Introduction to AI-Powered services

from arcgis.gis import GIS
from arcgis.ai import analyze_image, analyze_text, translate, AIUtilsResponse

gis = GIS(profile='your_online_profile')

image_url = "https://geosaurus.maps.arcgis.com/sharing/rest/content/items/6e9ff93df28a4231b4bd6e729ce42898/data"

prompt_str = "List all the colors present in the image."

colors = analyze_image(image = image_url, prompt = prompt_str)
colors

AIUtilsResponse(success=True, meta=AIUtilsResponseMetaData(usage={'inputTokens': 1831, 'outputTokens': 62, 'totalTokens': 1893}, model='gpt-5.4'), results=[AnalyzeResult(success=True, value=['green', 'dark green', 'light green', 'brown', 'dark brown', 'orange', 'yellow', 'red', 'pink', 'purple', 'blue', 'black', 'gray', 'white'], key='unique_string_1')])

colors.success

True

colors.results[0].value

['green',
 'dark green',
 'light green',
 'brown',
 'dark brown',
 'orange',
 'yellow',
 'red',
 'pink',
 'purple',
 'blue',
 'black',
 'gray',
 'white']

virginia_map_url = "https://geosaurus.maps.arcgis.com/sharing/rest/content/items/36c34ac9e2c5402ab94762f97f3349af/data"

prompt_list = [
    "Describe the image.",
    "Identify any hurricane related text in the image."
]

result_list = analyze_image(image = virginia_map_url, prompt = prompt_list)
result_list

AIUtilsResponse(success=True, meta=AIUtilsResponseMetaData(usage={'inputTokens': 1822, 'outputTokens': 448, 'totalTokens': 2270}, model='gpt-5.4'), results=[AnalyzeResult(success=True, value='A grayscale map of Virginia showing hurricane, tropical storm, and tropical depression tracks across the state and nearby Atlantic coast. The title reads "Virginia Hurricanes, Tropical Storms, and Tropical Depressions by Date 1852-2016, 2017-2021." Many storm paths are drawn in light blue for 1852-2016 and in orange/red shades for 2017-2021. County boundaries, neighboring states, a mileage scale, north arrow, legend, and source information are also shown.', key='unique_string_0'), AnalyzeResult(success=True, value=['Virginia Hurricanes, Tropical Storms, and Tropical Depressions by Date 1852-2016, 2017-2021', 'Hurricane Category 2, 2017-2021', 'Tropical Storm, 2017-2021', 'Tropical Depression 2017-2021', 'Hurricane Category 4, 1852-2016', 'Hurricane Category 3, 1852-2016', 'Hurricane Category 2, 1852-2016', 'Hurricane Category 1, 1852-2016', 'Tropical Storm, 1852-2016', 'Tropical Depression, 1852-2016', 'FLORENCE, 2018', 'IDA, 2021', 'ZETA, 2020', 'BERTHA, 2020', 'MICHAEL, 2018', 'ELSA, 2021', 'ISAIAS, 2020', 'FAY, 2020', 'CLAUDETTE, 2021', 'DORIAN, 2019', 'FRED, 2021'], key='unique_string_1')])

result_list.results[0].value

'A grayscale map of Virginia showing hurricane, tropical storm, and tropical depression tracks across the state and nearby Atlantic coast. The title reads "Virginia Hurricanes, Tropical Storms, and Tropical Depressions by Date 1852-2016, 2017-2021." Many storm paths are drawn in light blue for 1852-2016 and in orange/red shades for 2017-2021. County boundaries, neighboring states, a mileage scale, north arrow, legend, and source information are also shown.'

result_list.results[1].value

['Virginia Hurricanes, Tropical Storms, and Tropical Depressions by Date 1852-2016, 2017-2021',
 'Hurricane Category 2, 2017-2021',
 'Tropical Storm, 2017-2021',
 'Tropical Depression 2017-2021',
 'Hurricane Category 4, 1852-2016',
 'Hurricane Category 3, 1852-2016',
 'Hurricane Category 2, 1852-2016',
 'Hurricane Category 1, 1852-2016',
 'Tropical Storm, 1852-2016',
 'Tropical Depression, 1852-2016',
 'FLORENCE, 2018',
 'IDA, 2021',
 'ZETA, 2020',
 'BERTHA, 2020',
 'MICHAEL, 2018',
 'ELSA, 2021',
 'ISAIAS, 2020',
 'FAY, 2020',
 'CLAUDETTE, 2021',
 'DORIAN, 2019',
 'FRED, 2021']

prompt_dicts = [
    {"key": "images_prompt", "context": "How many sub-boundaries of Virginia are included in the image?"},
    {"key": "text_prompt", "context": "Identify the states named in the map."}
]

result_dicts = analyze_image(image = virginia_map_url, prompt = prompt_dicts)
result_dicts

