Ollama Special Needs Support Tools: Transform Special Education with Local AI

# Install models optimized for educational content
ollama pull llama2:7b
ollama pull codellama:7b
ollama pull mistral:7b

import ollama
import gradio as gr

def simplify_text(original_text, grade_level):
    """
    Simplifies text to specified grade level
    Maintains key concepts while reducing complexity
    """
    prompt = f"""
    Simplify this text for {grade_level} grade reading level.
    Keep all important information.
    Use shorter sentences and common words.
    
    Original text: {original_text}
    
    Simplified version:
    """
    
    response = ollama.generate(
        model='llama2:7b',
        prompt=prompt,
        stream=False
    )
    
    return response['response']

# Create user interface
interface = gr.Interface(
    fn=simplify_text,
    inputs=[
        gr.Textbox(label="Original Text", lines=5),
        gr.Dropdown(["3rd", "4th", "5th", "6th"], label="Target Grade Level")
    ],
    outputs=gr.Textbox(label="Simplified Text", lines=5),
    title="Text Simplification Tool",
    description="Makes content accessible for students with reading difficulties"
)

interface.launch(server_name="0.0.0.0", server_port=7860)

import ollama
from datetime import datetime, timedelta

def create_visual_schedule(activities, student_name):
    """
    Generates structured daily schedule with visual cues
    Includes transition warnings and sensory breaks
    """
    prompt = f"""
    Create a visual daily schedule for {student_name}.
    Include these activities: {', '.join(activities)}
    
    Format each activity with:
    - Simple description (5-8 words)
    - Duration estimate
    - Transition warning (2 minutes before)
    - Sensory break opportunities
    
    Use clear, positive language.
    """
    
    response = ollama.generate(
        model='mistral:7b',
        prompt=prompt,
        stream=False
    )
    
    return response['response']

# Example usage
daily_activities = [
    "Morning circle time",
    "Math lesson",
    "Sensory break",
    "Reading practice",
    "Lunch time",
    "Science experiment",
    "Art project",
    "End of day cleanup"
]

schedule = create_visual_schedule(daily_activities, "Alex")
print(schedule)

def add_sensory_accommodations(base_schedule, sensory_needs):
    """
    Adds specific accommodations for sensory processing
    Customizes environment and timing recommendations
    """
    accommodation_prompt = f"""
    Modify this schedule for a student with these sensory needs: {sensory_needs}
    
    Base schedule: {base_schedule}
    
    Add appropriate:
    - Quiet spaces for overwhelm
    - Movement breaks
    - Fidget tool suggestions
    - Lighting adjustments
    - Sound management
    """
    
    response = ollama.generate(
        model='llama2:7b',
        prompt=accommodation_prompt
    )
    
    return response['response']

import ollama
import json

class CommunicationBoard:
    def __init__(self):
        self.symbol_database = self.load_symbols()
    
    def text_to_symbols(self, message):
        """
        Converts text to communication symbols
        Supports PECS and AAC symbol systems
        """
        prompt = f"""
        Convert this message to communication symbols:
        "{message}"
        
        Provide:
        1. Core symbols needed
        2. Symbol sequence order
        3. Alternative phrases
        4. Emotion indicators
        
        Focus on essential communication elements.
        """
        
        response = ollama.generate(
            model='llama2:7b',
            prompt=prompt
        )
        
        return self.parse_symbol_response(response['response'])
    
    def expand_symbol_message(self, symbols):
        """
        Expands symbol selection into full sentences
        Helps teachers understand student intent
        """
        prompt = f"""
        A student selected these symbols: {symbols}
        
        Provide possible meanings:
        1. Most likely message
        2. Alternative interpretations
        3. Follow-up questions to clarify
        
        Consider context of classroom activities.
        """
        
        response = ollama.generate(
            model='mistral:7b',
            prompt=prompt
        )
        
        return response['response']

