Update app.py

app.py

@@ -39,6 +39,86 @@ download_models()
 
 # --- 2. LÓGICA DE INFERÊNCIA ---
 
+def run_subprocess_with_logs(command, cwd):
+    """Função genérica para rodar um subprocesso e streamar os logs para o Gradio."""
+    log_output = f"Executando comando:\n{' '.join(command)}\n\n"
+    yield [], log_output
+    
+    env = os.environ.copy(); env["PYTHONUNBUFFERED"] = "1"
+    process = subprocess.Popen(command, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, encoding='utf-8', env=env)
+    
+    while True:
+        output = process.stdout.readline()
+        if output == '' and process.poll() is not None: break
+        if output: log_output += output; yield [], log_output
+            
+    if process.poll() != 0: raise gr.Error("A inferência falhou. Verifique os logs.")
+
+def run_seedvr_inference(video_path, seed):
+    # (Lógica específica do SeedVR)
+    if video_path is None: raise gr.Error("Por favor, faça o upload de um arquivo para o SeedVR.")
+    job_id = str(uuid.uuid4())
+    input_dir = os.path.join("/app", "temp_inputs", job_id); os.makedirs(input_dir, exist_ok=True)
+    output_dir = os.path.join("/app", "temp_outputs", job_id); os.makedirs(output_dir, exist_ok=True)
+    shutil.copy(video_path, input_dir)
+    
+    input_folder_relative = os.path.relpath(input_dir, SEEDVR_DIR)
+    output_folder_relative = os.path.relpath(output_dir, SEEDVR_DIR)
+    command = ["torchrun", "--nproc-per-node=4", "projects/inference_seedvr2_3b.py", "--video_path", input_folder_relative, "--output_dir", output_folder_relative, "--seed", str(seed), "--res_h", "720", "--res_w", "1280"]
+    
+    for gallery, logs in run_subprocess_with_logs(command, SEEDVR_DIR):
+        yield None, logs # Retorna None para o output enquanto os logs são atualizados
+    
+    output_files = [os.path.join(output_dir, f) for f in os.listdir(output_dir) if f.endswith(('.mp4', '.png'))]
+    if not output_files: raise gr.Error("Nenhum arquivo de saída encontrado.")
+    yield output_files[0], logs
+
+def run_vincie_edit_inference(files, prompts_str):
+    # (Lógica específica para o modo de EDIÇÃO do V-INT)
+    if not files: raise gr.Error("Por favor, faça o upload de uma imagem para editar.")
+    if not prompts_str: raise gr.Error("Por favor, forneça os prompts de edição.")
+
+# app.py (Interface final com 3 abas: SeedVR, V-INT Edit, V-INT Pipeline)
+
+import gradio as gr
+import os
+import subprocess
+import shutil
+import uuid
+from huggingface_hub import snapshot_download
+import spaces
+
+# --- 1. CONFIGURAÇÃO E DOWNLOAD DOS MODELOS ---
+
+SEEDVR_DIR = "/app/SeedVR"
+VINCIE_DIR = "/app/VINCIE"
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+@spaces.GPU
+def download_models():
+    """Baixa os modelos para SeedVR e V-INT se eles não existirem."""
+    # Download do SeedVR
+    if not os.path.exists(os.path.join(SEEDVR_DIR, "ckpts", "seedvr2_ema_3b.pth")):
+        print("Baixando modelo do SeedVR-3B...")
+        snapshot_download(repo_id="ByteDance-Seed/SeedVR2-3B", local_dir=os.path.join(SEEDVR_DIR, "ckpts"), token=HF_TOKEN, local_dir_use_symlinks=False)
+    else: print("Modelo do SeedVR já existe.")
+
+    # Download do V-INT
+    if not os.path.exists(os.path.join(VINCIE_DIR, "ckpt", "VINCIE-3B")):
+        print("Baixando modelo do VINCIE-3B...")
+        snapshot_download(repo_id="ByteDance-Seed/VINCIE-3B", local_dir=os.path.join(VINCIE_DIR, "ckpt", "VINCIE-3B"), token=HF_TOKEN, local_dir_use_symlinks=False)
