Address PR #31 review comments

090-comprehend: Replace placeholder 'Your text here' with interesting example text
103-textract: Lead with local file method, S3 as secondary option
103-textract: Script checks for prereq bucket stack before creating one
105-rekognition: Use repo sample image, remove Python image generation step
sample-images/: Add placeholder images (to be replaced with real samples)

Author: mwunderl

sample-images/README.md

@@ -0,0 +1,10 @@
+# Sample Images
+
+Placeholder images for tutorials that process images (Textract, Rekognition, etc.).
+
+Replace these with real sample images before publishing.
+
+| File | Used by | Description |
+|------|---------|-------------|
+| sample-document.png | 103-amazon-textract-gs | Document image for text extraction |
+| sample-photo.png | 105-amazon-rekognition-gs | Photo for label detection |

sample-images/sample-document.png

@@ -10,14 +10,14 @@ This tutorial shows you how to use the Amazon Comprehend real-time analysis APIs
 ## Step 1: Detect the dominant language
 
 ```bash
-aws comprehend detect-dominant-language --text "Your text here" \
+aws comprehend detect-dominant-language --text "Amazon Web Services provides cloud computing services that help businesses scale and innovate faster. Customers love the reliability and breadth of services available." \
     --query 'Languages[0].{Language:LanguageCode,Confidence:Score}' --output table
 ```
 
 ## Step 2: Detect sentiment
 
 ```bash
-aws comprehend detect-sentiment --text "Your text here" --language-code en \
+aws comprehend detect-sentiment --text "Amazon Web Services provides cloud computing services that help businesses scale and innovate faster. Customers love the reliability and breadth of services available." --language-code en \
     --query '{Sentiment:Sentiment,Positive:SentimentScore.Positive,Negative:SentimentScore.Negative}' --output table
 ```
 
@@ -26,14 +26,14 @@ aws comprehend detect-sentiment --text "Your text here" --language-code en \
 Identifies people, places, organizations, dates, and other entity types.
 
 ```bash
-aws comprehend detect-entities --text "Your text here" --language-code en \
+aws comprehend detect-entities --text "Amazon Web Services provides cloud computing services that help businesses scale and innovate faster. Customers love the reliability and breadth of services available." --language-code en \
     --query 'Entities[].{Text:Text,Type:Type,Score:Score}' --output table
 ```
 
 ## Step 4: Detect key phrases
 
 ```bash
-aws comprehend detect-key-phrases --text "Your text here" --language-code en \
+aws comprehend detect-key-phrases --text "Amazon Web Services provides cloud computing services that help businesses scale and innovate faster. Customers love the reliability and breadth of services available." --language-code en \
     --query 'KeyPhrases[].{Text:Text,Score:Score}' --output table
 ```
 

sample-images/sample-photo.png

@@ -8,34 +8,30 @@ This tutorial shows you how to upload a document image to Amazon S3, use Amazon
 - Python 3 installed (used to generate a sample PNG image)
 - Permissions for `s3:CreateBucket`, `s3:PutObject`, `s3:DeleteObject`, `s3:DeleteBucket`, `textract:DetectDocumentText`, and `textract:AnalyzeDocument`
 
-## Step 1: Create a sample document image
+## Step 1: Use the sample document image
 
-Generate a minimal PNG image to use as a test document. In practice, you would use a scanned document or photograph containing text.
+This tutorial includes a sample document image at `../../sample-images/sample-document.png`. Copy it to your working directory:
+
+```
+cp ../../sample-images/sample-document.png sample.png
+```
+
+## Step 2: Detect text from local file bytes
+
+Send the document directly as base64-encoded bytes instead of referencing S3.
 
 ```bash
-WORK_DIR=$(mktemp -d)
-
-python3 -c "
-import struct, zlib
-w,h=200,50
-row=b'\x00'+b'\xff\xff\xff'*w
-raw=row*h
-comp=zlib.compress(raw)
-def ch(t,d):
-    c=t+d
-    return struct.pack('>I',len(d))+c+struct.pack('>I',zlib.crc32(c)&0xffffffff)
-with open('$WORK_DIR/sample.png','wb') as f:
-    f.write(b'\x89PNG\r\n\x1a\n')
-    f.write(ch(b'IHDR',struct.pack('>IIBBBBB',w,h,8,2,0,0,0)))
-    f.write(ch(b'IDAT',comp))
-    f.write(ch(b'IEND',b''))
-"
-echo "Created sample.png (200x50 white image)"
+aws textract detect-document-text \
+    --document '{"Bytes":"'"$(base64 -w0 "$WORK_DIR/sample.png")"'"}' \
+    --query '{Pages:DocumentMetadata.Pages,BlockCount:Blocks|length(@)}' --output table
 ```
 
-This creates a blank white PNG. Textract won't find text in it, but it demonstrates the API calls. Replace it with a real document to see text extraction in action.
+The `Bytes` option is useful for quick tests or when you don't want to upload to S3 first. The document size limit for synchronous operations is 10 MB.
+
+## Step 3: Upload to S3 (alternative method)
+
+If you want to use S3 instead of local file bytes, upload the image to a bucket. If the tutorial prereq bucket stack is deployed, use that bucket. Otherwise create one.
 
