Update app.py

app.py

@@ -1,13 +1,105 @@
-runtime error
-Exit code: 1. Reason:   File "/home/user/app/app.py", line 3
-    def fibonacci(n):Ap p.KZ.A pBApp ppvertApKert ppvAADB_ZA. pr prtoolsAppP.ADB view::EmKDBEmABP.A.ValuesEmBase.b pDB_comAppDBKtoolsDBAADB.BView.ScriptsDBAvalertCurrentDBKDBABDBAAImpl viewABaseDBCurrentBImplDBBDB.b::DBBaseDBDBADBBviewb::DB.DBA.ert.A_BaseValuesADB.DBADBDBDBBDBImplDB_DBAerDBDBABaseDB.privateDBImplBaseEmBaseDBBase::DBADBB
-                        ^
-SyntaxError: invalid syntax
-Container logs:
-
-===== Application Startup at 2025-10-21 06:08:54 =====
-
-  File "/home/user/app/app.py", line 3
-    def fibonacci(n):Ap p.KZ.A pBApp ppvertApKert ppvAADB_ZA. pr prtoolsAppP.ADB view::EmKDBEmABP.A.ValuesEmBase.b pDB_comAppDBKtoolsDBAADB.BView.ScriptsDBAvalertCurrentDBKDBABDBAAImpl viewABaseDBCurrentBImplDBBDB.b::DBBaseDBDBADBBviewb::DB.DBA.ert.A_BaseValuesADB.DBADBDBDBBDBImplDB_DBAerDBDBABaseDB.privateDBImplBaseEmBaseDBBase::DBADBB
-                        ^
-SyntaxError: invalid syntax
+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      
+DEFAULT_EPOCHS = 5 # <--- ¡ENTRENAMIENTO PROFUNDO!
+
+# 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, 
+            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}")
+        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)