Delete gui-gradio.py

gui-gradio.py

@@ -1,188 +0,0 @@
-import gradio as gr
-import subprocess
-import os
-import threading
-import queue
-import json
-
-# Função para rodar subprocessos
-def run_subprocess(cmd, output_queue):
-    try:
-        process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
-        for line in process.stdout:
-            output_queue.put(line)
-        process.wait()
-        if process.returncode == 0:
-            output_queue.put("Process completed successfully!")
-        else:
-            output_queue.put(f"Process failed with return code {process.returncode}")
-    except Exception as e:
-        output_queue.put(f"An error occurred: {str(e)}")
-
-# Classe para gerenciar as configurações salvas
-class ConfigManager:
-    def __init__(self, filepath="settings.json"):
-        self.filepath = filepath
-        self.settings = self.load_settings()
-
-    def save_settings(self):
-        with open(self.filepath, "w") as f:
-            json.dump(self.settings, f, indent=2, ensure_ascii=False)
-
-    def load_settings(self):
-        if os.path.exists(self.filepath):
-            with open(self.filepath, "r") as f:
-                return json.load(f)
-        return {"saved_combinations": {}}
-
-    def get_saved_combinations(self):
-        return self.settings.get("saved_combinations", {})
-
-    def add_combination(self, name, combination):
-        self.settings["saved_combinations"][name] = combination
-        self.save_settings()
-
-config_manager = ConfigManager()
-
-# Funções de treinamento e inferência
-def run_training(model_type, config_path, start_checkpoint, results_path, data_paths, valid_paths, num_workers, device_ids):
-    if not (model_type and config_path and results_path and data_paths and valid_paths):
-        return "Error: Missing required inputs for training."
-
-    cmd = [
-        "python", "train.py",
-        "--model_type", model_type,
-        "--config_path", config_path,
-        "--results_path", results_path,
-        "--data_path", *data_paths.split(';'),
-        "--valid_path", *valid_paths.split(';'),
-        "--num_workers", str(num_workers),
-        "--device_ids", device_ids
-    ]
-
-    if start_checkpoint:
-        cmd += ["--start_check_point", start_checkpoint]
-
-    output_queue = queue.Queue()
-    threading.Thread(target=run_subprocess, args=(cmd, output_queue), daemon=True).start()
-
-    output = []
-    while not output_queue.empty():
-        output.append(output_queue.get())
-    return "\n".join(output)
-
-def run_inference(model_type, config_path, start_checkpoint, input_folder, store_dir, extract_instrumental):
-    if not (model_type and config_path and input_folder and store_dir):
-        return "Error: Missing required inputs for inference."
-
-    cmd = [
-        "python", "inference.py",
-        "--model_type", model_type,
-        "--config_path", config_path,
-        "--input_folder", input_folder,
-        "--store_dir", store_dir
-    ]
-
-    if start_checkpoint:
-        cmd += ["--start_check_point", start_checkpoint]
-    if extract_instrumental:
-        cmd += ["--extract_instrumental"]
-
-    output_queue = queue.Queue()
-    threading.Thread(target=run_subprocess, args=(cmd, output_queue), daemon=True).start()
-
-    output = []
-    while not output_queue.empty():
-        output.append(output_queue.get())
-    return "\n".join(output)
-
-# Interface Gradio
-def add_preset(name, model_type, config_path, checkpoint):
-    if not name:
-        return "Error: Name is required to save a preset."
-
-    config_manager.add_combination(name, {
-        "model_type": model_type,
-        "config_path": config_path,
-        "checkpoint": checkpoint
-    })
-    return f"Preset '{name}' saved successfully."
-
-saved_presets = config_manager.get_saved_combinations()
-preset_names = list(saved_presets.keys())
-
-def load_preset(name):
-    if name in saved_presets:
-        preset = saved_presets[name]
-        return preset["model_type"], preset["config_path"], preset["checkpoint"]
-    return "", "", ""
-
-with gr.Blocks() as gui:
-    gr.Markdown("# Music Source Separation Training & Inference GUI")
-
-    # Treinamento
-    with gr.Accordion("Training Configuration", open=False):
-        model_type = gr.Dropdown(
-            choices=["apollo", "bandit", "htdemucs", "scnet"], label="Model Type"
-        )
-        config_path = gr.Textbox(label="Config File Path")
-        start_checkpoint = gr.Textbox(label="Checkpoint (Optional)")
-        results_path = gr.Textbox(label="Results Path")
-        data_paths = gr.Textbox(label="Data Paths (separated by ';')")
-        valid_paths = gr.Textbox(label="Validation Paths (separated by ';')")
-        num_workers = gr.Number(label="Number of Workers", value=4)
-        device_ids = gr.Textbox(label="Device IDs (comma-separated)", value="0")
-        train_output = gr.Textbox(label="Training Output", interactive=False)
-
-        gr.Button("Run Training").click(
-            run_training,
-            inputs=[
-                model_type, config_path, start_checkpoint, results_path, data_paths,
-                valid_paths, num_workers, device_ids
-            ],
-            outputs=train_output
-        )
-
-    # Inferência
-    with gr.Accordion("Inference Configuration", open=False):
-        infer_model_type = gr.Dropdown(
-            choices=["apollo", "bandit", "htdemucs", "scnet"], label="Model Type"
-        )
-        infer_config_path = gr.Textbox(label="Config File Path")
-        infer_checkpoint = gr.Textbox(label="Checkpoint (Optional)")
-        input_folder = gr.Textbox(label="Input Folder")
-        store_dir = gr.Textbox(label="Output Folder")
-        extract_instrumental = gr.Checkbox(label="Extract Instrumental", value=False)
-        infer_output = gr.Textbox(label="Inference Output", interactive=False)
-
-        gr.Button("Run Inference").click(
-            run_inference,
-            inputs=[
-                infer_model_type, infer_config_path, infer_checkpoint, input_folder,
-                store_dir, extract_instrumental
-            ],
-            outputs=infer_output
-        )
-
-    # Gerenciamento de Presets
-    with gr.Accordion("Presets", open=False):
-        preset_name = gr.Textbox(label="Preset Name")
-        preset_model_type = gr.Textbox(label="Model Type")
-        preset_config_path = gr.Textbox(label="Config Path")
-        preset_checkpoint = gr.Textbox(label="Checkpoint")
-        preset_feedback = gr.Textbox(label="Feedback", interactive=False)
-
-        gr.Button("Save Preset").click(
-            add_preset,
-            inputs=[preset_name, preset_model_type, preset_config_path, preset_checkpoint],
-            outputs=preset_feedback
-        )
-
-        preset_dropdown = gr.Dropdown(
-            choices=preset_names, label="Load Preset"
-        )
-        gr.Button("Load Preset").click(
-            load_preset, inputs=preset_dropdown, outputs=[preset_model_type, preset_config_path, preset_checkpoint]
-        )
-
-gui.launch()