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JoshuaCarrera commited on
Commit ·
c221f76
1
Parent(s): 69b7038
Añadir capacidad de debugging mediante hash
Browse files- src/app/app.py +28 -6
src/app/app.py
CHANGED
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@@ -1,8 +1,9 @@
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import io
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import os
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import boto3
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import gc
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import sys
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from PIL import Image
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from botocore.config import Config
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from ultralytics import YOLO
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@@ -32,6 +33,14 @@ model_configs = {
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}
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}
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# ==========================================
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# 2. DESCARGA CONSTRUCTORA Y DESTRUCTIVA (Ahorro de RAM)
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# ==========================================
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@@ -61,9 +70,11 @@ def download_models_from_r2():
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raise e
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else:
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print(f"El modelo {name} ya existe localmente. Cargando...")
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# OPTIMIZACIÓN EXTREMA: Destruir el cliente pesado de Boto3 para liberar RAM
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# Esto evita que el servidor haga SWAP al cargar YOLO
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del s3_client
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gc.collect()
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@@ -71,7 +82,7 @@ def download_models_from_r2():
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download_models_from_r2()
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# ==========================================
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# 3. CARGA DE MODELOS
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# ==========================================
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models = {
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"USD": YOLO(model_configs["USD"]["path"]),
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@@ -122,10 +133,21 @@ app.add_middleware(
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def detection_vef(image: UploadFile):
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print("FOTO RECIBIDA")
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# Flujo exacto de lectura
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imageBytes = image.file.read()
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imageStream = io.BytesIO(imageBytes)
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imageFile = Image.open(imageStream)
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which_currency = models["INFERENCIA"].predict(imageFile, verbose=False, imgsz=640, conf=0.25)
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import io
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import os
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import boto3
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import gc
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import sys
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import hashlib # DEBUG: Importamos hashlib para calcular los MD5
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from PIL import Image
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from botocore.config import Config
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from ultralytics import YOLO
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}
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}
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# DEBUG: Función auxiliar para calcular el MD5 de un archivo en disco
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def calculate_file_md5(filepath): # DEBUG
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hash_md5 = hashlib.md5() # DEBUG
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with open(filepath, "rb") as f: # DEBUG
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for chunk in iter(lambda: f.read(4096), b""): # DEBUG
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hash_md5.update(chunk) # DEBUG
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return hash_md5.hexdigest() # DEBUG
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# ==========================================
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# 2. DESCARGA CONSTRUCTORA Y DESTRUCTIVA (Ahorro de RAM)
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# ==========================================
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raise e
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else:
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print(f"El modelo {name} ya existe localmente. Cargando...")
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# DEBUG: Calculamos e imprimimos el MD5 del modelo local para verificar integridad
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local_md5 = calculate_file_md5(config["path"]) # DEBUG
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print(f"[DEBUG] MD5 del modelo {name} en disco: {local_md5}") # DEBUG
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del s3_client
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gc.collect()
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download_models_from_r2()
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# ==========================================
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# 3. CARGA DE MODELOS
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# ==========================================
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models = {
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"USD": YOLO(model_configs["USD"]["path"]),
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def detection_vef(image: UploadFile):
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print("FOTO RECIBIDA")
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# Flujo exacto de lectura
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imageBytes = image.file.read()
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# DEBUG: Calcular MD5 de la foto exacta recibida por la red (bytes puros)
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raw_md5 = hashlib.md5(imageBytes).hexdigest() # DEBUG
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print(f"[DEBUG] MD5 de la foto cruda recibida: {raw_md5}") # DEBUG
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imageStream = io.BytesIO(imageBytes)
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imageFile = Image.open(imageStream).convert("RGB")
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# DEBUG: Calcular MD5 de la foto después de ser procesada por Pillow
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debug_buffer = io.BytesIO() # DEBUG
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imageFile.save(debug_buffer, format="JPEG") # DEBUG
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pillow_md5 = hashlib.md5(debug_buffer.getvalue()).hexdigest() # DEBUG
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print(f"[DEBUG] MD5 de la foto tras procesar con Pillow (RGB): {pillow_md5}") # DEBUG
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which_currency = models["INFERENCIA"].predict(imageFile, verbose=False, imgsz=640, conf=0.25)
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