextArea(label="Prompt", placeholder="Digite um prompt (máx 77 caracteres)", lines=3)
+            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=1536, step=64, value=896)
+            height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1152)
             randomize_seed = gr.Checkbox(False, label="Randomize seed")
-            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=556215326)  # Use um valor padrão
-            lora_scale_1 = gr.Slider(label="LoRA Scale (AndroFlux)", minimum=0, maximum=1, step=0.01, value=0.7)  # Use um valor padrão
-            lora_scale_2 = gr.Slider(label="LoRA Scale (VitorCollos)", minimum=0, maximum=1, step=0.01, value=1)  # Use um valor padrão
+            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_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("Generate images using Androflux Lora and a text prompt.\n[[non-commercial license, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]")
+            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)]")
 
     generate_button.click(
         run_lora,
@@ -137,4 +142,4 @@ with gr.Blocks(theme=gr_theme) as app:
     )
 
 app.queue()
-app.launch(share=True)  # Set `share=True` to create a public link
+app.launch(share=True)  # `share=True` cria um link público