Changes to get Flask tutorial to run

Author: Seth Weidman

intermediate_source/flask_rest_api_tutorial.py

@@ -132,10 +132,10 @@ def transform_image(image_bytes):
 ######################################################################
 # Above method takes image data in bytes, applies the series of transforms
 # and returns a tensor. To test the above method, read an image file in
-# bytes mode (first replacing `<PATH/TO/.jpeg/FILE>` with the actual path
-# to the file on your computer) and see if you get a tensor back:
+# bytes mode (first replacing `../_static/img/sample_file.jpeg` with the actual
+# path to the file on your computer) and see if you get a tensor back:
 
-with open("<PATH/TO/.jpeg/FILE>/sample_file.jpeg", 'rb') as f:
+with open("../_static/img/sample_file.jpeg", 'rb') as f:
     image_bytes = f.read()
     tensor = transform_image(image_bytes=image_bytes)
     print(tensor)
@@ -169,15 +169,16 @@ def get_prediction(image_bytes):
 # The tensor ``y_hat`` will contain the index of the predicted class id.
 # However, we need a human readable class name. For that we need a class id
 # to name mapping. Download
-# `this file <https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json>`_ 
-# as ``imagenet_class_index.json`` and remember where you saved it.
-# This file contains the mapping of ImageNet class id to ImageNet class
-# name. We will load this JSON file and get the class name of the
-# predicted index.
+# `this file <https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json>`_
+# as ``imagenet_class_index.json`` and remember where you saved it (or, if you
+# are following the exact steps in this tutorial, save it in
+# `tutorials/_static`). This file contains the mapping of ImageNet class id to
+# ImageNet class name. We will load this JSON file and get the class name of
+# the predicted index.
 
 import json
 
-imagenet_class_index = json.load(open('<PATH/TO/.json/FILE>/imagenet_class_index.json'))
+imagenet_class_index = json.load(open('../_static/imagenet_class_index.json'))
 
 def get_prediction(image_bytes):
     tensor = transform_image(image_bytes=image_bytes)
@@ -194,7 +195,7 @@ def get_prediction(image_bytes):
 # We will test our above method:
 
 
-with open("<PATH/TO/.jpeg/FILE>/sample_file.jpeg", 'rb') as f:
+with open("../_static/img/sample_file.jpeg", 'rb') as f:
     image_bytes = f.read()
     print(get_prediction(image_bytes=image_bytes))
 
@@ -229,7 +230,7 @@ def get_prediction(image_bytes):
 # method to read files from the requests:
 #
 # .. code-block:: python
-#  
+#
 #    from flask import request
 #
 #    @app.route('/predict', methods=['POST'])
@@ -243,25 +244,26 @@ def get_prediction(image_bytes):
 #            return jsonify({'class_id': class_id, 'class_name': class_name})
 
 ######################################################################
-# The ``app.py`` file is now complete. Following is the full version:
+# The ``app.py`` file is now complete. Following is the full version; replace
+# the paths with the paths where you saved your files and it should run:
 #
 # .. code-block:: python
-#  
+#
 #   import io
 #   import json
-#   
+#
 #   from torchvision import models
 #   import torchvision.transforms as transforms
 #   from PIL import Image
 #   from flask import Flask, jsonify, request
-#   
-#   
+#
+#
 #   app = Flask(__name__)
 #   imagenet_class_index = json.load(open('<PATH/TO/.json/FILE>/imagenet_class_index.json'))
 #   model = models.densenet121(pretrained=True)
 #   model.eval()
-#   
-#   
+#
+#
 #   def transform_image(image_bytes):
 #       my_transforms = transforms.Compose([transforms.Resize(255),
 #                                           transforms.CenterCrop(224),
@@ -271,25 +273,25 @@ def get_prediction(image_bytes):
 #                                               [0.229, 0.224, 0.225])])
 #       image = Image.open(io.BytesIO(image_bytes))
 #       return my_transforms(image).unsqueeze(0)
-#   
-#   
+#
+#
 #   def get_prediction(image_bytes):
 #       tensor = transform_image(image_bytes=image_bytes)
 #       outputs = model.forward(tensor)
 #       _, y_hat = outputs.max(1)
 #       predicted_idx = str(y_hat.item())
 #       return imagenet_class_index[predicted_idx]
-#   
-#   
+#
+#
 #   @app.route('/predict', methods=['POST'])
 #   def predict():
 #       if request.method == 'POST':
 #           file = request.files['file']
 #           img_bytes = file.read()
 #           class_id, class_name = get_prediction(image_bytes=img_bytes)
 #           return jsonify({'class_id': class_id, 'class_name': class_name})
-#   
-#   
+#
+#
 #   if __name__ == '__main__':
 #       app.run()
 
@@ -301,15 +303,15 @@ def get_prediction(image_bytes):
 #     $ FLASK_ENV=development FLASK_APP=app.py flask run
 
 #######################################################################
-# We can use the 
-# `requests <https://pypi.org/project/requests/>`_   
+# We can use the
+# `requests <https://pypi.org/project/requests/>`_
 # library to send a POST request to our app:
 #
 # .. code-block:: python
-#  
+#
 #   import requests
-# 
-#   resp = requests.post("http://localhost:5000/predict", 
+#
+#   resp = requests.post("http://localhost:5000/predict",
 #                        files={"file": open('<PATH/TO/.jpg/FILE>/cat.jpg','rb')})
 
 #######################################################################