app.py

import os
import json
import time
import base64
import requests
import gradio as gr

username = os.environ.get("CLIENT_ID")
passwd = os.environ.get("CLIENT_SECRETS")


def authorization():
    url = "https://stage.id.trimblecloud.com/oauth/token"
    credential_pair = f"{username}:{passwd}"

    payload = "grant_type=client_credentials&scope=DLPointCloudSegmentation"
    headers = {
        "Content-Type": "application/x-www-form-urlencoded",
        "Authorization": f"Basic {base64.b64encode(credential_pair.encode('utf-8')).decode('utf-8')}",
    }

    response = requests.request("POST", url, headers=headers, data=payload)

    response.raise_for_status()
    print("Succesfully authenticated")
    auth_token = json.loads(response.text)["access_token"]

    return auth_token


def create_file(auth_token, input_filename):
    url = "https://cloud.stage.api.trimblecloud.com/dataocean/api/3.0/api/files"

    payload = json.dumps({"file": {"path": input_filename, "regions": ["us1"]}})
    headers = {
        "Authorization": f"Bearer {auth_token}",
        "Content-Type": "application/json",
    }

    response = requests.request("POST", url, headers=headers, data=payload)

    response.raise_for_status()
    print("File created successfully")
    file_upload_url = json.loads(response.text)["file"]["upload"]["url"]
    return file_upload_url


def upload_file(url, file):
    with open(file.name, "rb") as lasFile:
        payload = lasFile.read()

    headers = {"Content-Type": "application/octet-stream"}

    response = requests.request("PUT", url, headers=headers, data=payload)

    response.raise_for_status()
    print("Upload was successful")


def start_execution(auth_token, input_filename, output_filename="output.las"):
    url = "https://cloud.stage.api.trimblecloud.com/Processing/api/1/api/executions"

    payload = json.dumps(
        {
            "execution": {
                "procedure_id": "a7c4f9c3-b21a-4c9c-b4df-3dc6ba8934d9",
                "region": "aws-us1",
                "parameters": {
                    "source_path": input_filename,
                    "regions": ["us1"],
                    "output_path": output_filename,
                },
            }
        }
    )
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {auth_token}",
    }

    response = requests.request("POST", url, headers=headers, data=payload)

    response.raise_for_status()
    print("Execution has started")

    execution_id = json.loads(response.text)["execution"]["id"]

    return execution_id


def track_execution(auth_token, execution_id, output_filename):
    url = "https://cloud.stage.api.trimblecloud.com/Processing/api/1/api/executions"

    payload = {}
    headers = {
        "Authorization": f"Bearer {auth_token}",
    }

    response = requests.request("GET", url, headers=headers, data=payload)

    status = json.loads(response.text)["items"][0]["execution_status"]

    while status != "FINISHED":
        response = requests.request("GET", url, headers=headers, data=payload)
        status = json.loads(response.text)["items"][0]["execution_status"]
        time.sleep(1)

    return download_output(auth_token, output_filename)


def download_output(auth_token, output_filename):
    url = f"https://cloud.stage.api.trimblecloud.com/dataocean/api/3.0/api/files?path={output_filename}"

    payload = ""
    headers = {
        "Authorization": f"Bearer {auth_token}",
        "Content-Type": "application/json",
    }

    response = requests.request("GET", url, headers=headers, data=payload)

    response.raise_for_status()
    print("File downloading")
    response = json.loads(response.text)
    download_url = response["file"]["download"]["url"]

    return download_url


def predict(input_file):
    input_filename = "input.las"
    output_filename = "output.las"
    auth_token = authorization()
    file_upload_url = create_file(auth_token, input_filename)
    upload_file(file_upload_url, input_file)
    execution_id = start_execution(auth_token, input_filename, output_filename)
    download_url = track_execution(auth_token, execution_id, output_filename)

    html_content = f'<a href="{download_url}" download><button type="button" style="height:50px; width:100px; background-color:blue; border-color:black; color:white">Download</button></a>'

    return html_content

demo = gr.Interface(
#    title="Trimble AI 3D Point Cloud Segmentation",
    fn=predict,
    inputs=gr.File(file_types=[".las"], file_count="single"),
    outputs=["html"],
    examples=["westminster.las"],
    cache_examples=False,
    theme='trimble/trimble_ai_theme',
    description="""
<center><img src="https://huggingface.co/spaces/trimble/trimble_ai_theme/resolve/main/images/logo.png" alt="Trimble AI Logo" class="center"></center><BR>
This is a technology demonstration of Trimble AI's 3D Point Cloud Segmentation running on Trimble Cloud Core's Pegasus Processing Framework. The point cloud is uploaded on behalf of the user into Pegasus, then the result is offered as a downloadable link.
""").queue(concurrency_count=512, max_size=512)

demo.launch()