-## Step 2: Upload the document to S3
 
 Create an S3 bucket and upload the sample image.
 
@@ -52,7 +48,7 @@ echo "Uploaded to s3://$BUCKET_NAME/sample.png"
 
 Textract reads documents directly from S3. For `us-east-1`, omit the `--create-bucket-configuration` parameter.
 
-## Step 3: Detect text in the document
+## Step 4: Detect text from S3 in the document
 
 ```bash
 aws textract detect-document-text \
@@ -62,7 +58,7 @@ aws textract detect-document-text \
 
 `detect-document-text` returns `LINE` and `WORD` blocks. Each block includes the detected text and a confidence score. With the blank sample image, no text lines are returned.
 
-## Step 4: Analyze document for forms and tables
+## Step 5: Analyze document for forms and tables
 
 ```bash
 aws textract analyze-document \
@@ -73,18 +69,6 @@ aws textract analyze-document \
 
 `analyze-document` goes beyond text detection. With `FORMS`, it identifies key-value pairs (like form fields). With `TABLES`, it identifies rows and columns. You can request both features in a single call.
 
-## Step 5: Detect text from local file bytes
-
-Send the document directly as base64-encoded bytes instead of referencing S3.
-
-```bash
-aws textract detect-document-text \
-    --document '{"Bytes":"'"$(base64 -w0 "$WORK_DIR/sample.png")"'"}' \
-    --query '{Pages:DocumentMetadata.Pages,BlockCount:Blocks|length(@)}' --output table
-```
-
-The `Bytes` option is useful for quick tests or when you don't want to upload to S3 first. The document size limit for synchronous operations is 10 MB.
-
 ## Cleanup
 
 Delete the S3 bucket and its contents, then remove the temporary directory.

tuts/090-amazon-comprehend-gs/amazon-comprehend-gs.md

@@ -16,7 +16,16 @@ ACCOUNT_ID=$(aws sts get-caller-identity --query 'Account' --output text)
 echo "Region: $REGION"
 
 RANDOM_ID=$(cat /dev/urandom | tr -dc 'a-z0-9' | fold -w 8 | head -n 1)
-BUCKET_NAME="textract-tut-${RANDOM_ID}-${ACCOUNT_ID}"
+# Check for prereq bucket stack first
+PREREQ_BUCKET=$(aws cloudformation describe-stacks --stack-name tutorial-prereqs-bucket     --query 'Stacks[0].Outputs[?OutputKey==`BucketName`].OutputValue' --output text 2>/dev/null)
+if [ -n "$PREREQ_BUCKET" ] && [ "$PREREQ_BUCKET" != "None" ]; then
+    BUCKET_NAME="$PREREQ_BUCKET"
+    BUCKET_IS_PREREQ=true
+    echo "Using prereq bucket: $BUCKET_NAME"
+else
+    BUCKET_NAME="textract-tut-${RANDOM_ID}-${ACCOUNT_ID}"
+    BUCKET_IS_PREREQ=false
+fi
 
 handle_error() { echo "ERROR on line $1"; trap - ERR; cleanup; exit 1; }
 trap 'handle_error $LINENO' ERR

tuts/103-amazon-textract-gs/amazon-textract-gs.md

@@ -7,35 +7,15 @@ In this tutorial, you use the AWS CLI to analyze images with Amazon Rekognition.
 ## Prerequisites
 
 - AWS CLI installed and configured with appropriate permissions.
-- Python 3 (to generate the sample image).
 - An IAM principal with permissions for `rekognition:DetectLabels`, `rekognition:DetectText`, `s3:CreateBucket`, `s3:PutObject`, `s3:DeleteObject`, and `s3:DeleteBucket`.
 
-## Step 1: Create a sample image
+## Step 1: Use the sample image
 
-Generate a 100×100 gradient PNG using Python. The gradient gives Rekognition color data to analyze.
+This tutorial includes a sample photo at `../../sample-images/sample-photo.png`. Copy it to your working directory:
 
-```bash
-python3 -c "
-import struct, zlib
-w,h=100,100
-rows=b''
-for y in range(h):
-    rows+=b'\x00'
-    for x in range(w):
-        rows+=bytes([int(x*2.55), int(y*2.55), 128])
-comp=zlib.compress(rows)
-def ch(t,d):
-    c=t+d
-    return struct.pack('>I',len(d))+c+struct.pack('>I',zlib.crc32(c)&0xffffffff)
-with open('sample.png','wb') as f:
-    f.write(b'\x89PNG\r\n\x1a\n')
-    f.write(ch(b'IHDR',struct.pack('>IIBBBBB',w,h,8,2,0,0,0)))
-    f.write(ch(b'IDAT',comp))
-    f.write(ch(b'IEND',b''))
-"
 ```
-
-This creates a minimal valid PNG without any external dependencies.
+cp ../../sample-images/sample-photo.png sample.png
+```
 
 ## Step 2: Upload to S3