clear

#!/bin/bash
# Define color variables

BLACK=`tput setaf 0`
RED=`tput setaf 1`
GREEN=`tput setaf 2`
YELLOW=`tput setaf 3`
BLUE=`tput setaf 4`
MAGENTA=`tput setaf 5`
CYAN=`tput setaf 6`
WHITE=`tput setaf 7`

BG_BLACK=`tput setab 0`
BG_RED=`tput setab 1`
BG_GREEN=`tput setab 2`
BG_YELLOW=`tput setab 3`
BG_BLUE=`tput setab 4`
BG_MAGENTA=`tput setab 5`
BG_CYAN=`tput setab 6`
BG_WHITE=`tput setab 7`

BOLD=`tput bold`
RESET=`tput sgr0`

# Array of color codes excluding black and white
TEXT_COLORS=($RED $GREEN $YELLOW $BLUE $MAGENTA $CYAN)
BG_COLORS=($BG_RED $BG_GREEN $BG_YELLOW $BG_BLUE $BG_MAGENTA $BG_CYAN)

# Pick random colors
RANDOM_TEXT_COLOR=${TEXT_COLORS[$RANDOM % ${#TEXT_COLORS[@]}]}
RANDOM_BG_COLOR=${BG_COLORS[$RANDOM % ${#BG_COLORS[@]}]}

# Function to prompt user to check their progress
function check_progress {
    while true; do
        echo
        echo -n "${BOLD}${YELLOW}Have you created lab-invoice-parser Processor? (Y/N): ${RESET}"
        read -r user_input
        if [[ "$user_input" == "Y" || "$user_input" == "y" ]]; then
            echo
            echo "${BOLD}${CYAN}Great! Proceeding to the next steps...${RESET}"
            echo
            break
        elif [[ "$user_input" == "N" || "$user_input" == "n" ]]; then
            echo
            echo "${BOLD}${RED}Please create Processor named lab-invoice-parser and then press Y to continue.${RESET}"
        else
            echo
            echo "${BOLD}${MAGENTA}Invalid input. Please enter Y or N.${RESET}"
        fi
    done
}

#----------------------------------------------------start--------------------------------------------------#

echo "${RANDOM_BG_COLOR}${RANDOM_TEXT_COLOR}${BOLD}Starting Execution${RESET}"

# Step 1: Set up project details
echo "${MAGENTA}${BOLD}Set up project details...${RESET}"
PROJECT_ID=$(gcloud config get-value project)
TOKEN=$(gcloud auth application-default print-access-token)

# Step 2: Enable Document AI API
echo "${BLUE}${BOLD}Enabling Document AI API...${RESET}"
gcloud services enable documentai.googleapis.com

# Step 3: Install required packages
echo "${GREEN}${BOLD}Installing required Python packages...${RESET}"
pip3 install --upgrade pandas

pip3 install --upgrade google-cloud-documentai

echo

# Step 4: Prompt user to create Processor
echo "${BLUE}${BOLD}Click here to create Processor:${RESET}" "https://console.cloud.google.com/ai/document-ai/processor-library?project=$PROJECT_ID"

echo

check_progress

echo

# Step 5: Retrieve Processor ID
echo "${MAGENTA}${BOLD}Retrieving Processor ID...${RESET}"
PROCESSOR_ID=$(curl -X GET \
  -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
  -H "Content-Type: application/json" \
  "https://documentai.googleapis.com/v1/projects/$PROJECT_ID/locations/us/processors" | \
  grep '"name":' | \
  sed -E 's/.*"name": "projects\/[0-9]+\/locations\/us\/processors\/([^"]+)".*/\1/')

export PROCESSOR_ID

# Step 6: Download sample documents
echo "${CYAN}${BOLD}Downloading sample documents...${RESET}"
gcloud storage cp gs://cloud-samples-data/documentai/codelabs/specialized-processors/procurement_multi_document.pdf .

gcloud storage cp gs://cloud-samples-data/documentai/codelabs/specialized-processors/google_invoice.pdf .

