class AICodeAnalyzer:
"""AI Agent for superior code evaluation utilizing Griffe"""
def __init__(self):
self.analysis_cache = {}
self.dependency_graph = nx.DiGraph()
def analyze_package(self, package_name: str, search_paths: Checklist[str] = None) -> Dict[str, Any]:
"""Complete bundle evaluation for AI choice making"""
strive:
pkg = griffe.load(package_name, search_paths=search_paths, try_relative_path=False)
evaluation = {
'package_name': package_name,
'total_modules': 0,
'total_classes': 0,
'total_functions': 0,
'complexity_score': 0,
'api_surface': [],
'inheritance_tree': {},
'risk_factors': []
}
self._analyze_object(pkg, evaluation)
evaluation['complexity_score'] = self._calculate_complexity(evaluation)
evaluation['risk_factors'] = self._identify_risks(pkg, evaluation)
self.analysis_cache[package_name] = evaluation
return evaluation
besides Exception as e:
return {'error': f"Failed to investigate {package_name}: {str(e)}"}
def analyze_simple_module(self, module_name: str) -> Dict[str, Any]:
"""Analyze a easy module with out deep dependency decision"""
strive:
import importlib
module = importlib.import_module(module_name)
evaluation = {
'package_name': module_name,
'total_modules': 1,
'total_classes': 0,
'total_functions': 0,
'complexity_score': 0,
'api_surface': [],
'inheritance_tree': {},
'risk_factors': []
}
for attr_name in dir(module):
if not attr_name.startswith('_'):
attr = getattr(module, attr_name)
if isinstance(attr, sort):
evaluation['total_classes'] += 1
evaluation['api_surface'].append({
'identify': f"{module_name}.{attr_name}",
'sort': 'class',
'public': True,
'docstring': bool(attr.__doc__),
'strategies': len([m for m in dir(attr) if not m.startswith('_')])
})
elif callable(attr):
evaluation['total_functions'] += 1
evaluation['api_surface'].append({
'identify': f"{module_name}.{attr_name}",
'sort': 'perform',
'public': True,
'docstring': bool(attr.__doc__),
'parameters': 0
})
evaluation['complexity_score'] = self._calculate_complexity(evaluation)
evaluation['risk_factors'] = self._identify_basic_risks(evaluation)
self.analysis_cache[module_name] = evaluation
return evaluation
besides Exception as e:
return {'error': f"Failed to investigate {module_name}: {str(e)}"}
def _analyze_object(self, obj, evaluation: Dict[str, Any], path: str = ""):
"""Recursively analyze bundle objects"""
strive:
current_path = f"{path}.{obj.identify}" if path else obj.identify
if hasattr(obj, 'form'):
if obj.form.worth == 'module':
evaluation['total_modules'] += 1
elif obj.form.worth == 'class':
evaluation['total_classes'] += 1
evaluation['api_surface'].append({
'identify': current_path,
'sort': 'class',
'public': not obj.identify.startswith('_'),
'docstring': bool(obj.docstring),
'strategies': len([m for m in obj.members.values() if hasattr(m, 'kind') and m.kind.value == 'function'])
})
if hasattr(obj, 'bases') and obj.bases:
evaluation['inheritance_tree'][current_path] = [str(base) for base in obj.bases]
elif obj.form.worth == 'perform':
evaluation['total_functions'] += 1
evaluation['api_surface'].append({
'identify': current_path,
'sort': 'perform',
'public': not obj.identify.startswith('_'),
'docstring': bool(obj.docstring),
'parameters': len(obj.parameters) if hasattr(obj, 'parameters') else 0
})
if hasattr(obj, 'members'):
for member in obj.members.values():
self._analyze_object(member, evaluation, current_path)
besides Exception as e:
move
def _calculate_complexity(self, evaluation: Dict[str, Any]) -> float:
"""Calculate bundle complexity rating for AI choice making"""
base_score = (evaluation['total_classes'] * 2 +
evaluation['total_functions'] * 1 +
evaluation['total_modules'] * 0.5)
inheritance_penalty = len(evaluation['inheritance_tree']) * 1.5
documented_items = sum(1 for merchandise in evaluation['api_surface'] if merchandise['docstring'])
total_items = len(evaluation['api_surface'])
doc_penalty = (total_items - documented_items) * 0.3 if total_items > 0 else 0
return base_score + inheritance_penalty + doc_penalty
