Class: Rack::AI::Providers::HuggingFace

Inherits:
Base
  • Object
show all
Defined in:
lib/rack/ai/providers/huggingface.rb

Constant Summary collapse

API_BASE_URL = 
"https://api-inference.huggingface.co"

Instance Attribute Summary

Attributes inherited from Base

#config

Instance Method Summary collapse

Methods inherited from Base

#build_request_data, #healthy?, #validate!

Constructor Details

#initialize(config) ⇒ HuggingFace

Returns a new instance of HuggingFace.



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# File 'lib/rack/ai/providers/huggingface.rb', line 13

def initialize(config)
  super
  @client = build_client
end

Instance Method Details

#analyze_patterns(data) ⇒ Object 



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# File 'lib/rack/ai/providers/huggingface.rb', line 71

def analyze_patterns(data)
  # Use a general text analysis approach
  text = "Analyze these web traffic patterns: #{data.to_json}"
  
  response = @client.post("models/facebook/bart-large-cnn") do |req|
    req.body = {
      inputs: text,
      parameters: {
        max_length: 200,
        min_length: 50
      }
    }.to_json
  end

  handle_api_error(response) unless response.success?
  
  result = JSON.parse(response.body)
  summary = result.first&.dig("summary_text") || ""
  
  {
    anomalies: extract_anomalies(summary),
    trends: extract_trends(summary),
    recommendations: extract_recommendations(summary),
    confidence: 0.7,
    provider: :huggingface
  }
end

#classify_request(request_data) ⇒ Object 



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# File 'lib/rack/ai/providers/huggingface.rb', line 18

def classify_request(request_data)
  # Use a text classification model for request analysis
  text = build_request_text(request_data)
  
  response = @client.post("models/microsoft/DialoGPT-medium") do |req|
    req.body = {
      inputs: "Classify this web request as human, bot, spam, or suspicious: #{text}",
      parameters: {
        max_length: 50,
        temperature: 0.1
      }
    }.to_json
  end

  handle_api_error(response) unless response.success?
  
  result = JSON.parse(response.body)
  generated_text = result.first&.dig("generated_text") || ""
  
  # Parse classification from generated text
  classification = extract_classification(generated_text)
  
  {
    classification: classification,
    confidence: 0.8, # HuggingFace doesn't always provide confidence scores
    reasoning: generated_text,
    provider: :huggingface
  }
end

#detect_anomalies(request_data) ⇒ Object 



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# File 'lib/rack/ai/providers/huggingface.rb', line 99

def detect_anomalies(request_data)
  # Use anomaly detection approach
  text = build_security_analysis_text(request_data)
  
  response = @client.post("models/microsoft/DialoGPT-medium") do |req|
    req.body = {
      inputs: "Security analysis: #{text}. Threat level (low/medium/high):",
      parameters: {
        max_length: 100,
        temperature: 0.1
      }
    }.to_json
  end

  handle_api_error(response) unless response.success?
  
  result = JSON.parse(response.body)
  analysis = result.first&.dig("generated_text") || ""
  
  {
    threat_level: extract_threat_level(analysis),
    anomalies: extract_security_anomalies(analysis),
    confidence: 0.7,
    provider: :huggingface
  }
end

#enhance_content(content, enhancement_type) ⇒ Object 



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# File 'lib/rack/ai/providers/huggingface.rb', line 126

def enhance_content(content, enhancement_type)
  model = case enhancement_type
          when :seo
            "models/facebook/bart-large-cnn"
          when :readability
            "models/facebook/bart-large"
          else
            "models/t5-base"
          end

  prompt = build_enhancement_prompt(content, enhancement_type)
  
  response = @client.post(model) do |req|
    req.body = {
      inputs: prompt,
      parameters: {
        max_length: 500,
        temperature: 0.3
      }
    }.to_json
  end

  handle_api_error(response) unless response.success?
  
  result = JSON.parse(response.body)
  enhanced = result.first&.dig("generated_text") || content
  
  {
    original_content: content,
    enhanced_content: enhanced,
    enhancement_type: enhancement_type,
    provider: :huggingface
  }
end

#moderate_content(content, options = {}) ⇒ Object 



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# File 'lib/rack/ai/providers/huggingface.rb', line 48

def moderate_content(content, options = {})
  # Use toxicity detection model
  response = @client.post("models/unitary/toxic-bert") do |req|
    req.body = {
      inputs: content
    }.to_json
  end

  handle_api_error(response) unless response.success?
  
  result = JSON.parse(response.body)
  
  # HuggingFace toxicity models return classification results
  toxic_score = result.first&.find { |r| r["label"] == "TOXIC" }&.dig("score") || 0.0
  
  {
    flagged: toxic_score > 0.5,
    categories: { toxicity: toxic_score > 0.5 },
    category_scores: { toxicity: toxic_score },
    provider: :huggingface
  }
end

#ping ⇒ Object 



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# File 'lib/rack/ai/providers/huggingface.rb', line 161

def ping
  response = @client.get("models")
  response.success?
end