import labelbox as lb
from labelbox.schema.data_row_metadata import DataRowMetadataField, DataRowMetadataKind
import datetime
import random
import os
import json
from PIL import Image
from labelbox.schema.ontology import OntologyBuilder, Tool
import requests
from tqdm.notebook import tqdm
import uuid
from labelbox.data.annotation_types import Label, ImageData, ObjectAnnotation, Rectangle

# Initialize the Labelbox client
API_KEY = "" # Place API key
client = lb.Client(API_KEY)

# Function to download files
def download_files(filemap):
    path, uri = filemap
    if not os.path.exists(path):
        response = requests.get(uri, stream=True)
        with open(path, 'wb') as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
    return path

# Download data rows and annotations
DATA_ROWS_URL = "https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_datarows.json"
ANNOTATIONS_URL = "https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_annotations.json"
download_files(("data_rows.json", DATA_ROWS_URL))
download_files(("annotations.json", ANNOTATIONS_URL))

with open('data_rows.json', 'r') as fp:
    data_rows = json.load(fp)

with open('annotations.json', 'r') as fp:
    annotations = json.load(fp)

# Create a new dataset
dataset = client.create_dataset(name="Geospatial vessel detection")
print(f"Created dataset with ID: {dataset.uid}")

# Here is an example of adding two metadata fields to your Data Rows: a "captureDateTime" field with datetime value, and a "tag" field with string value
metadata_ontology = client.get_data_row_metadata_ontology()
datetime_schema_id = metadata_ontology.reserved_by_name["captureDateTime"].uid
tag_schema_id = metadata_ontology.reserved_by_name["tag"].uid
tag_items = ["WorldView-1", "WorldView-2", "WorldView-3", "WorldView-4"]

for datarow in tqdm(data_rows):
    dt = datetime.datetime.utcnow() + datetime.timedelta(days=random.random()*30) # this is random datetime value
    tag_item = random.choice(tag_items) # this is a random tag value

    # Option 1: Specify metadata with a list of DataRowMetadataField. This is the recommended option since it comes with validation for metadata fields.
    metadata_fields = [
                       DataRowMetadataField(schema_id=datetime_schema_id, value=dt),
                       DataRowMetadataField(schema_id=tag_schema_id, value=tag_item)
                       ]

    # Option 2: Uncomment to try. Alternatively, you can specify the metadata fields with dictionary format without declaring the DataRowMetadataField objects. It is equivalent to Option 1.
    # metadata_fields = [
    #                    {"schema_id": datetime_schema_id, "value": dt},
    #                    {"schema_id": tag_schema_id, "value": tag_item}
    #                    ]

    datarow["metadata_fields"] = metadata_fields

task = dataset.create_data_rows(data_rows)
task.wait_till_done()
print(f"Failed data rows: {task.failed_data_rows}")
print(f"Errors: {task.errors}")

if task.errors:
    for error in task.errors:
        if 'Duplicate global key' in error['message'] and dataset.row_count == 0:
            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.
            print(f"Deleting empty dataset: {dataset}")
            dataset.delete()

datarow = next(dataset.data_rows())
print(datarow)

# Initialize the OntologyBuilder
ontology_builder = OntologyBuilder()

# Assuming 'annotations' is defined and contains the necessary data
for category in annotations['categories']:
    print(category['name'])
    # Add tools to the ontology builder
    ontology_builder.add_tool(Tool(tool=Tool.Type.BBOX, name=category['name']))

# Create the ontology in Labelbox
ontology = client.create_ontology("Vessel Detection Ontology",
                                  ontology_builder.asdict(),
                                  media_type=lb.MediaType.Image)
print(f"Created ontology with ID: {ontology.uid}")

# Create a project and connect the ontology
project = client.create_project(name="Vessel Detection", media_type=lb.MediaType.Image)
project.connect_ontology(ontology=ontology)
print(f"Created project with ID: {project.uid}")

# Minimal Export parameters focused solely on data row IDs
export_params = {
    "data_row_details": True  # Only export data row details 
}

# Initiate the streamable export task from catalog
dataset = client.get_dataset(dataset.uid)  # Update with the actual dataset ID
export_task = dataset.export(params=export_params)
export_task.wait_till_done()
print(export_task)

data_rows = []

# Stream results and errors
if export_task.has_errors():
    export_task.get_buffered_stream(stream_type=lb.StreamType.ERRORS).start(
        stream_handler=lambda error: print(error))

if export_task.has_result():
    # Start export stream
    stream = export_task.get_buffered_stream()

    # Iterate through data rows
    for data_row in stream:
        print(data_row.json)

# Randomly select 200 Data Rows (or fewer if the dataset has less than 200 data rows)
sampled_data_rows = random.sample(data_rows, min(len(data_rows), 200))

# Create a new batch in the project and add the sampled data rows
batch = project.create_batch(
    "Initial batch",  # name of the batch
    sampled_data_rows,  # list of Data Rows
    1  # priority between 1-5
)
print(f"Created batch with ID: {batch.uid}")

# Set export parameters focused on data row details
export_params = {
    "data_row_details": True,  # Only export data row details
    "batch_ids": [batch.uid],  # Optional: Include batch ids to filter by specific batches
}

# Initialize the streamable export task from project
export_task = project.export(params=export_params)
export_task.wait_till_done()

data_rows = []

# Stream results and errors
if export_task.has_errors():
    export_task.get_buffered_stream(stream_type=lb.StreamType.ERRORS).start(
        stream_handler=lambda error: print(error))

if export_task.has_result():
    # Start export stream
    stream = export_task.get_buffered_stream()

    # Iterate through data rows
    for data_row in stream:
        print(data_row.json)

labels = []
for datarow in data_rows:
    annotations_list = []
    # Access the 'data_row' dictionary first
    data_row_dict = datarow['data_row']
    folder = data_row_dict['external_id'].split("/")[0]
    id = data_row_dict['external_id'].split("/")[1]
    
    if folder == "positive_image_set":
        for image in annotations['images']:
            if image['file_name'] == id:
                for annotation in annotations['annotations']:
                    if annotation['image_id'] == image['id']:
                        bbox = annotation['bbox']
                        category_id = annotation['category_id'] - 1
                        class_name = None
                        ontology = ontology_builder.asdict()  # Get the ontology dictionary
                        for category in ontology['tools']:
                            if category['name'] == annotations['categories'][category_id]['name']:
                                class_name = category['name']
                                break
                        if class_name:
                            annotations_list.append(ObjectAnnotation(
                                name=class_name,
                                value=Rectangle(start=Point(x=bbox[0], y=bbox[1]), end=Point(x=bbox[2]+bbox[0], y=bbox[3]+bbox[1]))
                            ))
    image_data = ImageData(uid=data_row_dict['id'])
    labels.append(Label(data=image_data, annotations=annotations_list))

upload_job = lb.LabelImport.create_from_objects(
    client=client,
    project_id=project.uid,
    name=f"label_import_job_{str(uuid.uuid4())}",
    labels=labels
)

# Wait for the upload to finish and print the results
upload_job.wait_until_done()

print(f"Errors: {upload_job.errors}")
print(f"Status of uploads: {upload_job.statuses}")