Update file

app.py

@@ -42,7 +42,7 @@ import tempfile
 import io
 from PIL import Image
 from pandas import json_normalize
-# Images
+# Imagenes por defecto para HF
 torch.hub.download_url_to_file('https://i.pinimg.com/564x/18/0b/00/180b00e454362ff5caabe87d9a763a6f.jpg', 'ejemplo1.jpg')
 torch.hub.download_url_to_file('https://i.pinimg.com/564x/3b/2f/d4/3b2fd4b6881b64429f208c5f32e5e4be.jpg', 'ejemplo2.jpg')
 
@@ -50,14 +50,14 @@ aws_access_key_id = os.environ['aws_access_key_id']
 aws_secret_access_key = os.environ['aws_secret_access_key'] 
 region = os.environ['region'] 
 
-#Lista Json y Dataframe
+#Elimina espacios en blanco
 def removeStr(string): 
   return string.replace(" ", "")
 
+#Informacion del Dataframe a partir del json
 def arrayListax(json_data):
   dict = json_data
   df2 = json_normalize(dict['detail']) 
-  #df = pd.DataFrame(df2,columns=['Cantidad','Especie'])
   return df2
 
 #Imagen temporal guardada en upload_file
@@ -78,14 +78,14 @@ def upload_file(file_name, bucket=None, object_name=None):
   :param object_name: S3 object name. If not specified then file_name is used
   :return: Json if file was uploaded, else False
   """
-  # If S3 object_name was not specified, use file_name
+  # Si S3 object_name no esta especificado, usa file_name
   if object_name is None:
     object_name = os.path.basename(file_name+".jpg")
   if bucket is None:
     bucket = 'oceanapp'
   s3_client = boto3.client('s3',aws_access_key_id=aws_access_key_id,aws_secret_access_key=aws_secret_access_key)
   aws_region = boto3.session.Session().region_name
-  # Upload the file
+  # Subida del archivo
   try:
     with open(file_name, "rb") as f:
       response = s3_client.upload_fileobj(f, bucket, object_name)
@@ -100,6 +100,7 @@ def upload_file(file_name, bucket=None, object_name=None):
     return False
   return stado
 
+# Contador de los resultados del modelo
 def qtyEspecies(datax, datay, resImg):
   numLobos = 0
   numPelicanos = 0
@@ -114,8 +115,7 @@ def qtyEspecies(datax, datay, resImg):
   json_data = json.loads(data)
   return json_data
 
-
-# Model
+# Modelo Yolov5x
 model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True, autoshape=True)  # local model  o google colab
 
 def yolo(size, iou, conf, im):
@@ -130,18 +130,18 @@ def yolo(size, iou, conf, im):
       model.conf = conf
       # inference 
       results2 = model(im)  
-      # updates results.imgs with boxes and labels
+      # actualiza el results.imgs con boxes y labels
       results2.render()  
       results3 = str(results2)
-      #Contador de especies
+      # contador de especies
       results5=results2.pandas().xyxy[0].sort_values('name')
-      #Transformando la img en bytes
+      # transforma la img en bytes
       pil_im = Image.fromarray(results2.ims[0])
       b = io.BytesIO()
       pil_im.save(b, 'jpeg')
       im_bytes = b.getvalue()
       fileImg = tempFileJSON(im_bytes)
-      #Enviando la informacion al contador de especies
+      #Envia la informacion al contador de especies
       results6 = qtyEspecies(results5,results3,fileImg)
       lista2 = arrayListax(results6)
       return Image.fromarray(results2.ims[0]), lista2, results6