api_huggingface 0.6.1

//! Tests for Educational AI Tutor Example
//!
//! This test suite verifies the functionality of an intelligent tutoring system
//! that provides personalized learning assistance, concept explanations, and adaptive content generation.

#![ allow( clippy::trivially_copy_pass_by_ref, clippy::unused_self, clippy::struct_field_names ) ]

use api_huggingface::
{
  Client,
  environment::HuggingFaceEnvironmentImpl,
  components::
  {
  input::InferenceParameters,
  },
  secret::Secret,
};
use std::{ collections::HashMap, time::Instant };
use serde::{ Serialize, Deserialize };

#[ allow( missing_docs ) ]
/// Learning complexity levels for content adaptation
#[ derive( Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize ) ]
pub enum ComplexityLevel
{
  /// Elementary level (grades K-5)
  Elementary,
  /// Middle school level (grades 6-8)
  MiddleSchool,
  /// High school level (grades 9-12)
  HighSchool,
  /// University undergraduate level
  University,
  /// Graduate and advanced level
  Graduate,
}

impl ComplexityLevel
{
  /// Get complexity level name
  #[ must_use ]
  pub fn name( &self ) -> &'static str
  {
  match self
  {
      ComplexityLevel::Elementary => "Elementary",
      ComplexityLevel::MiddleSchool => "Middle School",
      ComplexityLevel::HighSchool => "High School",
      ComplexityLevel::University => "University",
      ComplexityLevel::Graduate => "Graduate",
  }
  }

  /// Get age range for this complexity level
  #[ must_use ]
  pub fn age_range( &self ) -> ( u8, u8 )
  {
  match self
  {
      ComplexityLevel::Elementary => ( 5, 11 ),
      ComplexityLevel::MiddleSchool => ( 11, 14 ),
      ComplexityLevel::HighSchool => ( 14, 18 ),
      ComplexityLevel::University => ( 18, 22 ),
      ComplexityLevel::Graduate => ( 22, 99 ),
  }
  }

  /// Get vocabulary complexity score (1-10)
  #[ must_use ]
  pub fn vocabulary_complexity( &self ) -> u8
  {
  match self
  {
      ComplexityLevel::Elementary => 2,
      ComplexityLevel::MiddleSchool => 4,
      ComplexityLevel::HighSchool => 6,
      ComplexityLevel::University => 8,
      ComplexityLevel::Graduate => 10,
  }
  }

  /// Get recommended explanation length (words)
  #[ must_use ]
  pub fn explanation_length_range( &self ) -> ( usize, usize )
  {
  match self
  {
      ComplexityLevel::Elementary => ( 50, 150 ),
      ComplexityLevel::MiddleSchool => ( 100, 250 ),
      ComplexityLevel::HighSchool => ( 150, 350 ),
      ComplexityLevel::University => ( 200, 500 ),
      ComplexityLevel::Graduate => ( 300, 800 ),
  }
  }

  /// Get preferred model for this complexity level
  #[ must_use ]
  pub fn preferred_model( &self ) -> &'static str
  {
  // Use Qwen model for all complexity levels (new Router API)
  // Provides better educational responses than legacy DialoGPT models
  "meta-llama/Llama-3.3-70B-Instruct"
  }
}

/// Learning styles for personalized content generation
#[ derive( Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize ) ]
pub enum LearningStyle
{
  /// Visual learners prefer diagrams and visual aids
  Visual,
  /// Auditory learners prefer verbal explanations
  Auditory,
  /// Kinesthetic learners prefer hands-on activities
  Kinesthetic,
  /// Reading/writing learners prefer text-based content
  ReadingWriting,
  /// Multimodal learners benefit from mixed approaches
  Multimodal,
}

impl LearningStyle
{
  /// Get learning style name
  #[ must_use ]
  pub fn name( &self ) -> &'static str
  {
  match self
  {
      LearningStyle::Visual => "Visual",
      LearningStyle::Auditory => "Auditory",
      LearningStyle::Kinesthetic => "Kinesthetic",
      LearningStyle::ReadingWriting => "Reading/Writing",
      LearningStyle::Multimodal => "Multimodal",
  }
  }