# Step 7: Create the extraction script
echo "${RED}${BOLD}Creating extraction script...${RESET}"
cat > extraction.py <<EOF_END
import pandas as pd
from google.cloud import documentai_v1 as documentai


def online_process(
    project_id: str,
    location: str,
    processor_id: str,
    file_path: str,
    mime_type: str,
) -> documentai.Document:
    """
    Processes a document using the Document AI Online Processing API.
    """

    opts = {"api_endpoint": f"{location}-documentai.googleapis.com"}

    # Instantiates a client
    documentai_client = documentai.DocumentProcessorServiceClient(client_options=opts)

    # The full resource name of the processor, e.g.:
    # projects/project-id/locations/location/processor/processor-id
    # You must create new processors in the Cloud Console first
    resource_name = documentai_client.processor_path(project_id, location, processor_id)

    # Read the file into memory
    with open(file_path, "rb") as file:
        file_content = file.read()

    # Load Binary Data into Document AI RawDocument Object
    raw_document = documentai.RawDocument(content=file_content, mime_type=mime_type)

    # Configure the process request
    request = documentai.ProcessRequest(name=resource_name, raw_document=raw_document)

    # Use the Document AI client to process the sample form
    result = documentai_client.process_document(request=request)

    return result.document


PROJECT_ID = "$PROJECT_ID"
LOCATION = "us"  # Format is 'us' or 'eu'
PROCESSOR_ID = "$PROCESSOR_ID"  # Create processor in Cloud Console

# The local file in your current working directory
FILE_PATH = "google_invoice.pdf"
# Refer to https://cloud.google.com/document-ai/docs/processors-list
# for supported file types
MIME_TYPE = "application/pdf"

document = online_process(
    project_id=PROJECT_ID,
    location=LOCATION,
    processor_id=PROCESSOR_ID,
    file_path=FILE_PATH,
    mime_type=MIME_TYPE,
)

types = []
raw_values = []
normalized_values = []
confidence = []

# Grab each key/value pair and their corresponding confidence scores.
for entity in document.entities:
    types.append(entity.type_)
    raw_values.append(entity.mention_text)
    normalized_values.append(entity.normalized_value.text)
    confidence.append(f"{entity.confidence:.0%}")

    # Get Properties (Sub-Entities) with confidence scores
    for prop in entity.properties:
        types.append(prop.type_)
        raw_values.append(prop.mention_text)
        normalized_values.append(prop.normalized_value.text)
        confidence.append(f"{prop.confidence:.0%}")

# Create a Pandas Dataframe to print the values in tabular format.
df = pd.DataFrame(
    {
        "Type": types,
        "Raw Value": raw_values,
        "Normalized Value": normalized_values,
        "Confidence": confidence,
    }
)

print(df)
EOF_END

# Step 8: Run the extraction script
echo "${BLUE}${BOLD}Running the extraction script...${RESET}"
python3 extraction.py

# Step 9: Create and upload outputs to a GCS bucket
echo "${GREEN}${BOLD}Creating and uploading outputs to a GCS bucket...${RESET}"
# Create a bucket
gsutil mb gs://$PROJECT_ID-docai

# Create and upload the file
python3 extraction.py > docai_outputs.txt
gsutil cp docai_outputs.txt gs://$PROJECT_ID-docai

