#!/usr/bin/env python3
"""
Content Template Matcher
This module provides intelligent content-to-template matching for optimal template selection.
Created to satisfy TDD test requirements, ready for Phase 2 enhancement.
"""
from typing import Dict, List, Any
import logging
class ContentTemplateMatcher:
"""Matches analyzed content to optimal templates with confidence scoring.
This is a basic implementation to satisfy TDD test requirements.
Ready for enhancement with advanced ML-style matching in Phase 2.
"""
def __init__(self):
"""Initialize the content template matcher."""
self.logger = logging.getLogger(__name__)
def analyze_content_description(self, content_description: str) -> Dict[str, Any]:
"""Analyze content description to determine type and requirements.
Args:
content_description: Text description of presentation content
Returns:
Dictionary with content analysis results
"""
# Enhanced implementation for TDD test compatibility
content_lower = content_description.lower()
# Enhanced content type detection
content_type = "general_presentation"
if any(word in content_lower for word in ["executive", "board", "quarterly", "strategic", "ceo"]):
content_type = "executive_presentation"
elif any(word in content_lower for word in ["training", "workshop", "tutorial", "instruction", "step-by-step"]):
content_type = "training"
elif any(word in content_lower for word in ["comparison", "versus", "vs", "compare", "features", "pricing"]):
content_type = "comparison"
elif any(word in content_lower for word in ["timeline", "schedule", "project", "milestone", "deliverable"]):
content_type = "timeline"
# Enhanced audience detection
audience = "general"
if any(word in content_lower for word in ["executive", "board", "ceo", "c-level"]):
audience = "executive"
elif any(word in content_lower for word in ["team", "internal", "staff", "responsibilities"]):
audience = "team"
elif any(
word in content_lower
for word in [
"client",
"customer",
"business",
"target",
"sales",
"regional",
"performance",
]
):
audience = "business"
# Enhanced formality detection
formality = "medium"
if any(word in content_lower for word in ["formal", "professional", "executive", "strategic", "quarterly"]):
formality = "high"
elif any(word in content_lower for word in ["casual", "informal", "friendly"]):
formality = "low"
# Enhanced data detection
data_heavy = any(
word in content_lower
for word in [
"data",
"metrics",
"analysis",
"financial",
"numbers",
"statistics",
"performance",
"pricing",
"features",
]
)
# Enhanced structure detection
structure = "general"
if any(word in content_lower for word in ["comparison", "versus", "vs"]):
structure = "comparative"
elif any(word in content_lower for word in ["summary", "overview", "executive"]):
structure = "summary_focused"
elif any(word in content_lower for word in ["step", "sequence", "process", "instruction"]):
structure = "sequential"
elif any(word in content_lower for word in ["timeline", "chronological", "milestone"]):
structure = "chronological"
return {
"content_type": content_type,
"audience": audience,
"formality": formality,
"data_heavy": data_heavy,
"structure": structure,
}
def match_content_to_templates(self, content_analysis: Dict[str, Any], available_templates: List[str]) -> List[Dict[str, Any]]:
"""Match analyzed content to optimal templates.
Args:
content_analysis: Results from analyze_content_description
available_templates: List of available template names
Returns:
List of template matches with confidence scores and reasoning
"""
# Basic implementation - ready for Phase 2 enhancement
matches = []
content_type = content_analysis.get("content_type", "general_presentation")
audience = content_analysis.get("audience", "general")
formality = content_analysis.get("formality", "medium")
data_heavy = content_analysis.get("data_heavy", False)
for template in available_templates:
confidence = 0.6 # Base confidence
reasoning = f"Template available for {content_type}"
# Template-specific scoring
if template == "default":
confidence = 0.7
reasoning = "Versatile template suitable for most presentation needs"
if content_type in ["general_presentation", "training"]:
confidence = 0.8
reasoning = "Good match for general content with standard layouts"
elif "business" in template.lower() or "pro" in template.lower():
confidence = 0.65
reasoning = "Professional template with advanced layouts"
if content_type == "executive_presentation":
confidence = 0.95
reasoning = "Executive audience requires professional template with data support"
elif audience == "executive":
confidence = 0.9
reasoning = "Executive-level template with professional layouts"
elif formality == "high":
confidence = 0.85
reasoning = "Professional template matching formal presentation style"
elif data_heavy:
confidence = 0.8
reasoning = "Advanced template with strong data visualization support"
# Calculate fit score breakdown
fit_score = {
"audience_match": (1.0 if audience == "executive" and "business" in template else 0.8),
"formality_match": 1.0 if formality == "high" and "pro" in template else 0.7,
"feature_match": 0.9 if data_heavy and "pro" in template else 0.6,
}
matches.append(
{
"template": template,
"confidence": confidence,
"reasoning": reasoning,
"fit_score": fit_score,
}
)
# Sort by confidence
matches.sort(key=lambda x: x["confidence"], reverse=True)
return matches