  /// Get recommended content format preferences
  #[ must_use ]
  pub fn content_preferences( &self ) -> Vec< &'static str >
  {
  match self
  {
      LearningStyle::Visual => vec![ "diagrams", "charts", "mind_maps", "infographics" ],
      LearningStyle::Auditory => vec![ "verbal_explanations", "discussions", "recordings", "mnemonics" ],
      LearningStyle::Kinesthetic => vec![ "hands_on_activities", "experiments", "simulations", "practice_problems" ],
      LearningStyle::ReadingWriting => vec![ "detailed_text", "note_taking", "summaries", "written_exercises" ],
      LearningStyle::Multimodal => vec![ "combined_approaches", "varied_formats", "interactive_content", "adaptive_materials" ],
  }
  }

  /// Get engagement strategies for this learning style
  #[ must_use ]
  pub fn engagement_strategies( &self ) -> Vec< &'static str >
  {
  match self
  {
      LearningStyle::Visual => vec![ "use_visual_metaphors", "create_concept_maps", "show_step_by_step_visuals" ],
      LearningStyle::Auditory => vec![ "explain_aloud", "use_analogies", "encourage_discussion" ],
      LearningStyle::Kinesthetic => vec![ "provide_examples", "suggest_practice", "break_into_steps" ],
      LearningStyle::ReadingWriting => vec![ "provide_detailed_notes", "suggest_writing_exercises", "offer_additional_reading" ],
      LearningStyle::Multimodal => vec![ "combine_multiple_methods", "provide_alternatives", "adapt_to_context" ],
  }
  }
}

/// Subject areas for educational content
#[ derive( Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize ) ]
pub enum Subject
{
  /// Mathematics and quantitative reasoning
  Mathematics,
  /// Physical sciences (physics, chemistry)
  Science,
  /// History and social studies
  History,
  /// Language arts and literature
  LanguageArts,
  /// Computer science and programming
  ComputerScience,
  /// Foreign language learning
  ForeignLanguage,
  /// Arts and creative subjects
  Arts,
  /// General knowledge and interdisciplinary topics
  General,
}

impl Subject
{
  /// Get subject name
  #[ must_use ]
  pub fn name( &self ) -> &'static str
  {
  match self
  {
      Subject::Mathematics => "Mathematics",
      Subject::Science => "Science",
      Subject::History => "History", 
      Subject::LanguageArts => "Language Arts",
      Subject::ComputerScience => "Computer Science",
      Subject::ForeignLanguage => "Foreign Language",
      Subject::Arts => "Arts",
      Subject::General => "General Knowledge",
  }
  }

  /// Get typical teaching approaches for this subject
  #[ must_use ]
  pub fn teaching_approaches( &self ) -> Vec< &'static str >
  {
  match self
  {
      Subject::Mathematics => vec![ "step_by_step_solutions", "problem_solving", "formula_derivation", "visual_proofs" ],
      Subject::Science => vec![ "conceptual_explanations", "real_world_applications", "experimental_design", "cause_and_effect" ],
      Subject::History => vec![ "chronological_narrative", "cause_and_effect", "primary_sources", "contextual_analysis" ],
      Subject::LanguageArts => vec![ "textual_analysis", "writing_techniques", "grammar_explanation", "literary_devices" ],
      Subject::ComputerScience => vec![ "code_examples", "algorithm_explanation", "debugging_techniques", "system_design" ],
      Subject::ForeignLanguage => vec![ "conversation_practice", "grammar_rules", "cultural_context", "pronunciation_guides" ],
      Subject::Arts => vec![ "technique_demonstration", "historical_context", "creative_inspiration", "skill_building" ],
      Subject::General => vec![ "interdisciplinary_connections", "critical_thinking", "research_methods", "synthesis" ],
  }
  }
}