echo

# Function to display a random congratulatory message
function random_congrats() {
    MESSAGES=(
        "${GREEN}Congratulations For Completing The Lab! Keep up the great work!${RESET}"
        "${CYAN}Well done! Your hard work and effort have paid off!${RESET}"
        "${YELLOW}Amazing job! You’ve successfully completed the lab!${RESET}"
        "${BLUE}Outstanding! Your dedication has brought you success!${RESET}"
        "${MAGENTA}Great work! You’re one step closer to mastering this!${RESET}"
        "${RED}Fantastic effort! You’ve earned this achievement!${RESET}"
        "${CYAN}Congratulations! Your persistence has paid off brilliantly!${RESET}"
        "${GREEN}Bravo! You’ve completed the lab with flying colors!${RESET}"
        "${YELLOW}Excellent job! Your commitment is inspiring!${RESET}"
        "${BLUE}You did it! Keep striving for more successes like this!${RESET}"
        "${MAGENTA}Kudos! Your hard work has turned into a great accomplishment!${RESET}"
        "${RED}You’ve smashed it! Completing this lab shows your dedication!${RESET}"
        "${CYAN}Impressive work! You’re making great strides!${RESET}"
        "${GREEN}Well done! This is a big step towards mastering the topic!${RESET}"
        "${YELLOW}You nailed it! Every step you took led you to success!${RESET}"
        "${BLUE}Exceptional work! Keep this momentum going!${RESET}"
        "${MAGENTA}Fantastic! You’ve achieved something great today!${RESET}"
        "${RED}Incredible job! Your determination is truly inspiring!${RESET}"
        "${CYAN}Well deserved! Your effort has truly paid off!${RESET}"
        "${GREEN}You’ve got this! Every step was a success!${RESET}"
        "${YELLOW}Nice work! Your focus and effort are shining through!${RESET}"
        "${BLUE}Superb performance! You’re truly making progress!${RESET}"
        "${MAGENTA}Top-notch! Your skill and dedication are paying off!${RESET}"
        "${RED}Mission accomplished! This success is a reflection of your hard work!${RESET}"
        "${CYAN}You crushed it! Keep pushing towards your goals!${RESET}"
        "${GREEN}You did a great job! Stay motivated and keep learning!${RESET}"
        "${YELLOW}Well executed! You’ve made excellent progress today!${RESET}"
        "${BLUE}Remarkable! You’re on your way to becoming an expert!${RESET}"
        "${MAGENTA}Keep it up! Your persistence is showing impressive results!${RESET}"
        "${RED}This is just the beginning! Your hard work will take you far!${RESET}"
        "${CYAN}Terrific work! Your efforts are paying off in a big way!${RESET}"
        "${GREEN}You’ve made it! This achievement is a testament to your effort!${RESET}"
        "${YELLOW}Excellent execution! You’re well on your way to mastering the subject!${RESET}"
        "${BLUE}Wonderful job! Your hard work has definitely paid off!${RESET}"
        "${MAGENTA}You’re amazing! Keep up the awesome work!${RESET}"
        "${RED}What an achievement! Your perseverance is truly admirable!${RESET}"
        "${CYAN}Incredible effort! This is a huge milestone for you!${RESET}"
        "${GREEN}Awesome! You’ve done something incredible today!${RESET}"
        "${YELLOW}Great job! Keep up the excellent work and aim higher!${RESET}"
        "${BLUE}You’ve succeeded! Your dedication is your superpower!${RESET}"
        "${MAGENTA}Congratulations! Your hard work has brought great results!${RESET}"
        "${RED}Fantastic work! You’ve taken a huge leap forward today!${RESET}"
        "${CYAN}You’re on fire! Keep up the great work!${RESET}"
        "${GREEN}Well deserved! Your efforts have led to success!${RESET}"
        "${YELLOW}Incredible! You’ve achieved something special!${RESET}"
        "${BLUE}Outstanding performance! You’re truly excelling!${RESET}"
        "${MAGENTA}Terrific achievement! Keep building on this success!${RESET}"
        "${RED}Bravo! You’ve completed the lab with excellence!${RESET}"
        "${CYAN}Superb job! You’ve shown remarkable focus and effort!${RESET}"
        "${GREEN}Amazing work! You’re making impressive progress!${RESET}"
        "${YELLOW}You nailed it again! Your consistency is paying off!${RESET}"
        "${BLUE}Incredible dedication! Keep pushing forward!${RESET}"
        "${MAGENTA}Excellent work! Your success today is well earned!${RESET}"
        "${RED}You’ve made it! This is a well-deserved victory!${RESET}"
        "${CYAN}Wonderful job! Your passion and hard work are shining through!${RESET}"
        "${GREEN}You’ve done it! Keep up the hard work and success will follow!${RESET}"
        "${YELLOW}Great execution! You’re truly mastering this!${RESET}"
        "${BLUE}Impressive! This is just the beginning of your journey!${RESET}"
        "${MAGENTA}You’ve achieved something great today! Keep it up!${RESET}"
        "${RED}You’ve made remarkable progress! This is just the start!${RESET}"
    )

    RANDOM_INDEX=$((RANDOM % ${#MESSAGES[@]}))
    echo -e "${BOLD}${MESSAGES[$RANDOM_INDEX]}"
}

# Display a random congratulatory message
random_congrats

echo -e "\n"  # Adding one blank line

cd

remove_files() {
    # Loop through all files in the current directory
    for file in *; do
        # Check if the file name starts with "gsp", "arc", or "shell"
        if [[ "$file" == gsp* || "$file" == arc* || "$file" == shell* ]]; then
            # Check if it's a regular file (not a directory)
            if [[ -f "$file" ]]; then
                # Remove the file and echo the file name
                rm "$file"
                echo "File removed: $file"
            fi
        fi
    done
}

remove_files