Update app.py

app.py

@@ -1,5 +1,240 @@
-runtime error
-Exit code: 1. Reason:   File "/home/user/app/app.py", line 5
-    0%|          | 0/9 [00:00<?, ?it/s]/usr/local/lib/python3.10/site-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.
-                                                                                                                                                                                                                               ^
-SyntaxError: unterminated string literal (detected at line 5)
+import os
+import gradio as gr
+from huggingface_hub import login
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling, pipeline
+from peft import get_peft_model, LoraConfig, TaskType, PeftModel
+import json
+
+# ============================================================
+# ⚙️ CONFIGURACIÓN GLOBAL
+# ============================================================
+BASE_MODEL = "bigcode/santacoder"  
+LORA_PATH = "./lora_output"       
+DATASET_FILE = "codesearchnet_lora_dataset.json"   
+MAX_TOKEN_LENGTH = 256             
+NUM_SAMPLES_TO_PROCESS = 5000      
+
+# Variables globales
+tokenizer = None
+lora_model = None
+tokenized_dataset = None
+lora_generator = None
+
+# ============================================================
+# 🚨 LÓGICA DE PRE-PROCESAMIENTO DE DATOS (INTEGRADA) 🚨
+# ============================================================
+def prepare_codesearchnet():
+    """Descarga, procesa y guarda el dataset CodeSearchNet si no existe."""
+    if os.path.exists(DATASET_FILE):
+        print(f"✅ Dataset '{DATASET_FILE}' ya existe. Cargando directamente.")
+        return
+
+    print(f"🔄 Dataset no encontrado. Iniciando descarga y pre-procesamiento de CodeSearchNet ({NUM_SAMPLES_TO_PROCESS} muestras)...")
+    
+    try:
+        raw_csn = load_dataset('Nan-Do/code-search-net-python', split=f'train[:{NUM_SAMPLES_TO_PROCESS}]')
+
+        def format_for_lora(example):
+            prompt_text = (
+                f"# Descripción: {example['docstring_summary']}\n"
+                f"# Completa la siguiente función:\n"
+                f"def {example['func_name']}("
+            )
+            completion_text = example['code']
+            
+            return {
+                "prompt": prompt_text,
+                "completion": completion_text
+            }
+
+        lora_dataset = raw_csn.map(
+            format_for_lora, 
+            batched=False, # Importante: batched=False para evitar problemas de memoria en móvil
+            remove_columns=raw_csn["train"].column_names,
+        )
+
+        lora_dataset.to_json(DATASET_FILE)
+        print(f"✅ Pre-procesamiento completado. {NUM_SAMPLES_TO_PROCESS} ejemplos guardados en '{DATASET_FILE}'.")
+
+    except Exception as e:
+        print(f"❌ Error CRÍTICO al descargar/procesar CodeSearchNet. Error: {e}")
+        # Crea un JSON mínimo para evitar errores de ruta en el siguiente paso
+        minimal_dataset = [{"prompt": "# Error de carga. Intenta de nuevo.", "completion": "pass\n"}] * 10
+        with open(DATASET_FILE, 'w') as f:
+            json.dump(minimal_dataset, f)
+
+# ============================================================
+# 🔐 AUTENTICACIÓN Y PRE-CARGA DE RECURSOS (SINGLETON)
+# ============================================================
+
+def setup_resources():
+    """Carga y configura todos los recursos (modelo, tokenizer, dataset) una sola vez."""
+    global tokenizer, lora_model, tokenized_dataset
+
+    # 🛑 1. PREPARA EL DATASET DE CODESEARCHNET ANTES DE INTENTAR CARGARLO
+    prepare_codesearchnet()
+    
+    # 2. Autenticación con Hugging Face
+    hf_token = os.environ.get("HF_TOKEN")
+    if hf_token:
+        login(token=hf_token)
+
+    # 3. Carga del Tokenizer y Modelo Base
+    print("\n🔄 Cargando modelo y tokenizer una sola vez...")
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+    base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, device_map="auto") 
+
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    # 4. Configuración y Aplicación LoRA (PEFT)
+    peft_config = LoraConfig(
+        task_type=TaskType.CAUSAL_LM,
+        r=8,
+        lora_alpha=32,
+        lora_dropout=0.1,
+        target_modules=["c_proj", "c_attn"], 
+    )
+    lora_model = get_peft_model(base_model, peft_config)
+    print(f"✅ Modelo LoRA preparado. Parámetros entrenables: {lora_model.print_trainable_parameters()}")
+
+    # 5. Carga y Tokenización del Dataset
+    print(f"📚 Cargando y tokenizando dataset de: {DATASET_FILE}...")
+    try:
+        raw_dataset = load_dataset("json", data_files=DATASET_FILE)
+        
+        def tokenize_function(examples):
+            return tokenizer(
+                examples["prompt"] + examples["completion"],
+                truncation=True,
+                padding="max_length",
+                max_length=MAX_TOKEN_LENGTH
+            )
+
+        tokenized_dataset = raw_dataset.map(
+            tokenize_function,
+            batched=True,
+            remove_columns=raw_dataset["train"].column_names if "train" in raw_dataset else [],
+        )
+        print("✅ Dataset tokenizado correctamente.")
