import io
import os

from google.cloud import vision
from google.cloud.vision import types


def detect_document(path):
    """Detects document features in an image."""
    client = vision.ImageAnnotatorClient()

    bounds = []

    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = types.Image(content=content)

    response = client.document_text_detection(image=image)

    for page in response.full_text_annotation.pages:
        for block in page.blocks:
            print('\nBlock confidence: {}\n'.format(block.confidence))

            for par in block.paragraphs:
                print('Paragraph confidence: {}'.format(par.confidence))

                for word in par.words:
                    word_text = ''.join([
                        symbol.text for symbol in word.symbols
                    ])
                    print('Word text: {} (confidence: {})'.format(
                        word_text, word.confidence))

                    for symbol in word.symbols:
                        print('\tSymbol: {} (confidence: {})'.format(
                            symbol.text, symbol.confidence))

            for word in block.words:
                word_text = ''.join([
                    symbol.text for symbol in word.symbols
                ])
                print('Word text: {} (confidence: {})'.format(
                    word_text, word.confidence))
                temp = []
                for symbol in word.symbols:
                    temp.append([symbol.bounding_box.vertices[0].x, symbol.bounding_box.vertices[0].y])
                bounds.append([temp])
                for symbol in word.symbols:
                    print('\tSymbol: {} (confidence: {})'.format(
                        symbol.text, symbol.confidence))

    print(bounds)
    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))

import io
import os

from google.cloud import vision
from google.cloud.vision import types


def detect_document(path):
    """Detects document features in an image."""
    client = vision.ImageAnnotatorClient()

    bounds = []

    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = types.Image(content=content)

    response = client.document_text_detection(image=image)

    for page in response.full_text_annotation.pages:
        for block in page.blocks:
            print('\nBlock confidence: {}\n'.format(block.confidence))

            for par in block.paragraphs:
                print('Paragraph confidence: {}'.format(par.confidence))

                for word in par.words:
                    word_text = ''.join([
                        symbol.text for symbol in word.symbols
                    ])
                    print('Word text: {} (confidence: {})'.format(
                        word_text, word.confidence))

                    for symbol in word.symbols:
                        print('\tSymbol: {} (confidence: {})'.format(
                            symbol.text, symbol.confidence))

            for word in block.words:
                word_text = ''.join([
                    symbol.text for symbol in word.symbols
                ])
                print('Word text: {} (confidence: {})'.format(
                    word_text, word.confidence))
                temp = []
                for symbol in word.symbols:
                    temp.append([symbol.bounding_box.vertices[0].x, symbol.bounding_box.vertices[0].y])
                bounds.append([temp])
                for symbol in word.symbols:
                    print('\tSymbol: {} (confidence: {})'.format(
                        symbol.text, symbol.confidence))

    print(bounds)
    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))

from typing import List, Dict, Union
from google.cloud import automl

def classification_predict(
    project_id: str,
    model_id: str,
    filename: str,
    score_threshold: float = 0.8,
    max_predictions: int = 5,
) -> List[Dict[str, Union[str, float]]]:
    """
    Make a prediction using AutoML tables model in batch

    Args:
      project_id: ID of the project.
      model_id: Id of the model.
      filename: File path of the data to be classified.
      score_threshold: (Optional) Only produce results higher than this threshold.
      max_predictions: (Optional) Limit the number of predictions the model produces.

    Returns:
      List of predictions. Predictions are sorted by score in decreasing order.
      For a multilabel model, the maximum number of labels is 100. Each prediction
      contains the following fields:
          - Display names of classified classes.
          - Predicted class names.
          - Probabilities associated with predicted classes.
    """
    from google.protobuf import json_format
    from google.protobuf.struct_pb2 import Value

    client_options = {"api_endpoint": "eu-automl.googleapis.com:443"}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = automl.TablesClient(client_options)

    # Get the full path of the model.
    model_full_id = client.model_path(project_id, "us-central1", model_id)

    # Supported mime_types: 'text/csv', 'text/json', 'application/json'
    mime_type = "text/csv"
    gcs_source = automl.GcsSource(input_uris=[filename])
    input_config = automl.BatchPredictInputConfig(gcs_source=gcs_source, mime_type=mime_type)
    response = client.batch_predict(input_config=input_config, model_id=model_full_id)
    print("Batch Prediction results:")

    results = []

    for payload in response.payload:
        assert payload.tables

        for table in payload.tables:
            for row in table.rows:
                values = []

                for cell in row.cells:
                    value = cell.value.string_value

                    if isinstance(value, bytes):
                        value = value.decode("utf-8")  # Byte strings should use standard encoding.