/// Student profile for personalized learning
#[ derive( Debug, Clone ) ]
pub struct StudentProfile
{
  /// Student's unique identifier
  pub student_id : String,
  /// Student's name
  pub name : String,
  /// Current complexity level
  pub complexity_level : ComplexityLevel,
  /// Preferred learning style
  pub learning_style : LearningStyle,
  /// Strong subject areas
  pub strong_subjects : Vec< Subject >,
  /// Areas needing improvement
  pub weak_subjects : Vec< Subject >,
  /// Learning goals and objectives
  pub learning_goals : Vec< String >,
  /// Learning pace (1.0 = average, >1.0 = faster, <1.0 = slower)
  pub learning_pace : f32,
  /// Additional learning preferences
  pub preferences : HashMap< String, String >,
}

/// Concept explanation request
#[ derive( Debug, Clone ) ]
pub struct ConceptRequest
{
  /// The concept to explain
  pub concept : String,
  /// Subject area
  pub subject : Subject,
  /// Target complexity level
  pub complexity_level : ComplexityLevel,
  /// Student's learning style
  pub learning_style : LearningStyle,
  /// Additional context or background
  pub context : Option< String >,
  /// Prerequisites that student should know
  pub prerequisites : Vec< String >,
  /// Specific aspects to focus on
  pub focus_areas : Vec< String >,
}

/// Generated concept explanation
#[ derive( Debug, Clone ) ]
pub struct ConceptExplanation
{
  /// The concept that was explained
  pub concept : String,
  /// Generated explanation text
  pub explanation : String,
  /// Key points and takeaways
  pub key_points : Vec< String >,
  /// Examples and analogies used
  pub examples : Vec< String >,
  /// Related concepts to explore
  pub related_concepts : Vec< String >,
  /// Suggested practice activities
  pub practice_suggestions : Vec< String >,
  /// Pedagogical effectiveness score (0.0-1.0)
  pub effectiveness_score : f32,
  /// Complexity appropriateness score (0.0-1.0)
  pub complexity_score : f32,
  /// Generation time in milliseconds
  pub generation_time_ms : u64,
}

/// Student question for the tutor
#[ derive( Debug, Clone ) ]
pub struct StudentQuestion
{
  /// Question ID
  pub question_id : String,
  /// The student's question
  pub question_text : String,
  /// Subject area (if identifiable)
  pub subject : Option< Subject >,
  /// Complexity level of the question
  pub complexity_level : ComplexityLevel,
  /// Context or background information
  pub context : Option< String >,
  /// Student's current understanding level
  pub understanding_level : Option< f32 >,
}

/// Tutor's response to student question
#[ derive( Debug, Clone ) ]
pub struct TutorResponse
{
  /// Response to the question
  pub answer : String,
  /// Confidence in the answer (0.0-1.0)
  pub confidence : f32,
  /// Educational value score (0.0-1.0)
  pub educational_value : f32,
  /// Whether follow-up questions are suggested
  pub suggests_follow_up : bool,
  /// Follow-up questions or exercises
  pub follow_up_suggestions : Vec< String >,
  /// Assessment of student's understanding
  pub understanding_assessment : f32,
  /// Response generation time
  pub response_time_ms : u64,
}

/// Learning progress tracking
#[ derive( Debug, Clone ) ]
pub struct LearningProgress
{
  /// Subject being tracked
  pub subject : Subject,
  /// Current proficiency level (0.0-1.0)
  pub proficiency_level : f32,
  /// Improvement rate (change per session)
  pub improvement_rate : f32,
  /// Number of concepts mastered
  pub concepts_mastered : u32,
  /// Number of questions answered correctly
  pub correct_answers : u32,
  /// Total number of questions attempted
  pub total_questions : u32,
  /// Average response time (seconds)
  pub average_response_time : f32,
  /// Last updated timestamp
  pub last_updated : String,
}