AIUtilsResponse(success=True, meta=AIUtilsResponseMetaData(usage={'inputTokens': 1826, 'outputTokens': 117, 'totalTokens': 1943}, model='gpt-5.4'), results=[AnalyzeResult(success=True, value=95, key='images_prompt'), AnalyzeResult(success=True, value=['Ohio', 'Pennsylvania', 'Maryland', 'Delaware', 'Kentucky', 'Tennessee', 'North Carolina', 'Virginia'], key='text_prompt')])

result_dicts.results[0].value

95

dicts_result.results[1].value

['Ohio',
 'Pennsylvania',
 'Maryland',
 'Delaware',
 'Kentucky',
 'Tennessee',
 'North Carolina',
 'Virginia']

sample_text = "During our recent travels, we explored bustling city centers like London and Prague, \
            wandered through the ancient, narrow streets of Rome, and relaxed on the sunny beaches of \
            Alanya near the historic Alanya Castle. We visited the majestic Taj Mahal in Agra, toured the \
            expansive Amber Palace in Jaipur, and took a relaxing break by the calm waters of the Dim River."

prompt_str = "Detect all locations in this text"

locations = analyze_text(text = sample_text, prompt = prompt_str)
locations

AIUtilsResponse(success=True, meta=AIUtilsResponseMetaData(usage={'inputTokens': 750, 'outputTokens': 55, 'totalTokens': 805}, model='gpt-5.4'), results=[AnalyzeResult(success=True, value=['London', 'Prague', 'Rome', 'Alanya', 'Alanya Castle', 'Taj Mahal', 'Agra', 'Amber Palace', 'Jaipur', 'Dim River'], key='key:1')])

locations.results[0].value

['London',
 'Prague',
 'Rome',
 'Alanya',
 'Alanya Castle',
 'Taj Mahal',
 'Agra',
 'Amber Palace',
 'Jaipur',
 'Dim River']

prompt_list = [
    "Detect the sentiment of the text.",
    "Identify countries in the text."
]

result_list = analyze_text(text = sample_text, prompt = prompt_list)
result_list

AIUtilsResponse(success=True, meta=AIUtilsResponseMetaData(usage={'inputTokens': 768, 'outputTokens': 54, 'totalTokens': 822}, model='gpt-5.4'), results=[AnalyzeResult(success=True, value='positive', key='unique_string_0'), AnalyzeResult(success=True, value=['United Kingdom', 'Czech Republic', 'Italy', 'Turkey', 'India'], key='unique_string_1')])

result_list.results[0].value

'positive'

result_list.results[1].value

['United Kingdom', 'Czech Republic', 'Italy', 'Turkey', 'India']

document = gis.content.get('c7b4e9240149481ea69af628513962fe')
document

item_description = document.description
item_description

"<div>Esri's Water Resources GIS Platform offers a comprehensive suite of tools and resources designed to modernize water resource management. It emphasizes geospatial solutions for monitoring, analyzing, and modeling water systems, helping decision-makers tackle challenges like drought resilience, flood mitigation, and environmental protection. By leveraging the capabilities of ArcGIS, users can transform raw water data into actionable insights, ensuring more efficient and effective water resource management.</div><div><br /></div><div>A central feature of the platform is Arc Hydro, a specialized data model and toolkit developed for GIS-based water resource analysis. This toolset allows users to integrate, analyze, and visualize water datasets for applications ranging from live stream gauge monitoring to pollution control. Additionally, the platform connects users to the ArcGIS Living Atlas of the World, which offers extensive water-related datasets such as rivers, wetlands, and soils, supporting in-depth analyses of hydrologic conditions. The Hydro Community further enhances collaboration, enabling stakeholders to share expertise, discuss challenges, and build innovative solutions together.</div><div><br /></div><div>Esri’s platform also provides training opportunities and professional services to empower users with technical knowledge and skills. Through instructor-led courses, documentation, and best practices, users gain expertise in using ArcGIS and Arc Hydro for their specific water management needs. The combination of tools, datasets, and community engagement makes Esri's water resources platform a powerful asset for advancing sustainable water management initiatives across public and private sectors.</div><div><br /></div>"

prompt_dicts = [
    {"key": "summary_prompt", "context": "Summarize this text"},
    {"key": "tools_prompt", "context": "Identify the tools mentioned in the text."}
]

result_dicts = analyze_text(text = item_description, prompt = prompt_dicts)
result_dicts