                    values.append(value)

                print(f"\t{values[0]} : {values[1]}\n")
                result = {"displayName": values[0], "predictedClass": values[1]}
                results.append(result)

    return results[:: -1][:max_predictions]

from typing import List, Dict, Union
from google.cloud import automl

def classification_predict(
    project_id: str,
    model_id: str,
    filename: str,
    score_threshold: float = 0.8,
    max_predictions: int = 5,
) -> List[Dict[str, Union[str, float]]]:
    """
    Make a prediction using AutoML tables model in batch

    Args:
      project_id: ID of the project.
      model_id: Id of the model.
      filename: File path of the data to be classified.
      score_threshold: (Optional) Only produce results higher than this threshold.
      max_predictions: (Optional) Limit the number of predictions the model produces.

    Returns:
      List of predictions. Predictions are sorted by score in decreasing order.
      For a multilabel model, the maximum number of labels is 100. Each prediction
      contains the following fields:
          - Display names of classified classes.
          - Predicted class names.
          - Probabilities associated with predicted classes.
    """
    from google.protobuf import json_format
    from google.protobuf.struct_pb2 import Value

    client_options = {"api_endpoint": "eu-automl.googleapis.com:443"}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = automl.TablesClient(client_options)

    # Get the full path of the model.
    model_full_id = client.model_path(project_id, "us-central1", model_id)

    # Supported mime_types: 'text/csv', 'text/json', 'application/json'
    mime_type = "text/csv"
    gcs_source = automl.GcsSource(input_uris=[filename])
    input_config = automl.BatchPredictInputConfig(gcs_source=gcs_source, mime_type=mime_type)
    response = client.batch_predict(input_config=input_config, model_id=model_full_id)
    print("Batch Prediction results:")

    results = []

    for payload in response.payload:
        assert payload.tables

        for table in payload.tables:
            for row in table.rows:
                values = []

                for cell in row.cells:
                    value = cell.value.string_value

                    if isinstance(value, bytes):
                        value = value.decode("utf-8")  # Byte strings should use standard encoding.

                    values.append(value)

                print(f"\t{values[0]} : {values[1]}\n")
                result = {"displayName": values[0], "predictedClass": values[1]}
                results.append(result)

    return results[:: -1][:max_predictions]

#!/bin/bash

# Define search patterns for login information.
# Examples:
patterns=(
  "password:"
  "^pass.*:"
  "login.*:"
  ".*password$"
)

# Function to search file for login information.
# $1: File path
search_file() {
  for pattern in "${patterns[@]}"; do
    if grep -q "$pattern" "$1"; then
      echo "$1" >>"./files_with_credentials.txt"
    fi
  done
}

# Recursively search directories for files containing login information.
# $1: Directory path
search_directory() {
  for file in "$1"/*; do
    if [ -f "$file" ]; then
      search_file "$file"
    elif [ -d "$file" ]; then
      search_directory "$file"
    fi
  done
}

# Execute the search and save the results to the specified file.
echo "Searching for login information in your files. Please wait..."
find /root/kamile -type f -exec search_file {} +
echo "Search complete. The paths to files with potential login information have been saved to ./files_with_credentials.txt"

#!/bin/bash

# Define search patterns for login information.
# Examples:
patterns=(
  "password:"
  "^pass.*:"
  "login.*:"
  ".*password$"
)

# Function to search file for login information.
# $1: File path
search_file() {
  for pattern in "${patterns[@]}"; do
    if grep -q "$pattern" "$1"; then
      echo "$1" >>"./files_with_credentials.txt"
    fi
  done
}

# Recursively search directories for files containing login information.
# $1: Directory path
search_directory() {
  for file in "$1"/*; do
    if [ -f "$file" ]; then
      search_file "$file"
    elif [ -d "$file" ]; then
      search_directory "$file"
    fi
  done
}

# Execute the search and save the results to the specified file.
echo "Searching for login information in your files. Please wait..."
find /root/kamile -type f -exec search_file {} +
echo "Search complete. The paths to files with potential login information have been saved to ./files_with_credentials.txt"

