Update app aws

app.py

@@ -29,31 +29,70 @@ Este Notebook se acelera opcionalmente con un entorno de ejecución de GPU
 # Código
 """
 
-#!pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt gradio # install dependencies
-
 import gradio as gr
 import pandas as pd
 import torch
 import logging
 import json
 import re
+import os 
+import boto3
+from botocore.exceptions import NoCredentialsError
+import tempfile
+import io
 from PIL import Image
-
 # Images
 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')
 
-# Model
-#model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # force_reload=True to update
-
-#model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')  # local model  o google colab
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True, autoshape=True)  # local model  o google colab
-#model = torch.hub.load('path/to/yolov5', 'custom', path='/content/yolov56.pt', source='local')  # local repo
+# Envio de imagenes a S3
+def upload_file(file_name, bucket=None, object_name=None):
+    """Upload a file to an S3 bucket
+
+    :param file_name: File to upload
+    :param bucket: Bucket to upload to
+    :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
+    if object_name is None:
+        object_name = os.path.basename(file_name+".jpg")
+    if bucket is None:
+        bucket = 'oceanapp'
+    s3_client = boto3.client('s3', os.environ['aws_access_key_id'],os.environ['aws_secret_access_key'],os.environ['region'])
+    aws_region = boto3.session.Session().region_name
+    # Upload the file
+    try:
+        with open(file_name, "rb") as f:
+          response = s3_client.upload_fileobj(f, bucket, object_name)
+          s3_url = f"https://{bucket}.s3.{aws_region}.amazonaws.com/{object_name}"
+          stado = '"url_details":[{"statusCode":200, "s3_url":"'+s3_url+'"}]'
+        print(s3_url)
+    except FileNotFoundError:
+        print("The file was not found")
+        return False
+    except NoCredentialsError as e:
+        logging.error(e)
+        return False
+    return stado
+
+#Imagen temporal guardada en upload_file
+def tempFileJSON(img_file):
+  temp = tempfile.NamedTemporaryFile(mode="wb")
+  with temp as jpg:
+    jpg.write(img_file)
+    print(jpg.name)
+    uf = upload_file(jpg.name)
+    return uf
 
 def removeStr(string): 
     return string.replace(" ", "")
 
-def listJSON(a,b,c,d,e,f):
+# Model
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True, autoshape=True)  # local model  o google colab
+
+def listJSON(a,b,c,d,e,f,resImg):
       x = re.findall("obo mar", d)
       y = re.findall("elica", d)
       z = re.findall("elica", f)
@@ -70,7 +109,7 @@ def listJSON(a,b,c,d,e,f):
           strlista = '"detail":[{"quantity":"'+str(removeStr(c))+'","description":"'+str(d)+'"}]'
         else:
           strlista = '"detail":[{"quantity":"'+str(removeStr(c))+'","description":"'+str(d)+'"},{"quantity":"'+str(removeStr(e))+'","description":"'+str(f)+'"}]'
-        strlist = '{"image":"'+str(removeStr(a))+'","size":"'+str(removeStr(b))+'",'+strlista+'}'
+        strlist = '{"image":"'+str(removeStr(a))+'","size":"'+str(removeStr(b))+'",'+strlista+','+resImg+'}'
         json_string = json.loads(strlist)
         return json_string
 
@@ -98,22 +137,31 @@ def arrayLista(a,b,c,d):
 
 def yolo(size, iou, conf, im):
     try:
-        '''Wrapper fn for gradio'''
-        g = (int(size) / max(im.size))  # gain
-        im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
-
-        model.iou = iou
-        
-        model.conf = conf
-
-        results2 = model(im)  # inference
-        results2.render()  # updates results.imgs with boxes and labels
-        results3 = str(results2)
-        lista = listJSON(results3[0:9], results3[11:18] ,results3[19:21],results3[22:32], results3[34:36], results3[37:45])
-        lista2 = arrayLista(results3[19:21],results3[22:32], results3[34:36], results3[37:45])        
-        return Image.fromarray(results2.ims[0]), lista2, lista
+      '''Wrapper fn for gradio'''
+      # gain
+      g = (int(size) / max(im.size))  
+      # resize
+      im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  
+
+      model.iou = iou
+      
+      model.conf = conf
+      # inference 
+      results2 = model(im)  
+      # updates results.imgs with boxes and labels
+      results2.render()  
+      results3 = str(results2)
+      #Transformando 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)
+      lista = listJSON(results3[0:9], results3[11:18] ,results3[19:21],results3[22:32], results3[34:36], results3[36:45], fileImg)
+      lista2 = arrayLista(results3[19:21],results3[22:32], results3[34:36], results3[37:45])
+      return Image.fromarray(results2.ims[0]), lista2, lista
     except Exception as e:
-        logging.error(e, exc_info=True)
+      logging.error(e, exc_info=True)
 
 #------------ Interface-------------