+    except Exception as e:
+        tokenized_dataset = None
+        print(f"❌ Error al cargar o tokenizar el dataset. {e}")
+
+
+# ============================================================
+# 🧩 FUNCIÓN DE ENTRENAMIENTO
+# ============================================================
+def train_lora(epochs, batch_size, learning_rate):
+    """Ejecuta el entrenamiento del modelo LoRA."""
+    global lora_model, tokenized_dataset, lora_generator
+
+    if tokenized_dataset is None or "train" not in tokenized_dataset:
+        return f"❌ Error: El dataset no pudo cargarse o está vacío. No se puede entrenar."
+
+    try:
+        lora_generator = None 
+        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+        training_args = TrainingArguments(
+            output_dir=LORA_PATH,
+            per_device_train_batch_size=int(batch_size),
+            num_train_epochs=float(epochs),
+            learning_rate=float(learning_rate),
+            save_total_limit=1,
+            logging_steps=10,
+            push_to_hub=False,
+        )
+
+        trainer = Trainer(
+            model=lora_model,
+            args=training_args,
+            train_dataset=tokenized_dataset["train"],
+            data_collator=data_collator,
+        )
+
+        trainer.train()
+        
+        lora_model.save_pretrained(LORA_PATH) 
+        tokenizer.save_pretrained(LORA_PATH) 
+
+        return f"✅ Entrenamiento completado. Adaptadores LoRA guardados en **{LORA_PATH}**"
+    except Exception as e:
+        return f"❌ Error durante el entrenamiento: {e}"
+
+# ============================================================
+# 🤖 FUNCIÓN DE GENERACIÓN (INFERENCIA)
+# ============================================================
+def generate_text(prompt_text):
+    """Genera texto usando el modelo base + adaptadores LoRA."""
+    global lora_generator
+
+    try:
+        if lora_generator is None:
+            base_model_gen = AutoModelForCausalLM.from_pretrained(BASE_MODEL, device_map="auto")
+            
+            if os.path.exists(LORA_PATH):
+                print("Cargando adaptadores LoRA...")
+                model_with_lora = PeftModel.from_pretrained(base_model_gen, LORA_PATH)
+            else:
+                print("No se encontraron adaptadores LoRA. Usando modelo base.")
+                model_with_lora = base_model_gen
+
+            final_model = model_with_lora.merge_and_unload()
+            final_model.eval() 
+            
+            lora_generator = pipeline("text-generation", model=final_model, tokenizer=tokenizer)
+            print("Modelo de inferencia listo.")
+
+        output = lora_generator(prompt_text, max_new_tokens=150, temperature=0.7, top_p=0.9)
+        return output[0]["generated_text"]
+        
+    except Exception as e:
+        return f"❌ Error generando texto (Asegúrate de que el modelo base/LoRA esté cargado): {e}"
+
+# ============================================================
+# 💻 INTERFAZ GRADIO
+# ============================================================
+with gr.Blocks(title="AmorCoderAI - LoRA") as demo:
+    gr.Markdown("# 💙 AmorCoderAI - Entrenamiento y Pruebas LoRA")
+    gr.Markdown(f"Modelo base: `{BASE_MODEL}`. Usando **{NUM_SAMPLES_TO_PROCESS}** ejemplos de CodeSearchNet.")
+
+    with gr.Tab("🧠 Entrenar (Manual)"):
+        gr.Markdown("--- **¡CUIDADO!** El entrenamiento es lento y consume muchos recursos (VRAM/RAM). ---")
+        epochs = gr.Number(value=1, label="Épocas", precision=0)
+        batch_size = gr.Number(value=2, label="Tamaño de lote (ajusta según tu VRAM)", precision=0)
+        learning_rate = gr.Number(value=5e-5, label="Tasa de aprendizaje")
+        train_button = gr.Button("🚀 Iniciar Entrenamiento Manual")
+        train_output = gr.Textbox(label="Resultado del Entrenamiento Manual")
+        
+        train_button.click(
+            train_lora, 
+            inputs=[epochs, batch_size, learning_rate], 
+            outputs=train_output
+        )
+
+    with gr.Tab("✨ Probar modelo"):
+        prompt = gr.Textbox(label="Escribe código (ej: 'def fibonacci(n):')", lines=4)
+        generate_button = gr.Button("💬 Generar código")
+        output_box = gr.Textbox(label="Salida generada", lines=10)
+        generate_button.click(generate_text, inputs=prompt, outputs=output_box)
+
+# ============================================================
+# 🚀 LANZAR APP Y AUTO-ENTRENAMIENTO
+# ============================================================
+if __name__ == "__main__":
+    setup_resources()
+    
+    print("\n=============================================")
+    print(f"🤖 INICIANDO AUTO-ENTRENAMIENTO (1 Época, 2 Batch Size) usando {NUM_SAMPLES_TO_PROCESS} ejemplos")
+    print("=============================================")
+    
+    auto_train_result = train_lora(epochs=1, batch_size=2, learning_rate=5e-5) 
+    
+    print(f"\nFIN DEL AUTO-ENTRENAMIENTO: {auto_train_result}")
+    
+    print("\n=============================================")
+    print("💻 LANZANDO INTERFAZ GRADIO")
+    print("=============================================")
+    demo.launch()