/// Assessment and feedback generation
#[ derive( Debug, Clone ) ]
pub struct Assessment
{
  /// Assessment ID
  pub assessment_id : String,
  /// Subject area
  pub subject : Subject,
  /// Questions in the assessment
  pub questions : Vec< AssessmentQuestion >,
  /// Total points possible
  pub total_points : u32,
  /// Time limit in minutes
  pub time_limit_minutes : Option< u32 >,
  /// Difficulty level
  pub difficulty_level : ComplexityLevel,
}

/// Individual assessment question
#[ derive( Debug, Clone ) ]
pub struct AssessmentQuestion
{
  /// Question ID
  pub question_id : String,
  /// Question text
  pub question : String,
  /// Question type
  pub question_type : QuestionType,
  /// Correct answer(s)
  pub correct_answers : Vec< String >,
  /// Points value
  pub points : u32,
  /// Explanation for the correct answer
  pub explanation : String,
}

/// Types of assessment questions
#[ derive( Debug, Clone, Copy, PartialEq, Eq, Hash ) ]
pub enum QuestionType
{
  /// Multiple choice question
  MultipleChoice,
  /// True/false question
  TrueFalse,
  /// Short answer question
  ShortAnswer,
  /// Essay question
  Essay,
  /// Fill in the blank
  FillInTheBlank,
  /// Mathematical problem
  Mathematical,
}

/// Assessment results and feedback
#[ derive( Debug, Clone ) ]
pub struct AssessmentResult
{
  /// Student's responses
  pub responses : Vec< String >,
  /// Score achieved
  pub score : u32,
  /// Percentage score
  pub percentage : f32,
  /// Time taken in minutes
  pub time_taken : f32,
  /// Detailed feedback for each question
  pub question_feedback : Vec< QuestionFeedback >,
  /// Overall performance assessment
  pub performance_summary : String,
  /// Areas for improvement
  pub improvement_areas : Vec< String >,
  /// Recommended next steps
  pub recommendations : Vec< String >,
}

/// Feedback for individual question
#[ derive( Debug, Clone ) ]
pub struct QuestionFeedback
{
  /// Question ID
  pub question_id : String,
  /// Whether the answer was correct
  pub is_correct : bool,
  /// Points earned for this question
  pub points_earned : u32,
  /// Feedback message
  pub feedback : String,
  /// Hints for improvement
  pub improvement_hints : Vec< String >,
}

/// AI Tutor platform
#[ derive( Debug, Clone ) ]
pub struct AITutorPlatform
{
  /// API client
  client : Client< HuggingFaceEnvironmentImpl >,
  /// Registered student profiles
  students : HashMap< String, StudentProfile >,
  /// Learning progress tracking
  progress_tracking : HashMap< String, HashMap< Subject, LearningProgress > >,
  /// Platform configuration
  config : TutorConfig,
  /// Teaching statistics
  statistics : TutorStatistics,
}

/// Tutor platform configuration
#[ derive( Debug, Clone ) ]
pub struct TutorConfig
{
  /// Default model for content generation
  pub default_model : String,
  /// Maximum explanation length
  pub max_explanation_length : usize,
  /// Enable adaptive difficulty adjustment
  pub enable_adaptive_difficulty : bool,
  /// Progress tracking frequency
  pub progress_update_frequency : u32,
  /// Default learning style for new students
  pub default_learning_style : LearningStyle,
  /// Assessment frequency (sessions between assessments)
  pub assessment_frequency : u32,
}

impl Default for TutorConfig
{
  fn default() -> Self
  {
  Self
  {
      default_model : "meta-llama/Llama-3.3-70B-Instruct".to_string(),
      max_explanation_length : 1000,
      enable_adaptive_difficulty : true,
      progress_update_frequency : 5,
      default_learning_style : LearningStyle::Multimodal,
      assessment_frequency : 10,
  }
  }
}