AIUtilsResponse(success=True, meta=AIUtilsResponseMetaData(usage={'inputTokens': 997, 'outputTokens': 140, 'totalTokens': 1137}, model='gpt-5.4'), results=[AnalyzeResult(success=True, value="Esri's Water Resources GIS Platform provides geospatial tools, datasets, training, and community resources to improve water resource management. It uses ArcGIS capabilities to monitor, analyze, and model water systems for issues such as drought resilience, flood mitigation, and environmental protection. Key components include Arc Hydro for GIS-based water analysis, the ArcGIS Living Atlas of the World for water-related datasets, and the Hydro Community for collaboration and knowledge sharing.", key='summary_prompt'), AnalyzeResult(success=True, value=['ArcGIS', 'Arc Hydro', 'ArcGIS Living Atlas of the World', 'Hydro Community'], key='tools_prompt')])

result_dicts.results[0].value

"Esri's Water Resources GIS Platform provides geospatial tools, datasets, training, and community resources to improve water resource management. It uses ArcGIS capabilities to monitor, analyze, and model water systems for issues such as drought resilience, flood mitigation, and environmental protection. Key components include Arc Hydro for GIS-based water analysis, the ArcGIS Living Atlas of the World for water-related datasets, and the Hydro Community for collaboration and knowledge sharing."

result_dicts.results[1].value

['ArcGIS', 'Arc Hydro', 'ArcGIS Living Atlas of the World', 'Hydro Community']

gis.languages

[{'language': 'Arabic', 'culture': 'ar', 'localizedName': 'عربي'},
 {'language': 'Bosnian', 'culture': 'bs', 'localizedName': 'Bosanski'},
 {'language': 'Bulgarian', 'culture': 'bg', 'localizedName': 'Български'},
 {'language': 'Simplified Chinese',
  'culture': 'zh-CN',
  'localizedName': '简体中文'},
 {'language': 'Catalan', 'culture': 'ca', 'localizedName': 'Català'},
 {'language': 'Croatian', 'culture': 'hr', 'localizedName': 'Hrvatski'},
 {'language': 'Czech', 'culture': 'cs', 'localizedName': 'čeština'},
 {'language': 'Danish', 'culture': 'da', 'localizedName': 'Dansk'},
 {'language': 'Dutch', 'culture': 'nl', 'localizedName': 'Nederlands'},
 {'language': 'English',
  'culture': 'en',
  'localizedName': 'English',
  'cultureFormats': [{'name': 'United States',
    'localizedName': 'United States',
    'format': 'us'},
   {'name': 'Great Britain', 'localizedName': 'Great Britain', 'format': 'gb'},
   {'name': 'Australia', 'localizedName': 'Australia', 'format': 'au'},
   {'name': 'Canada', 'localizedName': 'Canada', 'format': 'ca'}]},
 {'language': 'Estonian', 'culture': 'et', 'localizedName': 'Eesti'},
 {'language': 'Finnish', 'culture': 'fi', 'localizedName': 'Suomi'},
 {'language': 'French',
  'culture': 'fr',
  'localizedName': 'Français',
  'cultureFormats': [{'name': 'France',
    'localizedName': 'France',
    'format': 'fr'},
   {'name': 'Switzerland', 'localizedName': 'Suisse', 'format': 'ch'},
   {'name': 'Canada', 'localizedName': 'Canada', 'format': 'ca'}]},
 {'language': 'Greek', 'culture': 'el', 'localizedName': 'Ελλάδα'},
 {'language': 'German',
  'culture': 'de',
  'localizedName': 'Deutsch',
  'cultureFormats': [{'name': 'Germany',
    'localizedName': 'Deutschland',
    'format': 'de'},
   {'name': 'Switzerland', 'localizedName': 'Schweiz', 'format': 'ch'},
   {'name': 'Austria', 'localizedName': 'Österreich', 'format': 'at'}]},
 {'language': 'Hebrew', 'culture': 'he', 'localizedName': 'עברית'},
 {'language': 'Hungarian', 'culture': 'hu', 'localizedName': 'Magyar'},
 {'language': 'Indonesian',
  'culture': 'id',
  'localizedName': 'Bahasa Indonesia'},
 {'language': 'Italian',
  'culture': 'it',
  'localizedName': 'Italiano',
  'cultureFormats': [{'name': 'Italy',
    'localizedName': 'Italia',
    'format': 'it'},
   {'name': 'Switzerland', 'localizedName': 'Svizzera', 'format': 'ch'}]},
 {'language': 'Japanese', 'culture': 'ja', 'localizedName': '日本語'},
 {'language': 'Korean', 'culture': 'ko', 'localizedName': '한국어'},
 {'language': 'Latvian', 'culture': 'lv', 'localizedName': 'Latviešu'},
 {'language': 'Lithuanian', 'culture': 'lt', 'localizedName': 'Lietuvių'},
 {'language': 'Norwegian', 'culture': 'nb', 'localizedName': 'Norsk'},
 {'language': 'Polish', 'culture': 'pl', 'localizedName': 'Polski'},
 {'language': 'Portuguese (Brazil)',
  'culture': 'pt-BR',
  'localizedName': 'Português (Brasil)'},
 {'language': 'Portuguese (Portugal)',
  'culture': 'pt-PT',
  'localizedName': 'Português (Portugal)'},
 {'language': 'Romanian', 'culture': 'ro', 'localizedName': 'Română'},
 {'language': 'Russian', 'culture': 'ru', 'localizedName': 'Русский'},
 {'language': 'Serbian', 'culture': 'sr', 'localizedName': 'Srpski'},
 {'language': 'Slovenian', 'culture': 'sl', 'localizedName': 'Slovenščina'},
 {'language': 'Slovak', 'culture': 'sk', 'localizedName': 'Slovenčina'},
 {'language': 'Spanish',
  'culture': 'es',
  'localizedName': 'Español',
  'cultureFormats': [{'name': 'Spain',
    'localizedName': 'España',
    'format': 'es'},
   {'name': 'Mexico', 'localizedName': 'México', 'format': 'mx'}]},
 {'language': 'Swedish', 'culture': 'sv', 'localizedName': 'Svenska'},
 {'language': 'Thai', 'culture': 'th', 'localizedName': 'ไทย'},
 {'language': 'Traditional Chinese (Hong Kong)',
  'culture': 'zh-HK',
  'localizedName': '繁體中文（香港）'},
 {'language': 'Traditional Chinese (Taiwan)',
  'culture': 'zh-TW',
  'localizedName': '繁體中文（台灣）'},
 {'language': 'Ukrainian', 'culture': 'uk', 'localizedName': 'Україна'},
 {'language': 'Turkish', 'culture': 'tr', 'localizedName': 'Türkiye'},
 {'language': 'Vietnamese', 'culture': 'vi', 'localizedName': 'Tiếng Việt'}]

