import labelbox as lb
from labelbox.schema.quality_mode import QualityMode
client = lb.Client(api_key="<YOUR_API_KEY>")

# Create a new project
project = client.create_project(
    name="<project_name>",
    description="<project_description>",    # optional
    media_type=lb.MediaType.Image           # specify the media type
)

project = client.create_project(
  name="benchmark-project",
  description="description",
  media_type=lb.MediaType.Image,
  quality_mode=QualityMode.Benchmark
)

# create a project that uses consensus quality control
project = client.create_project(
  name="consensus-project",
  description="description",
  media_type=lb.MediaType.Image,
  quality_mode=QualityMode.Consensus
)

project = client.get_project("<project_id>")

# alternatively, you can get a dataset by name 
project = client.get_projects(where=lb.Project.name == "<project_name>").get_one()

project.create_batch(
  name="<unique_batch_name>",
  global_keys=["key1", "key2", "key3"],
  priority=5,
)

# if the project uses consensus, you can optionally supply a dictionary with consensus settings
# if provided, the batch will use consensus with the specificed coverage and votes
project.create_batch(
  name="<unique_batch_name>",
  data_rows=["<data_row_id>", "<data_row_id>"],
  priority=1,
  consensus_settings={"number_of_labels": 3, "coverage_percentage": 0.1}
)

task = project.create_batches(
  name_prefix="demo-create-batches-",
  global_keys=global_keys,
  priority=5
)

print("Errors: ", task.errors())
print("Result: ", task.result())

dataset = client.get_dataset("<dataset_id>")

task = project.create_batches_from_dataset(
    name_prefix="demo-dataset-",
    dataset_id=dataset.uid,
    priority=5
)

print("Errors: ", task.errors())
print("Result: ", task.result())

 # upload data
task = dataset.create_data_rows(...)
# optionally, wait for data to be processed
task.wait_till_done()

# get the batches (objects of the Batch class)
batches = project.batches()

# inspect one batch
next(batches)

# inspect all batches
for batch in batches:
  print(batch)
    
# for ease of use, you can convert the paginated collection to a list
list(batches)

# the argument must be an object of the Ontology class
project.setup_editor(ontology)

# get the members (objects of the ProjectMember class with relationships to a User and Role)  
members = project.members()

# inspect one member
member = next(members)
print(member.user(), member.role(), member.access_from)

# Display member info:
print(member.user().uid, member.user().email, member.role().name, access_from, sep="\t")

# inspect all members
for member in members:
  print(member.user(), member.role(), access_from)
  
# for ease of use, you can convert the paginated collection to a list
list(members)

# must be a PDF or HTML file
project.upsert_instructions("<local_file_path>")

# get the task queues (relationship to TaskQueue objects)
task_queues = project.task_queues()

# inspect all task queues
for task_queue in task_queues:
  print(task_queue)

project.move_data_rows_to_task_queue(
  data_row_ids=lb.GlobalKeys(["<global_key>", "<global_key>"]), # Use "lb.UniqueIds" for "<data_row_ids>"
  task_queue_id="<task_queue_id>" # Use "None" to move data rows to the "Done" bucket 
)

# Select data rows from batches 
data_rows = []
for batch in list(project.batches()):
  for data_row in batch.export_data_rows(): 
    data_rows.append(data_row)

# Add LPOs
lpos = []
priority=1
for data_row in data_rows: 
  lpos.append((data_row, priority, 1)) #Use "lb.GlobalKey" for "<global_key>" instead of data_row
  priority+=1

project.set_labeling_parameter_overrides(lpos)

# Check results
project_lpos = list(project.labeling_parameter_overrides())
for lpo in project_lpos:
  print(lpo)

# Select data rows from batches 
data_rows = []
for batch in list(project.batches()):
  for data_row in batch.export_data_rows(): 
    data_rows.append(data_row)
    
# Update LPOs
global_keys = []
for data_row in data_rows:
    global_keys.append(data_row.global_key)

project.update_data_row_labeling_priority(data_rows=lb.GlobalKeys(global_keys), priority=1)

# Check results
project_lpos = list(project.labeling_parameter_overrides())

for lpo in project_lpos:
  print(lpo)

tags = project.update_project_resource_tags(["<project_tag_id>", "<project_tag_id>"])

tags = project.get_resource_tags()

# Set the export params to include/exclude certain fields. Make sure each of these fields are correctly grabbed 
export_params= {
  "attachments": True,
  "metadata_fields": True,
  "data_row_details": True,
  "project_details": True,
  "performance_details": True
}

# You can set the range for last_activity_at and label_created_at. 
# For context, last_activity_at captures the creation and modification of labels, metadata, status, comments, and reviews.
# Note: This is an AND logic between the filters, so usually using one filter is sufficient.
filters= {
  "last_activity_at": ["2000-01-01 00:00:00", "2050-01-01 00:00:00"]
}

export_task = project.export_v2(params=export_params, filters=filters)
export_task.wait_till_done()

if export_task.errors:
  print(export_task.errors)

export_json = export_task.result
print("results: ", export_json)

export_params= {
 "attachments": True, # Set to true if you want to export attachments
  "metadata_fields": True, # Set to true if you want to export metadata
  "data_row_details": True,
  "project_details": True
}
filters = {
  "workflow_status": "ToLabel" ## Using this filter will only export queued data rows
}

# A task is returned, this provides additional information about the status of your task, such as
# any errors encountered
task = project.export_v2(params=export_params, filters=filters)

task.wait_till_done()
if task.errors:
  print(task.errors)

export_json = task.result
# Fetch the first data row
pp.pprint(export_json[0])

import requests

url = project.export_issues()
issues = requests.get(url).json()

# optionally, you can export only the open or resolved issues
open_issues_url = project.export_issues(status="Open")
resolved_issues_url = project.export_issues(status="Resolved")

project.update(name="<new_project_name>")

project.delete()

# name (str)
project.name

# description (str)
project.description

# updated at (datetime)
project.updated_at

# created at (datetime)
project.created_at

# last activity time (datetime)
project.last_activity_time

# number of required labels per consensus data row (int) 
project.auto_audit_number_of_labels

# default percentage of consensus data rows per batch (float)
project.auto_audit_percentage

# created by (relationship to User object)
user = project.created_by()

# organization (relationship to Organization object)
organization = project.organization()

# get the ontology connected to the project (relationship to Ontology object)
ontology = project.ontology()

# get the benchmarks (relationship to Benchmark objects)
benchmarks = project.benchmarks()

# inspect one benchmark
next(benchmarks)

# inspect all benchmarks
for benchmark in benchmarks:
  print(benchmark)
  
# for ease of use, you can convert the paginated collection to a list
list(benchmarks)

# get the webhooks connected to the project (relationship to Webhook objects)
webhooks = project.webhooks()

# inspect one webhook
next(webhooks)

# inspect all webhooks
for webhook in webhooks:
  print(webhook)
  
# for ease of use, you can convert the paginated collection to a list
list(webhooks)

# gets the LPOs created in the project (relationship to LabelingParameterOverride objects)
lpos = project.labeling_parameter_overrides()

# inspect one LPO
next(lpos)

# inspect all LPOs
for lpo in lpos:
  print(lpo)

# for ease of use, you can convert the paginated collection to a list
list(lpos)

# Get the data row id 
for lpo in lpos:
  print(lpo)
  print("Data row:", lpo.data_row().uid)

# Return the number of 
project.get_label_count()