Guardar logs en el s3

src/app/app.py

@@ -3,7 +3,8 @@ import os
 import boto3
 import gc 
 import sys
-import hashlib # DEBUG: Importamos hashlib para calcular los MD5
+import hashlib
+from datetime import datetime, timezone, timedelta
 from PIL import Image
 from botocore.config import Config
 from ultralytics import YOLO
@@ -33,19 +34,19 @@ model_configs = {
     }
 }
 
-# DEBUG: Función auxiliar para calcular el MD5 de un archivo en disco
-def calculate_file_md5(filepath): # DEBUG
-    hash_md5 = hashlib.md5() # DEBUG
-    with open(filepath, "rb") as f: # DEBUG
-        for chunk in iter(lambda: f.read(4096), b""): # DEBUG
-            hash_md5.update(chunk) # DEBUG
-    return hash_md5.hexdigest() # DEBUG
-
 # ==========================================
-# 2. DESCARGA CONSTRUCTORA Y DESTRUCTIVA (Ahorro de RAM)
+# 2. FUNCIONES AUXILIARES
 # ==========================================
-def download_models_from_r2():
-    s3_client = boto3.client(
+def calculate_file_md5(filepath): 
+    hash_md5 = hashlib.md5() 
+    with open(filepath, "rb") as f: 
+        for chunk in iter(lambda: f.read(4096), b""): 
+            hash_md5.update(chunk) 
+    return hash_md5.hexdigest() 
+
+def get_s3_client():
+    """Retorna un cliente s3 configurado para Cloudflare R2"""
+    return boto3.client(
         service_name="s3",
         endpoint_url=R2_ENDPOINT_URL,
         aws_access_key_id=R2_ACCESS_KEY,
@@ -53,6 +54,12 @@ def download_models_from_r2():
         config=Config(signature_version="s3v4")
     )
 
+# ==========================================
+# 3. DESCARGA CONSTRUCTORA Y DESTRUCTIVA
+# ==========================================
+def download_models_from_r2():
+    s3_client = get_s3_client()
+
     for name, config in model_configs.items():
         os.makedirs(os.path.dirname(config["path"]), exist_ok=True)
         
@@ -71,18 +78,16 @@ def download_models_from_r2():
         else:
             print(f"El modelo {name} ya existe localmente. Cargando...")
         
-        # DEBUG: Calculamos e imprimimos el MD5 del modelo local para verificar integridad
-        local_md5 = calculate_file_md5(config["path"]) # DEBUG
-        print(f"[DEBUG] MD5 del modelo {name} en disco: {local_md5}") # DEBUG
+        local_md5 = calculate_file_md5(config["path"])
+        print(f"[DEBUG] MD5 del modelo {name} en disco: {local_md5}")
 
     del s3_client
     gc.collect()
 
-# Ejecutamos la descarga antes de cargar YOLO
 download_models_from_r2()
 
 # ==========================================
-# 3. CARGA DE MODELOS
+# 4. CARGA DE MODELOS
 # ==========================================
 models = {
     "USD":        YOLO(model_configs["USD"]["path"]),
@@ -90,12 +95,6 @@ models = {
     "INFERENCIA": YOLO(model_configs["INFERENCIA"]["path"]),
 }
 
-versions = {
-    "USD": "plus_01",
-    "VEF": "13f",
-    "INFERENCIA": "1",
-}
-
 classes = {
     "USD": [
         "fifty-back",        "fifty-front", 
@@ -133,21 +132,18 @@ app.add_middleware(
 def detection_vef(image: UploadFile):
     print("FOTO RECIBIDA")
     
-    # Flujo exacto de lectura
     imageBytes = image.file.read()
     
-    # DEBUG: Calcular MD5 de la foto exacta recibida por la red (bytes puros)
-    raw_md5 = hashlib.md5(imageBytes).hexdigest() # DEBUG
-    print(f"[DEBUG] MD5 de la foto cruda recibida: {raw_md5}") # DEBUG
+    raw_md5 = hashlib.md5(imageBytes).hexdigest()
+    print(f"[DEBUG] MD5 de la foto cruda recibida: {raw_md5}") 
     
     imageStream = io.BytesIO(imageBytes)
     imageFile = Image.open(imageStream).convert("RGB")
 
-    # DEBUG: Calcular MD5 de la foto después de ser procesada por Pillow
-    debug_buffer = io.BytesIO() # DEBUG
-    imageFile.save(debug_buffer, format="JPEG") # DEBUG
-    pillow_md5 = hashlib.md5(debug_buffer.getvalue()).hexdigest() # DEBUG
-    print(f"[DEBUG] MD5 de la foto tras procesar con Pillow (RGB): {pillow_md5}") # DEBUG
+    debug_buffer = io.BytesIO() 
+    imageFile.save(debug_buffer, format="JPEG") 
+    pillow_md5 = hashlib.md5(debug_buffer.getvalue()).hexdigest()
+    print(f"[DEBUG] MD5 de la foto tras procesar con Pillow (RGB): {pillow_md5}") 
 
     which_currency = models["INFERENCIA"].predict(imageFile, verbose=False, imgsz=640, conf=0.25)
     
@@ -164,6 +160,35 @@ def detection_vef(image: UploadFile):
     results = models[currency].predict(imageFile, verbose=False, imgsz=640, conf=0.25)
 
     if len(results[0].boxes) > 0:
+        # Tomamos el label y la confianza de la primera detección (la de mayor confianza usualmente)
+        primary_label = classes[currency][int(results[0].boxes[0].cls.item())]
+        primary_conf = results[0].boxes[0].conf.item() # <--- Extraemos la confianza
+        
+        # -----------------------------------------------------
+        # LÓGICA DE GUARDADO EN R2 (LOGS)
+        # -----------------------------------------------------
+        try:
+            # Zona horaria de Venezuela (UTC-4)
+            vzla_tz = timezone(timedelta(hours=-4))
+            now = datetime.now(vzla_tz)
+            
+            # Formato: DD-MM-YYYY_HHMMSS_<label>_<confianza>.jpg
+            # Usamos :.4f para limitar la confianza a 4 decimales (ej. 0.9876)
+            filename = f"{now.strftime('%d-%m-%Y_%H%M%S')}_{primary_label}_{primary_conf:.4f}.jpg"
+            r2_key = f"logs/{filename}"
+            
+            s3_client = get_s3_client()
+            s3_client.put_object(
+                Bucket=R2_BUCKET_NAME,
+                Key=r2_key,
+                Body=imageBytes, # Usamos los bytes originales para no perder calidad
+                ContentType="image/jpeg"
+            )
+            print(f"[LOG] Imagen respaldada en R2: {r2_key}")
+        except Exception as e:
+            print(f"❌ [ERROR] Fallo al subir la imagen a logs en R2: {e}")
+        # -----------------------------------------------------
+
         boxes = [
             {
                 "label": classes[currency][int(box.cls.item())],