+    else: print("Modelo do VINCIE já existe.")
+
+    # Download de assets e configs do V-INT (para os exemplos)
+    if not os.path.exists(os.path.join(VINCIE_DIR, "assets", "woman_pineapple.png")):
+         print("Baixando assets e configs do V-INT...")
+         snapshot_download(repo_id="ByteDance-Seed/VINCIE", repo_type="space", local_dir=VINCIE_DIR, token=HF_TOKEN, allow_patterns=["assets/*", "configs/*"], local_dir_use_symlinks=False)
+    else: print("Assets e configs do V-INT já existem.")
+
+download_models()
+
+# --- 2. LÓGICA DE INFERÊNCIA ---
+
 def run_subprocess_with_logs(command, cwd):
     """Função genérica para rodar um subprocesso e streamar os logs para o Gradio."""
     log_output = f"Executando comando:\n{' '.join(command)}\n\n"
@@ -141,4 +221,29 @@ with gr.Blocks() as demo:
                     vincie_edit_prompts = gr.Textbox(label="Prompts de Edição (separados por ';')", lines=5, placeholder="Ex: Add a crown to her head; Change the background...")
                     vincie_edit_button = gr.Button("Executar Edição", variant="primary")
                 with gr.Column(scale=2):
-                    vincie_edit_output 
+                    vincie_edit_output = gr.Gallery(label="Imagens Editadas")
+                    vincie_edit_logs = gr.Textbox(label="Logs", lines=10, interactive=False)
+            gr.Examples(examples=[[[os.path.join(VINCIE_DIR, "assets/woman_pineapple.png")], "Lower the pineapple beside her face; Add a crown to the woman's head."]], inputs=[vincie_edit_input, vincie_edit_prompts])
+            vincie_edit_button.click(fn=run_vincie_edit_inference, inputs=[vincie_edit_input, vincie_edit_prompts], outputs=[vincie_edit_output, vincie_edit_logs])
+
+        # --- NOVA ABA V-INT PIPELINE ---
+        with gr.TabItem("V-INT (Pipeline/Composição)"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    vincie_pipe_inputs = gr.Files(label="Upload de Múltiplas Imagens para Composição (<IMG0>, <IMG1>, etc.)", file_types=["image"])
+                    vincie_pipe_prompt = gr.Textbox(label="Prompt de Composição Final", lines=5, placeholder="Ex: Based on <IMG0> and <IMG1>, a woman in <IMG0> is holding the cat from <IMG1>. Output <IMG2>:")
+                    vincie_pipe_button = gr.Button("Executar Pipeline", variant="primary")
+                with gr.Column(scale=2):
+                    vincie_pipe_output = gr.Gallery(label="Imagem Composta")
+                    vincie_pipe_logs = gr.Textbox(label="Logs", lines=10, interactive=False)
+            
+            # Prepara os caminhos para os arquivos de exemplo
+            father_path = os.path.join(VINCIE_DIR, "assets/father.png")
+            mother_path = os.path.join(VINCIE_DIR, "assets/mother.png")
+            son_path = os.path.join(VINCIE_DIR, "assets/son.png")
+            family_prompt = "Based on <IMG0>, <IMG1>, and <IMG2>, A smiling family with the father from <IMG0>, mother from <IMG1>, and son from <IMG2>, poses for a portrait amidst the sunlit trees. Output <IMG3>:"
+            gr.Examples(examples=[[[father_path, mother_path, son_path], family_prompt]], inputs=[vincie_pipe_inputs, vincie_pipe_prompt])
+            
+            vincie_pipe_button.click(fn=run_vincie_pipeline_inference, inputs=[vincie_pipe_inputs, vincie_pipe_prompt], outputs=[vincie_pipe_output, vincie_pipe_logs])
+
+demo.queue().launch()