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from typing import Dict, List, Set, Tuple, Optional
from collections import defaultdict, deque
class EnhancedKnowledgeGraph:
"""Enhanced Knowledge Graph with traversal capabilities"""
def __init__(self):
# Node properties
self.nodes = {
# Tones
"fun": {
"type": "tone",
"properties": {
"formality": 0.2,
"energy": 0.9,
"creativity": 0.8
}
},
"professional": {
"type": "tone",
"properties": {
"formality": 0.9,
"energy": 0.5,
"creativity": 0.3
}
},
"semi-fun": {
"type": "tone",
"properties": {
"formality": 0.5,
"energy": 0.7,
"creativity": 0.6
}
},
# Platforms
"Meta": {
"type": "platform",
"properties": {
"char_limit": 2200,
"emoji_friendly": True,
"hashtag_friendly": True,
"visual_emphasis": 0.9
}
},
"Google": {
"type": "platform",
"properties": {
"char_limit": 90,
"emoji_friendly": False,
"hashtag_friendly": False,
"visual_emphasis": 0.2
}
},
"LinkedIn": {
"type": "platform",
"properties": {
"char_limit": 3000,
"emoji_friendly": False,
"hashtag_friendly": True,
"visual_emphasis": 0.4
}
},
# Creative Types
"awareness": {
"type": "creative_type",
"properties": {
"goal": "brand_visibility",
"cta_strength": 0.3
}
},
"engagement": {
"type": "creative_type",
"properties": {
"goal": "interaction",
"cta_strength": 0.7
}
},
"conversion": {
"type": "creative_type",
"properties": {
"goal": "sales",
"cta_strength": 1.0
}
}
}
# Edges (relationships)
self.edges = defaultdict(list)
self._build_relationships()
def _build_relationships(self):
"""Build graph relationships"""
# Tone -> Platform compatibility
self.add_edge("fun", "Meta", "highly_compatible", weight=0.9)
self.add_edge("fun", "LinkedIn", "moderately_compatible", weight=0.3)
self.add_edge("fun", "Google", "poorly_compatible", weight=0.1)
self.add_edge("professional", "LinkedIn", "highly_compatible", weight=0.95)
self.add_edge("professional", "Google", "highly_compatible", weight=0.9)
self.add_edge("professional", "Meta", "moderately_compatible", weight=0.5)
self.add_edge("semi-fun", "Meta", "highly_compatible", weight=0.8)
self.add_edge("semi-fun", "LinkedIn", "highly_compatible", weight=0.7)
self.add_edge("semi-fun", "Google", "moderately_compatible", weight=0.5)
# Tone -> Creative Type
self.add_edge("fun", "awareness", "suitable_for", weight=0.9)
self.add_edge("fun", "engagement", "suitable_for", weight=0.95)
self.add_edge("professional", "conversion", "suitable_for", weight=0.9)
self.add_edge("semi-fun", "engagement", "suitable_for", weight=0.8)
# Platform -> Creative Type preferences
self.add_edge("Meta", "engagement", "prefers", weight=0.9)
self.add_edge("LinkedIn", "conversion", "prefers", weight=0.8)
self.add_edge("Google", "conversion", "prefers", weight=0.95)
def add_edge(self, from_node: str, to_node: str, relationship: str, weight: float = 1.0):
"""Add an edge to the graph"""
self.edges[from_node].append({
"to": to_node,
"relationship": relationship,
"weight": weight
})
def traverse_bfs(self, start_node: str, max_depth: int = 2) -> Dict[str, List[Tuple[str, str, float]]]:
"""Breadth-first traversal to find related nodes"""
visited = set()
queue = deque([(start_node, 0)])
paths = defaultdict(list)
while queue:
current_node, depth = queue.popleft()
if current_node in visited or depth > max_depth:
continue
visited.add(current_node)
for edge in self.edges.get(current_node, []):
to_node = edge["to"]
relationship = edge["relationship"]
weight = edge["weight"]
paths[to_node].append((current_node, relationship, weight))
if depth < max_depth:
queue.append((to_node, depth + 1))
return dict(paths)
def find_best_path(self, start: str, end: str) -> Optional[List[Tuple[str, str, float]]]:
"""Find the best path between two nodes using weighted edges"""
# Simple Dijkstra-like approach
distances = {node: float('inf') for node in self.nodes}
distances[start] = 0
previous = {}
unvisited = set(self.nodes.keys())
while unvisited:
current = min(unvisited, key=lambda x: distances[x])
if distances[current] == float('inf'):
break
unvisited.remove(current)
for edge in self.edges.get(current, []):
neighbor = edge["to"]
weight = 1 - edge["weight"] # Convert to distance (lower is better)
distance = distances[current] + weight
if distance < distances[neighbor]:
distances[neighbor] = distance
previous[neighbor] = (current, edge["relationship"], edge["weight"])
# Reconstruct path
if end not in previous:
return None
path = []
current = end
while current != start:
if current not in previous:
return None
prev_node, rel, weight = previous[current]
path.append((prev_node, rel, weight))
current = prev_node
return list(reversed(path))
def get_recommendations(self, tone: str, platform: str) -> Dict[str, any]:
"""Get recommendations based on tone and platform"""
recommendations = {
"compatibility_score": 0,
"suggested_elements": [],
"warnings": [],
"creative_types": []
}
# Check direct compatibility
for edge in self.edges.get(tone, []):
if edge["to"] == platform:
recommendations["compatibility_score"] = edge["weight"]
break
# Find related creative types
tone_paths = self.traverse_bfs(tone, max_depth=1)
platform_paths = self.traverse_bfs(platform, max_depth=1)
# Extract creative type recommendations
for node, paths in tone_paths.items():
if self.nodes.get(node, {}).get("type") == "creative_type":
for _, rel, weight in paths:
if rel == "suitable_for" and weight > 0.7:
recommendations["creative_types"].append(node)
# Platform-specific suggestions
platform_props = self.nodes.get(platform, {}).get("properties", {})
tone_props = self.nodes.get(tone, {}).get("properties", {})
if platform_props.get("emoji_friendly") and tone_props.get("creativity", 0) > 0.7:
recommendations["suggested_elements"].append("Use emojis to enhance engagement")
elif not platform_props.get("emoji_friendly") and tone == "fun":
recommendations["warnings"].append("Platform doesn't support emojis well - adjust tone")
if platform_props.get("char_limit", float('inf')) < 100:
recommendations["suggested_elements"].append("Keep message extremely concise")
return recommendations
def explain_relationship(self, node1: str, node2: str) -> str:
"""Explain the relationship between two nodes"""
# Check direct connection first
for edge in self.edges.get(node1, []):
if edge["to"] == node2:
return f"{node1} is {edge['relationship']} with {node2} (strength: {edge['weight']:.2f})"
# If no direct connection, find path
path = self.find_best_path(node1, node2)
if not path:
return f"No direct relationship found between {node1} and {node2}"
explanation = []
current = node1
for prev_node, relationship, weight in path:
# The path reconstruction gives us the path backwards, so we need to handle it correctly
explanation.append(f"{prev_node} {relationship} {current} (strength: {weight:.2f})")
current = prev_node
return " → ".join(explanation) |