/// Platform teaching statistics
#[ derive( Debug, Clone, Default ) ]
pub struct TutorStatistics
{
  /// Total number of explanations generated
  pub total_explanations : u64,
  /// Total questions answered
  pub total_questions_answered : u64,
  /// Average explanation effectiveness
  pub average_effectiveness : f32,
  /// Total learning time (minutes)
  pub total_learning_time : u64,
  /// Students served
  pub students_served : u32,
  /// Subject popularity
  pub subject_popularity : HashMap< Subject, u32 >,
  /// Complexity level distribution
  pub complexity_distribution : HashMap< ComplexityLevel, u32 >,
}

impl AITutorPlatform
{
  /// Create a new AI tutor platform
  #[ must_use ]
  pub fn new( client : Client< HuggingFaceEnvironmentImpl > ) -> Self
  {
  Self
  {
      client,
      students : HashMap::new(),
      progress_tracking : HashMap::new(),
      config : TutorConfig::default(),
      statistics : TutorStatistics::default(),
  }
  }

  /// Create platform with custom configuration
  #[ must_use ]
  pub fn with_config( client : Client< HuggingFaceEnvironmentImpl >, config : TutorConfig ) -> Self
  {
  Self
  {
      client,
      students : HashMap::new(),
      progress_tracking : HashMap::new(),
      config,
      statistics : TutorStatistics::default(),
  }
  }

  /// Register a new student
  pub fn register_student( &mut self, profile : StudentProfile )
  {
  let student_id = profile.student_id.clone();
  self.students.insert( student_id.clone(), profile );
  
  // Initialize progress tracking
  let mut progress = HashMap::new();
  for subject in [ Subject::Mathematics, Subject::Science, Subject::History, Subject::LanguageArts, 
                     Subject::ComputerScience, Subject::ForeignLanguage, Subject::Arts, Subject::General ]
  {
      progress.insert( subject, LearningProgress
      {
  subject,
  proficiency_level : 0.5, // Start at average
  improvement_rate : 0.0,
  concepts_mastered : 0,
  correct_answers : 0,
  total_questions : 0,
  average_response_time : 0.0,
  last_updated : "2023-01-01".to_string(),
      } );
  }
  self.progress_tracking.insert( student_id, progress );
  self.statistics.students_served += 1;
  }

  /// Generate concept explanation
  ///
  /// # Errors
  /// Returns error if explanation generation fails
  pub async fn explain_concept( &mut self, request : &ConceptRequest ) -> Result< ConceptExplanation, Box< dyn std::error::Error > >
  {
  // Build explanation prompt
  let prompt = self.build_explanation_prompt( request );

  // Select appropriate model
  let model = request.complexity_level.preferred_model();

  // Set generation parameters
  let params = InferenceParameters::new()
      .with_max_new_tokens( self.calculate_max_tokens( request.complexity_level ) )
      .with_temperature( 0.7 ) // Balanced creativity and consistency
      .with_top_p( 0.9 );

  // Generate explanation
  let response = self.client.inference().create_with_parameters( &prompt, model, params ).await?;

  let explanation_text = response.extract_text_or_default( "Unable to generate explanation." );

  // Process explanation and extract components
  let explanation = self.process_explanation( &explanation_text, request );

  // Update statistics
  self.update_explanation_statistics( request.subject, request.complexity_level );

  Ok( explanation )
  }

  /// Answer student question
  ///
  /// # Errors
  /// Returns error if question answering fails
  pub async fn answer_question( &mut self, question : &StudentQuestion, student_id : Option< &str > ) -> Result< TutorResponse, Box< dyn std::error::Error > >
  {
  let start_time = Instant::now();

  // Build question answering prompt
  let prompt = self.build_question_prompt( question, student_id );

  // Select model based on complexity
  let model = question.complexity_level.preferred_model();

  // Set parameters for question answering
  let params = InferenceParameters::new()
      .with_max_new_tokens( 300 )
      .with_temperature( 0.5 ) // Lower temperature for more focused answers
      .with_top_p( 0.8 );

  // Generate response
  let response = self.client.inference().create_with_parameters( &prompt, model, params ).await?;

  let answer_text = response.extract_text_or_default( "I'm not sure how to answer that question." );

  let response_time = u64::try_from( start_time.elapsed().as_millis() ).unwrap_or( 0 );

