public static async Task <IActionResult> Run(
[HttpTrigger(AuthorizationLevel.Function, "get", "post", Route = null)] HttpRequest req,
ILogger log)
{
string cognitive_service_key = Environment.GetEnvironmentVariable("cognitive_service_key");
string cognitive_service_endpoint = Environment.GetEnvironmentVariable("cognitive_service_endpoint");
int SentencesToSummarize = 3;
string requestBody = await new StreamReader(req.Body).ReadToEndAsync();
dynamic data = JsonConvert.DeserializeObject(requestBody);
string inputText = data.text;
var credentials = new ApiKeyServiceClientCredentials(cognitive_service_key);
var client = new TextAnalyticsClient(credentials)
{
Endpoint = cognitive_service_endpoint
};
dynamic result = new JObject();
//Detecting language first
var inputDocuments = new LanguageBatchInput(
new List <LanguageInput>
{
new LanguageInput(id: "1", text: inputText)
});
var langResults = await client.DetectLanguageAsync(false, inputDocuments);
string inputLanguage = null;
foreach (var document in langResults.Documents)
{
inputLanguage = document.DetectedLanguages[0].Iso6391Name;
}
result.language = inputLanguage;
log.LogInformation($"{result.ToString()}");
//Detecting sentiment of the input text
var inputDocuments2 = new MultiLanguageBatchInput(
new List <MultiLanguageInput>
{
new MultiLanguageInput(inputLanguage, "1", inputText)
});
var sentimentResult = await client.SentimentAsync(false, inputDocuments2);
double?sentimentScore = 0;
foreach (var document in sentimentResult.Documents)
{
sentimentScore = document.Score;
}
result.sentimentScore = sentimentScore;
log.LogInformation($"{result.ToString()}");
//Detecting entities in the text
var entitiesResult = await client.EntitiesAsync(false, inputDocuments2);
JArray entities = new JArray();
foreach (var document in entitiesResult.Documents)
{
dynamic entityObject = new JObject();
foreach (var entity in document.Entities)
{
entityObject.name = entity.Name;
entityObject.type = entity.Type;
entityObject.subtype = entity.SubType;
foreach (var match in entity.Matches)
{
entityObject.offset = match.Offset;
entityObject.length = match.Length;
entityObject.score = match.EntityTypeScore;
//log.LogInformation($"\t\t\tOffset: {match.Offset},\tLength: {match.Length},\tScore: {match.EntityTypeScore:F3}");
}
entities.Add(entityObject);
}
}
result.entities = entities;
log.LogInformation($"{result.ToString()}");
//Detecting keyphrases
var kpResults = await client.KeyPhrasesAsync(false, inputDocuments2);
JArray keyPhrases = new JArray();
var Phrases = new List <string>();
// Printing keyphrases
foreach (var document in kpResults.Documents)
{
foreach (string keyphrase in document.KeyPhrases)
{
keyPhrases.Add(keyphrase);
Phrases.Add(keyphrase);
}
}
result.keyphrases = keyPhrases;
//Generating text summary
String[] sentences = inputText.Split('!', '.', '?');
List <Match> matchList = new List <Match>();
int counter = 0;
// Take the 10 best words
var topPhrases = Phrases.Take(10);
foreach (var sentence in sentences)
{
double count = 0;
Match match = new Match();
foreach (var phrase in topPhrases)
{
if ((sentence.ToLower().IndexOf(phrase) > -1) &&
(sentence.Length > 20) && (WordCount(sentence) >= 3))
{
count++;
}
;
}
if (count > 0)
{
matchList.Add(new Match {
sentence = counter, total = count
});
}
counter++;
}
var MatchList = matchList.OrderByDescending(y => y.total).Take(SentencesToSummarize).OrderBy(x => x.sentence).ToList();
StringBuilder summary = new StringBuilder();
List <string> SentenceList = new List <string>();
int sentenceCount = 0;
for (int i = 0; i < MatchList.Count; i++)
{
summary.Append(sentences[MatchList[i].sentence] + ".");
sentenceCount++;
}
// If there are no sentences found, just take the first three
if (sentenceCount == 0)
{
for (int i = 0; i < Math.Min(SentencesToSummarize, sentences.Count()); i++)