# Usage example
comm_board = CommunicationBoard()
symbols_needed = comm_board.text_to_symbols("I need help with math")
expanded_message = comm_board.expand_symbol_message(["help", "numbers", "confused"])

def sync_with_aac_device(ollama_output, device_format):
    """
    Converts Ollama responses to AAC device formats
    Supports Proloquo2Go, LAMP, and other systems
    """
    conversion_map = {
        'proloquo2go': convert_to_proloquo_format,
        'lamp': convert_to_lamp_format,
        'unity': convert_to_unity_format
    }
    
    return conversion_map[device_format](ollama_output)

import ollama

class ReadingSupport:
    def __init__(self, model='llama2:7b'):
        self.model = model
    
    def create_reading_scaffold(self, text, disability_type, grade_level):
        """
        Creates multi-level reading support
        Adapts to specific learning disabilities
        """
        scaffolds = {
            'dyslexia': self.dyslexia_scaffold,
            'adhd': self.adhd_scaffold,
            'autism': self.autism_scaffold,
            'intellectual': self.intellectual_scaffold
        }
        
        return scaffolds[disability_type](text, grade_level)
    
    def dyslexia_scaffold(self, text, grade_level):
        """
        Provides phonics support and word breakdown
        Includes audio cues and visual helpers
        """
        prompt = f"""
        Create reading support for a {grade_level} student with dyslexia:
        
        Text: {text}
        
        Provide:
        1. Difficult words with phonetic breakdown
        2. Main idea in simple terms
        3. Picture cues suggestions
        4. Comprehension questions (3 levels)
        5. Word families from the text
        
        Focus on decoding strategies.
        """
        
        response = ollama.generate(model=self.model, prompt=prompt)
        return self.format_dyslexia_response(response['response'])
    
    def adhd_scaffold(self, text, grade_level):
        """
        Breaks content into attention-friendly chunks
        Includes focus reminders and movement breaks
        """
        prompt = f"""
        Adapt this text for a {grade_level} student with ADHD:
        
        Text: {text}
        
        Create:
        1. Text in 2-3 sentence chunks
        2. Attention grabbers between sections
        3. Quick comprehension checks
        4. Movement break suggestions
        5. Main points summary
        
        Keep engagement high throughout.
        """
        
        response = ollama.generate(model=self.model, prompt=prompt)
        return self.format_adhd_response(response['response'])

# Implementation example
reading_tool = ReadingSupport()
scaffold = reading_tool.create_reading_scaffold(
    text="The water cycle involves evaporation, condensation, and precipitation...",
    disability_type="adhd",
    grade_level="4th"
)

def assess_comprehension(student_responses, original_text):
    """
    Evaluates understanding and provides next steps
    Identifies specific areas needing support
    """
    prompt = f"""
    Student answered these comprehension questions:
    {student_responses}
    
    About this text: {original_text}
    
    Analyze:
    1. Understanding level (1-5 scale)
    2. Specific concepts mastered
    3. Areas needing reteaching
    4. Suggested interventions
    5. Next reading level recommendation
    """
    
    response = ollama.generate(model='mistral:7b', prompt=prompt)
    return parse_assessment_results(response['response'])

# Step 1: Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Step 2: Start the service
ollama serve

# Step 3: Download education-focused models
ollama pull llama2:7b
ollama pull mistral:7b

# Step 4: Test installation
ollama run llama2:7b "Explain photosynthesis in simple terms"

# docker-compose.yml for school deployment
version: '3.8'
services:
  ollama:
    image: ollama/ollama:latest
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    environment:
      - OLLAMA_HOST=0.0.0.0
    networks:
      - school_network

  accessibility_tools:
    build: ./accessibility_apps
    ports:
      - "7860:7860"
    depends_on:
      - ollama
    networks:
      - school_network

volumes:
  ollama_data:

networks:
  school_network:
    driver: bridge

# Access control for student safety
import requests
from functools import wraps

def secure_ollama_access(allowed_models=None):
    """
    Restricts model access and content filtering
    Ensures age-appropriate responses
    """
    if allowed_models is None:
        allowed_models = ['llama2:7b', 'mistral:7b']
    
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            # Validate model selection
            if kwargs.get('model') not in allowed_models:
                raise ValueError("Model not approved for educational use")
            