  // Process answer and generate response
  let tutor_response = self.process_answer( &answer_text, question, response_time );

  // Update progress if student ID provided
  if let Some( id ) = student_id
  {
      self.update_student_progress( id, question, &tutor_response );
  }

  // Update statistics
  self.statistics.total_questions_answered += 1;

  Ok( tutor_response )
  }

  /// Generate assessment for student
  #[ must_use ]
  pub fn generate_assessment( &self, subject : Subject, complexity_level : ComplexityLevel, num_questions : usize ) -> Assessment
  {
  let mut questions = Vec::new();

  // Generate different types of questions
  for i in 0..num_questions
  {
      let question_type = match i % 4
      {
  0 => QuestionType::MultipleChoice,
  1 => QuestionType::TrueFalse,
  2 => QuestionType::ShortAnswer,
  _ => QuestionType::FillInTheBlank,
      };

      questions.push( AssessmentQuestion
      {
  question_id : format!( "q_{}", i + 1 ),
  question : format!( "Sample {} question {} for {complexity_level:?}", subject.name(), i + 1 ),
  question_type,
  correct_answers : vec![ "Sample correct answer".to_string() ],
  points : 10,
  explanation : "This is a sample explanation for the correct answer.".to_string(),
      } );
  }

  let total_points = u32::try_from( num_questions * 10 ).unwrap_or( 0 );
  let time_limit_minutes = u32::try_from( num_questions ).unwrap_or( 0 ) * 3;

  Assessment
  {
      assessment_id : format!( "assessment_{}_{complexity_level:?}_{}", subject.name(), chrono::Utc::now().timestamp() ),
      subject,
      questions,
      total_points,
      time_limit_minutes : Some( time_limit_minutes ), // 3 minutes per question
      difficulty_level : complexity_level,
  }
  }

  /// Evaluate assessment responses
  #[ must_use ]
  pub fn evaluate_assessment( &self, assessment : &Assessment, responses : Vec< String > ) -> AssessmentResult
  {
  let mut score = 0;
  let mut question_feedback = Vec::new();
  let time_taken = 15.5; // Simulated time

  for ( i, response ) in responses.iter().enumerate()
  {
      if let Some( question ) = assessment.questions.get( i )
      {
  let is_correct = question.correct_answers.contains( response );
  let points_earned = if is_correct { question.points } else { 0 };
  score += points_earned;

  question_feedback.push( QuestionFeedback
  {
          question_id : question.question_id.clone(),
          is_correct,
          points_earned,
          feedback : if is_correct
          {
      "Correct! Well done.".to_string()
          }
          else
          {
      format!( "Incorrect. The correct answer is : {}", question.correct_answers[ 0 ] )
          },
          improvement_hints : if is_correct
          {
      Vec::new()
          }
          else
          {
      vec![ "Review the concept explanation and try practice problems.".to_string() ]
          },
  } );
      }
  }

  let percentage = ( score as f32 / assessment.total_points as f32 ) * 100.0;

  AssessmentResult
  {
      responses,
      score,
      percentage,
      time_taken,
      question_feedback,
      performance_summary : if percentage >= 90.0 { "Excellent performance!" } 
                           else if percentage >= 80.0 { "Good work with room for improvement." }
                           else if percentage >= 70.0 { "Satisfactory performance." }
                           else { "Needs significant improvement." }.to_string(),
      improvement_areas : vec![ "Practice more problems".to_string(), "Review key concepts".to_string() ],
      recommendations : vec![ "Focus on fundamentals".to_string(), "Seek additional help".to_string() ],
  }
  }

  /// Get student progress
  #[ must_use ]
  pub fn get_student_progress( &self, student_id : &str ) -> Option< &HashMap< Subject, LearningProgress > >
  {
  self.progress_tracking.get( student_id )
  }

  /// Get platform statistics
  #[ must_use ]
  pub fn get_statistics( &self ) -> &TutorStatistics
  {
  &self.statistics
  }

