Update app.py

app.py

@@ -1,240 +1,3 @@
-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()
+# Descripción: Calcula el número de Fibonacci de forma recursiva.
+# Completa la siguiente función:
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