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"""
Utility functions for multi-modal interactions including text processing,
voice recognition and handwriting recognition for the TutorX MCP server.
"""
from typing import Dict, Any, List, Optional
import base64
import json
from datetime import datetime
def process_text_query(query: str, context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""
Process a text query from the student
Args:
query: The text query from the student
context: Optional context about the student and current session
Returns:
Processed response
"""
# In a real implementation, this would use NLP to understand the query
# and generate an appropriate response
# Simple keyword-based response for demonstration
keywords = {
"solve": {
"type": "math_solution",
"response": "To solve this equation, first isolate the variable by..."
},
"what is": {
"type": "definition",
"response": "This concept refers to..."
},
"how do i": {
"type": "procedure",
"response": "Follow these steps: 1)..."
},
"help": {
"type": "assistance",
"response": "I'm here to help! You can ask me questions about..."
}
}
for key, value in keywords.items():
if key in query.lower():
return {
"query": query,
"response_type": value["type"],
"response": value["response"],
"confidence": 0.85,
"timestamp": datetime.now().isoformat()
}
# Default response if no keywords match
return {
"query": query,
"response_type": "general",
"response": "That's an interesting question. Let me think about how to help you with that.",
"confidence": 0.6,
"timestamp": datetime.now().isoformat()
}
def process_voice_input(audio_data_base64: str) -> Dict[str, Any]:
"""
Process voice input from the student
Args:
audio_data_base64: Base64 encoded audio data
Returns:
Transcription and analysis
"""
# In a real implementation, this would use ASR to transcribe the audio
# and then process the transcribed text
# For demonstration purposes, we'll simulate a transcription
return {
"transcription": "What is the quadratic formula?",
"confidence": 0.92,
"detected_emotions": {
"confusion": 0.7,
"interest": 0.9,
"frustration": 0.2
},
"audio_quality": "good",
"background_noise": "low",
"timestamp": datetime.now().isoformat()
}
def process_handwriting(image_data_base64: str) -> Dict[str, Any]:
"""
Process handwritten input from the student
Args:
image_data_base64: Base64 encoded image data of handwriting
Returns:
Transcription and analysis
"""
# In a real implementation, this would use OCR/handwriting recognition
# to transcribe the handwritten text or equations
# For demonstration purposes, we'll simulate a transcription
return {
"transcription": "x^2 + 5x + 6 = 0",
"confidence": 0.85,
"detected_content_type": "math_equation",
"equation_type": "quadratic",
"parsed_latex": "x^2 + 5x + 6 = 0",
"timestamp": datetime.now().isoformat()
}
def generate_speech_response(text: str, voice_params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""
Generate speech response from text
Args:
text: The text to convert to speech
voice_params: Parameters for the voice (gender, age, accent, etc.)
Returns:
Speech data and metadata
"""
# In a real implementation, this would use TTS to generate audio
# For demonstration, we'll simulate audio generation metadata
return {
"text": text,
"audio_format": "mp3",
"audio_data_base64": "SIMULATED_BASE64_AUDIO_DATA",
"voice_id": voice_params.get("voice_id", "default"),
"duration_seconds": len(text) / 15, # Rough estimate of speech duration
"sample_rate": 24000,
"timestamp": datetime.now().isoformat()
}
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