text = "El bosque está lleno de vida. Mientras el sol brilla a través de las altas ramas, el viento sopla suavemente. Los \
        pájaros cantan en los árboles y las mariposas vuelan sobre las flores. Estar en la naturaleza nos da paz y tranquilidad."

spanish_to_english = translate(text = text, to_language = ['en'], from_language = 'es')
spanish_to_english

AIUtilsResponse(success=True, meta=AIUtilsResponseMetaData(usage={'inputCharacters': 242}, model='azure-ai-translator'), results=[TranslateResult(translations=[{'text': 'The forest is full of life. As the sun shines through the tall branches, the wind blows gently. Birds sing in the trees and butterflies fly over the flowers. Being in nature gives us peace and tranquility.', 'to': 'en'}], key='key:1')])

spanish_to_english.results[0].translations[0]['text']

'The forest is full of life. As the sun shines through the tall branches, the wind blows gently. Birds sing in the trees and butterflies fly over the flowers. Being in nature gives us peace and tranquility.'

ontario_canada_item = gis.content.get('9c2e4d1a671f41eb913411f048aaf661')
ontario_canada_item

item_snippet = ontario_canada_item.snippet
item_snippet

'Les données du rapport annuel historique sur les forêts de l’Ontario constituent un ensemble de données consolidées à l’échelle de la province, provenant des rapports annuels sur les opérations forestières depuis 2002. Format des données :  ESRI Geodatabase'

french_to_english = translate(text = item_snippet, to_language = ['en'], from_language = 'fr')
french_to_english

AIUtilsResponse(success=True, meta=AIUtilsResponseMetaData(usage={'inputCharacters': 257}, model='azure-ai-translator'), results=[TranslateResult(translations=[{'text': 'The Ontario Historical Annual Forest Report is a province-wide consolidated dataset from annual reports on forest operations since 2002. Data Format: ESRI Geodatabase', 'to': 'en'}], key='key:1')])

french_to_english.results[0].translations[0]['text']

'The Ontario Historical Annual Forest Report is a province-wide consolidated dataset from annual reports on forest operations since 2002. Data Format: ESRI Geodatabase'