Create gui-gradio-full.py

gui-gradio-full.py

@@ -0,0 +1,214 @@
+import gradio as gr
+import subprocess
+import os
+import threading
+import queue
+import json
+
+# Função para rodar subprocessos com saída ao vivo
+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)}")
+
+# Gerenciador de configurações e presets
+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 principais (Treinamento, Inferência, etc.)
+def run_training(
+    model_type, dataset_type, config_path, start_checkpoint, metrics, results_path, 
+    data_paths, valid_paths, num_workers, device_ids
+):
+    if not (model_type and dataset_type and config_path and results_path and data_paths and valid_paths and metrics):
+        return "Error: Missing required inputs for training."
+
+    cmd = [
+        "python", "train.py",
+        "--model_type", model_type,
+        "--dataset_type", str(dataset_type),
+        "--config_path", config_path,
+        "--results_path", results_path,
+        "--data_path", *data_paths.split(';'),
+        "--valid_path", *valid_paths.split(';'),
+        "--metrics", *metrics.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, additional_metrics
+):
+    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"]
+    if additional_metrics:
+        cmd += ["--metrics", *additional_metrics.split(';')]
+
+    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)
+
+# Função para salvar presets
+def add_preset(name, model_type, config_path, checkpoint, metrics):
+    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,
+        "metrics": metrics
+    })
+    return f"Preset '{name}' saved successfully."
+
+# Carregar presets
+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"], preset["metrics"]
+    return "", "", "", ""
+
+# Interface Gradio
+with gr.Blocks() as gui:
+    gr.Markdown("# 🎵 Music Source Separation GUI - Complete Version")
+
+    # Aba: Treinamento
+    with gr.Tab("Training"):
+        with gr.Row():
+            model_type = gr.Dropdown(
+                label="Model Type", 
+                choices=["apollo", "bandit", "bandit_v2", "bs_roformer", "htdemucs", "mdx23c", "mel_band_roformer", "scnet", "scnet_unofficial", "segm_models", "swin_upernet", "torchseg"]
+            )
+            dataset_type = gr.Dropdown(label="Dataset Type", choices=["1", "2", "3", "4"])
+        
+        with gr.Row():
+            config_path = gr.Textbox(label="Config File Path")
+            start_checkpoint = gr.Textbox(label="Start Checkpoint (Optional)")
+
+        metrics = gr.Textbox(label="Metrics (comma-separated, e.g., SDR, SIR)")
+        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, dataset_type, config_path, start_checkpoint, metrics, 
+                results_path, data_paths, valid_paths, num_workers, device_ids
+            ], 
+            outputs=train_output
+        )
+
+    # Aba: Inferência
+    with gr.Tab("Inference"):
+        with gr.Row():
+            infer_model_type = gr.Dropdown(
+                label="Model Type",
+                choices=["apollo", "bandit", "bandit_v2", "bs_roformer", "htdemucs", "mdx23c", "mel_band_roformer", "scnet", "scnet_unofficial", "segm_models", "swin_upernet", "torchseg"]
+            )
+            infer_config_path = gr.Textbox(label="Config File Path")
+
+        infer_checkpoint = gr.Textbox(label="Start 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)
+        additional_metrics = gr.Textbox(label="Additional Metrics (comma-separated)")
+
+        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, additional_metrics
+            ],
+            outputs=infer_output
+        )
+
+    # Aba: Presets
+    with gr.Tab("Presets"):
+        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_metrics = gr.Textbox(label="Metrics")
+        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, preset_metrics],
+            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, preset_metrics]
+        )
+
+gui.launch()