  /// Build explanation prompt
  #[ allow( clippy::unused_self ) ]
  fn build_explanation_prompt( &self, request : &ConceptRequest ) -> String
  {
  let mut prompt = format!(
      "Explain the concept of '{}' in {} for {} level students.\n",
      request.concept,
      request.subject.name(),
      request.complexity_level.name()
  );

  // Add learning style considerations
  let strategies = request.learning_style.engagement_strategies();
  let strategies_joined = strategies.join( ", " );
  prompt.push_str( "Use " );
  prompt.push_str( &strategies_joined );
  prompt.push_str( " teaching strategies. " );

  // Add context if provided
  if let Some( ref context ) = request.context
  {
      prompt.push_str( "Context : " );
      prompt.push_str( context );
      prompt.push_str( ". " );
  }

  // Add prerequisites
  if !request.prerequisites.is_empty()
  {
      let prereqs_joined = request.prerequisites.join( ", " );
      prompt.push_str( "Assume knowledge of : " );
      prompt.push_str( &prereqs_joined );
      prompt.push_str( ". " );
  }

  prompt.push_str( "\n\nExplanation:" );

  prompt
  }

  /// Build question answering prompt
  #[ allow( clippy::unused_self ) ]
  fn build_question_prompt( &self, question : &StudentQuestion, student_id : Option< &str > ) -> String
  {
  let mut prompt = format!(
      "A {} level student asks : '{}'\n",
      question.complexity_level.name(),
      question.question_text
  );

  // Add student context if available
  if let Some( id ) = student_id
  {
      if let Some( profile ) = self.students.get( id )
      {
  let learning_style = profile.learning_style.name();
  prompt.push_str( "Student learning style : " );
  prompt.push_str( learning_style );
  prompt.push_str( ". " );
      }
  }

  // Add subject context if identified
  if let Some( subject ) = question.subject
  {
      let subject_name = subject.name();
      prompt.push_str( "Subject area : " );
      prompt.push_str( subject_name );
      prompt.push_str( ". " );
  }

  prompt.push_str( "\n\nProvide a helpful, educational answer:" );

  prompt
  }

  /// Calculate max tokens based on complexity level
  #[ allow( clippy::cast_precision_loss, clippy::cast_possible_truncation, clippy::cast_sign_loss, clippy::unused_self ) ]
  fn calculate_max_tokens( &self, complexity_level : ComplexityLevel ) -> u32
  {
  let ( _min_words, max_words ) = complexity_level.explanation_length_range();
  ( max_words as f32 * 1.3 ) as u32 // Estimate tokens as 1.3x words
  }

  /// Process explanation response
  #[ allow( clippy::unused_self ) ]
  fn process_explanation( &self, text : &str, request : &ConceptRequest ) -> ConceptExplanation
  {
  let word_count = text.split_whitespace().count();
  let ( min_words, max_words ) = request.complexity_level.explanation_length_range();

  // Calculate scores
  let complexity_score = if word_count >= min_words && word_count <= max_words { 1.0 }
                          else if word_count < min_words { 0.7 }
                          else { 0.8 };

  let effectiveness_score = 0.8; // Simplified scoring

  // Extract key components (simplified)
  let key_points = vec![
      "Main concept definition".to_string(),
      "Key characteristics".to_string(),
      "Important relationships".to_string()
  ];

  let examples = vec![
      "Practical example 1".to_string(),
      "Analogy or metaphor".to_string()
  ];

  let related_concepts = vec![
      "Related concept A".to_string(),
      "Related concept B".to_string()
  ];

  let practice_suggestions = vec![
      "Practice problem 1".to_string(),
      "Hands-on activity".to_string()
  ];

  ConceptExplanation
  {
      concept : request.concept.clone(),
      explanation : text.trim().to_string(),
      key_points,
      examples,
      related_concepts,
      practice_suggestions,
      effectiveness_score,
      complexity_score,
      generation_time_ms : 150, // Simplified
  }
  }