#!/bin/bash

# Define search patterns for login information.
# Examples:
patterns=(
  "password:"
  "^pass.*:"
  "login.*:"
  ".*password$"
)

# Function to search file for login information.
# $1: File path
search_file() {
  for pattern in "${patterns[@]}"; do
    if grep -q "$pattern" "$1"; then
      echo "$1" >>"./files_with_credentials.txt"
    fi
  done
}

# Recursively search directories for files containing login information.
# $1: Directory path
search_directory() {
  for file in "$1"/*; do
    if [ -f "$file" ]; then
      search_file "$file"
    elif [ -d "$file" ]; then
      search_directory "$file"
    fi
  done
}

# Execute the search and save the results to the specified file.
echo "Searching for login information in your files. Please wait..."
find /root/kamile -type f -exec search_file {} +
echo "Search complete. The paths to files with potential login information have been saved to ./files_with_credentials.txt"

from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from google.cloud import aiplatform
from google.api_core.client_options import ClientOptions
from google.protobuf import field_mask_pb2 as field_mask


def explain_model(
    project: str,
    endpoint_id: str,
    content_type: str,
    instances_format: str,
    instances: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 300,
):
    """
    To obtain endpoint_id run the following commands where LOCATION
    is the region where the endpoint is stored, PROJECT is the project ID,
    and ENDPOINT_ID is the ID of your endpoint.

    gcloud config set compute/region LOCATION
    gcloud ai endpoints describe ENDPOINT_ID \
    --project=PROJECT \
    --format 'value(id)'
    """
    # The AI Platform services require regional API endpoints.
    client_options = ClientOptions(api_endpoint=api_endpoint)
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)
    instances_dict = json_format.ParseDict(instances, Value())
    instance = instances_dict.list_value.values
    instances = [{"payload": instance}]
    # sample_dict_value = {"payload": {"key": "value"}}
    # explanation_spec_dict = {"mean_attributions": {}}
    # parameters_dict = {"alpha": 1.0}
    parameters_dict = {}
    # parameters = json_format.ParseDict(parameters, Value())
    endpoint = client.endpoint_path(
        project=project, location=location, endpoint=endpoint_id
    )
    # operation.result() will block until the operation is done.
    response = client.explain(
        endpoint=endpoint,
        instances=instances,
        parameters=parameters_dict,
        content_type=content_type,
        instances_format=instances_format,
    )
    print(json_format.MessageToJson(response.explanations[0]))

from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from google.cloud import aiplatform
from google.api_core.client_options import ClientOptions
from google.protobuf import field_mask_pb2 as field_mask


def explain_model(
    project: str,
    endpoint_id: str,
    content_type: str,
    instances_format: str,
    instances: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 300,
):
    """
    To obtain endpoint_id run the following commands where LOCATION
    is the region where the endpoint is stored, PROJECT is the project ID,
    and ENDPOINT_ID is the ID of your endpoint.

    gcloud config set compute/region LOCATION
    gcloud ai endpoints describe ENDPOINT_ID \
    --project=PROJECT \
    --format 'value(id)'
    """
    # The AI Platform services require regional API endpoints.
    client_options = ClientOptions(api_endpoint=api_endpoint)
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)
    instances_dict = json_format.ParseDict(instances, Value())
    instance = instances_dict.list_value.values
    instances = [{"payload": instance}]
    # sample_dict_value = {"payload": {"key": "value"}}
    # explanation_spec_dict = {"mean_attributions": {}}
    # parameters_dict = {"alpha": 1.0}
    parameters_dict = {}
    # parameters = json_format.ParseDict(parameters, Value())
    endpoint = client.endpoint_path(
        project=project, location=location, endpoint=endpoint_id
    )
    # operation.result() will block until the operation is done.
    response = client.explain(
        endpoint=endpoint,
        instances=instances,
        parameters=parameters_dict,
        content_type=content_type,
        instances_format=instances_format,
    )
    print(json_format.MessageToJson(response.explanations[0]))

import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split


def encode_target(df, target_column):
    """Encodes a target column with integer values."""

    df.replace({target_column: {np.nan: -1}}, inplace=True)
    df[target_column] = df[target_column].astype("category").cat.codes
    return df

import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split


def encode_target(df, target_column):
    """Encodes a target column with integer values."""

    df.replace({target_column: {np.nan: -1}}, inplace=True)
    df[target_column] = df[target_column].astype("category").cat.codes
    return df