{
summary.Append(sentences[MatchList[i].sentence] + ".");
}
}
result.summary = summary.ToString();
log.LogInformation($"{result.ToString()}");
return(inputText != null
? (ActionResult) new OkObjectResult($"{result.ToString()}")
: new BadRequestObjectResult("{ \"error\": \"Please pass the text input for the text analytics operations\""));
}
public static async Task <IActionResult> Run(
[HttpTrigger(AuthorizationLevel.Function, "get", "post", Route = null)] HttpRequest req,
ILogger log)
{
string cognitive_service_key = Environment.GetEnvironmentVariable("cognitive_service_key");
string cognitive_service_endpoint = Environment.GetEnvironmentVariable("cognitive_service_endpoint");
string requestBody = await new StreamReader(req.Body).ReadToEndAsync();
dynamic data = JsonConvert.DeserializeObject(requestBody);
string imageURL = data.imageurl;
//imageURL = "https://www.thehansindia.com/assets/9583_rahul-modi.jpg";
dynamic result = new JObject();
var credentials = new ApiKeyServiceClientCredentials(cognitive_service_key);
ComputerVisionClient computerVision = new ComputerVisionClient(credentials,
new System.Net.Http.DelegatingHandler[] { });
// Specify the Azure region
computerVision.Endpoint = cognitive_service_endpoint;
// Analyzing image from remote URL
if (!Uri.IsWellFormedUriString(imageURL, UriKind.Absolute))
{
log.LogError(
"\nInvalid remoteImageUrl:\n{0} \n", imageURL);
log.LogInformation("invalid image URL provided.");
}
else
{
ImageAnalysis analysis = new ImageAnalysis();
try
{
analysis = await computerVision.AnalyzeImageAsync(imageURL, features);
// Getting caption
result.caption = "";
if (analysis.Description.Captions.Count != 0)
{
result.caption = analysis.Description.Captions[0].Text;
}
// Getting faces
dynamic faces = new JArray();
dynamic celebrities = new JArray();
if (analysis.Faces.Count != 0)
{
foreach (var face in analysis.Faces)
{
dynamic faceObject = new JObject();
faceObject.rectangle = $"({face.FaceRectangle.Left.ToString()}, " +
$"{face.FaceRectangle.Top.ToString()}, " +
$"{face.FaceRectangle.Height.ToString()}, " +
$"{face.FaceRectangle.Width.ToString()})";
faceObject.age = face.Age;
faceObject.gender = face.Gender.ToString();
faces.Add(faceObject);
}
var celebRecognition = await computerVision.AnalyzeImageByDomainAsync("celebrities", imageURL);
dynamic celebResult = JsonConvert.DeserializeObject(celebRecognition.Result.ToString());
if (celebResult.celebrities.Count > 1)
{
foreach (var celeb in celebResult.celebrities)
{
celebrities.Add(celeb);
}
}
}
result.faces = faces;
result.celebrities = celebrities;
// Getting categories
dynamic categories = new JArray();
if (analysis.Categories.Count != 0)
{
foreach (var category in analysis.Categories)
{
categories.Add(category.Name);
}
}
result.categories = categories;
// Getting brands
dynamic brands = new JArray();
if (analysis.Brands.Count != 0)
{
foreach (var brand in analysis.Brands)
{
brands.Add(brand.Name);
}
}
result.brands = brands;
// Getting objects
dynamic objects = new JArray();
if (analysis.Objects.Count != 0)
{
foreach (var objectItem in analysis.Objects)
{
dynamic objObject = new JObject();
objObject.name = objectItem.ObjectProperty;
objObject.rectangle = $"({objectItem.Rectangle.X.ToString()}, " +
$"{objectItem.Rectangle.Y.ToString()}, " +
$"{objectItem.Rectangle.H.ToString()}, " +
$"{objectItem.Rectangle.W.ToString()})";
objects.Add(objObject);
}
}
result.objects = objects;
// Getting tags
dynamic tags = new JArray();
if (analysis.Tags.Count != 0)
{
foreach (var tag in analysis.Tags)
{
tags.Add(tag.Name);
}
}
result.tags = tags;
}
catch (Exception ex)
{
string exception = ex.Message;
}
}
return(imageURL != null
? (ActionResult) new OkObjectResult($"{result.ToString()}")
: new BadRequestObjectResult("{ \"error\": \"Please pass a valid image URL in the request\""));
}