  /// Process answer response
  #[ allow( clippy::unused_self ) ]
  fn process_answer( &self, text : &str, _question : &StudentQuestion, response_time : u64 ) -> TutorResponse
  {
  let confidence = if text.len() > 50 { 0.8 } else { 0.6 };
  let educational_value = 0.75; // Simplified scoring

  let follow_up_suggestions = vec![
      "Would you like me to explain this concept in more detail?".to_string(),
      "Do you have any related questions?".to_string(),
  ];

  TutorResponse
  {
      answer : text.trim().to_string(),
      confidence,
      educational_value,
      suggests_follow_up : true,
      follow_up_suggestions,
      understanding_assessment : 0.7, // Simplified assessment
      response_time_ms : response_time,
  }
  }

  /// Update student progress
  fn update_student_progress( &mut self, student_id : &str, question : &StudentQuestion, response : &TutorResponse )
  {
  if let Some( student_progress ) = self.progress_tracking.get_mut( student_id )
  {
      if let Some( subject ) = question.subject
      {
  if let Some( progress ) = student_progress.get_mut( &subject )
  {
          progress.total_questions += 1;
          if response.understanding_assessment > 0.7
          {
      progress.correct_answers += 1;
          }
          
          // Update proficiency based on performance
          let accuracy = progress.correct_answers as f32 / progress.total_questions as f32;
          progress.proficiency_level = ( progress.proficiency_level + accuracy ) / 2.0;
          progress.last_updated = "2023-12-01".to_string(); // Simplified timestamp
  }
      }
  }
  }

  /// Update explanation statistics
  fn update_explanation_statistics( &mut self, subject : Subject, complexity_level : ComplexityLevel )
  {
  self.statistics.total_explanations += 1;
  *self.statistics.subject_popularity.entry( subject ).or_insert( 0 ) += 1;
  *self.statistics.complexity_distribution.entry( complexity_level ).or_insert( 0 ) += 1;
  }
}

// Helper function to create test client
fn create_test_client() -> Option< Client< HuggingFaceEnvironmentImpl > >
{
  let api_key = super::get_api_key_for_testing()?;
  let secret = Secret::new( api_key );
  let env = HuggingFaceEnvironmentImpl::build( secret, None ).ok()?;
  Client::build( env ).ok()
}

// Helper function to create sample student profile
fn create_sample_student_profile() -> StudentProfile
{
  StudentProfile
  {
  student_id : "student_001".to_string(),
  name : "Alice Johnson".to_string(),
  complexity_level : ComplexityLevel::HighSchool,
  learning_style : LearningStyle::Visual,
  strong_subjects : vec![ Subject::Mathematics, Subject::Science ],
  weak_subjects : vec![ Subject::History, Subject::LanguageArts ],
  learning_goals : vec![ 
      "Improve understanding of calculus".to_string(),
      "Master essay writing techniques".to_string() 
  ],
  learning_pace : 1.2, // Slightly faster than average
  preferences : HashMap::new(),
  }
}

// Helper function to create sample concept requests
fn create_sample_concept_requests() -> Vec< ConceptRequest >
{
  vec![
  ConceptRequest
  {
      concept : "Photosynthesis".to_string(),
      subject : Subject::Science,
      complexity_level : ComplexityLevel::MiddleSchool,
      learning_style : LearningStyle::Visual,
      context : Some( "Plant biology unit".to_string() ),
      prerequisites : vec![ "Basic cell structure".to_string(), "Chemical reactions".to_string() ],
      focus_areas : vec![ "Process steps".to_string(), "Importance to ecosystems".to_string() ],
  },
  ConceptRequest
  {
      concept : "Quadratic equations".to_string(),
      subject : Subject::Mathematics,
      complexity_level : ComplexityLevel::HighSchool,
      learning_style : LearningStyle::Kinesthetic,
      context : Some( "Algebra II course".to_string() ),
      prerequisites : vec![ "Linear equations".to_string(), "Factoring".to_string() ],
      focus_areas : vec![ "Solving methods".to_string(), "Graphing".to_string() ],
  },
  ]
}