            # Content filtering
            result = func(*args, **kwargs)
            return filter_educational_content(result)
        
        return wrapper
    return decorator

def filter_educational_content(response):
    """
    Ensures responses are appropriate for school environment
    Removes potentially harmful or off-topic content
    """
    filtering_rules = [
        'no_personal_information',
        'age_appropriate_language',
        'educational_focus_only',
        'no_external_links'
    ]
    
    return apply_content_filters(response, filtering_rules)

def generate_behavior_plan(student_profile, target_behaviors):
    """
    Creates individualized behavior intervention plans
    Includes positive reinforcement strategies
    """
    prompt = f"""
    Create a behavior support plan for:
    Student profile: {student_profile}
    Target behaviors: {target_behaviors}
    
    Include:
    1. Antecedent strategies (prevention)
    2. Teaching replacement behaviors
    3. Response strategies for challenging moments
    4. Data collection methods
    5. Reinforcement schedule
    
    Focus on positive, evidence-based approaches.
    """
    
    response = ollama.generate(model='llama2:7b', prompt=prompt)
    return format_behavior_plan(response['response'])

def optimize_sensory_environment(classroom_data, student_needs):
    """
    Analyzes classroom sensory factors
    Provides accommodation recommendations
    """
    prompt = f"""
    Optimize this classroom for sensory needs:
    
    Classroom: {classroom_data}
    Student needs: {student_needs}
    
    Recommend adjustments for:
    1. Lighting (natural/artificial)
    2. Sound levels and sources
    3. Seating arrangements
    4. Visual organization
    5. Movement opportunities
    6. Quiet spaces
    
    Consider budget-friendly solutions.
    """
    
    response = ollama.generate(model='mistral:7b', prompt=prompt)
    return parse_environment_recommendations(response['response'])

def create_social_skills_lessons(age_group, skill_focus, session_length):
    """
    Develops structured social skills instruction
    Includes role-play scenarios and practice activities
    """
    prompt = f"""
    Design a {session_length}-minute social skills lesson:
    
    Age group: {age_group}
    Skill focus: {skill_focus}
    
    Structure:
    1. Lesson objective (measurable)
    2. Opening activity (engagement)
    3. Direct instruction (explanation)
    4. Guided practice (role-play)
    5. Independent practice (real scenarios)
    6. Closing reflection
    7. Home practice suggestions
    
    Include visual supports and concrete examples.
    """
    
    response = ollama.generate(model='llama2:7b', prompt=prompt)
    return format_lesson_plan(response['response'])

class AccessibilityMetrics:
    def __init__(self):
        self.metrics = {
            'engagement_time': [],
            'task_completion_rate': [],
            'independence_level': [],
            'communication_frequency': [],
            'behavioral_incidents': []
        }
    
    def track_student_progress(self, student_id, session_data):
        """
        Records quantitative progress metrics
        Identifies trends and areas for improvement
        """
        self.metrics['engagement_time'].append({
            'student': student_id,
            'date': session_data['date'],
            'duration': session_data['active_time'],
            'tool_used': session_data['ollama_tool']
        })
        
        return self.calculate_progress_trends(student_id)
    
    def generate_iep_data(self, student_id, timeframe):
        """
        Creates progress reports for IEP meetings
        Provides evidence-based recommendations
        """
        student_data = self.filter_by_student(student_id, timeframe)
        
        prompt = f"""
        Analyze this special education progress data:
        {student_data}
        
        Provide:
        1. Goal achievement summary
        2. Areas of significant growth
        3. Challenges identified
        4. Recommended IEP adjustments
        5. Technology effectiveness rating
        
        Use objective, professional language.
        """
        
        response = ollama.generate(model='mistral:7b', prompt=prompt)
        return format_iep_report(response['response'])

# Optimize model loading
ollama pull llama2:7b-q4_0  # Quantized version for speed
export OLLAMA_NUM_PARALLEL=2  # Limit concurrent requests
export OLLAMA_MAX_LOADED_MODELS=1  # Reduce memory usage

def add_educational_context(prompt):
    """
    Adds classroom context to all prompts
    Ensures appropriate response tone and content
    """
    educational_prefix = """
    You are an educational assistant for K-12 special education.
    Use simple language, positive tone, and encouraging responses.
    Focus on learning objectives and student success.
    
    Student question: """
    
    return educational_prefix + prompt