def _identify_risks(self, pkg, evaluation: Dict[str, Any]) -> Checklist[str]:
"""Determine potential dangers for AI agent choice making"""
dangers = []
if len(evaluation['api_surface']) > 50:
dangers.append("Massive API floor - potential upkeep burden")
documented = sum(1 for merchandise in evaluation['api_surface'] if merchandise['docstring'])
whole = len(evaluation['api_surface'])
if whole > 0 and documented / whole 3:
dangers.append("Deep inheritance hierarchy detected")
return dangers
def _identify_basic_risks(self, evaluation: Dict[str, Any]) -> Checklist[str]:
"""Fundamental danger identification for easy module evaluation"""
dangers = []
if len(evaluation['api_surface']) > 30:
dangers.append("Massive API floor")
documented = sum(1 for merchandise in evaluation['api_surface'] if merchandise['docstring'])
whole = len(evaluation['api_surface'])
if whole > 0 and documented / whole Dict[str, Any]:
"""Examine two packages for AI choice making"""
analysis1 = self.analyze_package(pkg1)
if 'error' in analysis1:
analysis1 = self.analyze_simple_module(pkg1)
analysis2 = self.analyze_package(pkg2)
if 'error' in analysis2:
analysis2 = self.analyze_simple_module(pkg2)
if 'error' in analysis1 or 'error' in analysis2:
return {'error': 'Failed to match packages'}
comparability = {
'package_comparison': {
'complexity': {
pkg1: analysis1['complexity_score'],
pkg2: analysis2['complexity_score'],
'winner': pkg1 if analysis1['complexity_score'] self._doc_coverage(analysis2) else pkg2
}
},
'suggestion': self._make_recommendation(analysis1, analysis2, pkg1, pkg2)
}
return comparability
def _doc_coverage(self, evaluation: Dict[str, Any]) -> float:
"""Calculate documentation protection proportion"""
if not evaluation['api_surface']:
return 0.0
documented = sum(1 for merchandise in evaluation['api_surface'] if merchandise['docstring'])
return (documented / len(evaluation['api_surface'])) * 100
def _make_recommendation(self, a1: Dict, a2: Dict, pkg1: str, pkg2: str) -> str:
"""Make AI suggestion primarily based on evaluation"""
score1 = (100 - a1['complexity_score']) + self._doc_coverage(a1)
score2 = (100 - a2['complexity_score']) + self._doc_coverage(a2)
if score1 > score2:
return f"Advocate {pkg1}: Higher complexity/documentation stability"
elif score2 > score1:
return f"Advocate {pkg2}: Higher complexity/documentation stability"
else:
return "Packages are equal in key metrics"
def visualize_analysis(self, package_name: str):
"""Create visualizations for AI insights"""
if package_name not in self.analysis_cache:
evaluation = self.analyze_package(package_name)
if 'error' in evaluation:
evaluation = self.analyze_simple_module(package_name)
evaluation = self.analysis_cache[package_name]
if 'error' in evaluation:
print(f"❌ Can not visualize {package_name}: {evaluation['error']}")
return
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))
fig.suptitle(f'AI Evaluation Dashboard: {package_name}', fontsize=16, fontweight="daring")
parts = ['Modules', 'Classes', 'Functions']
counts = [analysis['total_modules'], evaluation['total_classes'], evaluation['total_functions']]
colours = ['#FF6B6B', '#4ECDC4', '#45B7D1']
non_zero_components = []
non_zero_counts = []
non_zero_colors = []
for i, depend in enumerate(counts):
if depend > 0:
non_zero_components.append(parts[i])
non_zero_counts.append(depend)
non_zero_colors.append(colours[i])
if non_zero_counts:
ax1.pie(non_zero_counts, labels=non_zero_components, colours=non_zero_colors,
autopct="%1.1f%%", startangle=90)
else:
ax1.textual content(0.5, 0.5, 'No parts discovered', ha="heart", va="heart", remodel=ax1.transAxes)
ax1.set_title('Element Distribution')
public_items = sum(1 for merchandise in evaluation['api_surface'] if merchandise['public'])
private_items = len(evaluation['api_surface']) - public_items
ax2.bar(['Public API', 'Private API'], [public_items, private_items],
shade=['#2ECC71', '#E74C3C'])
ax2.set_title('API Floor Evaluation')
ax2.set_ylabel('Depend')
documented = sum(1 for merchandise in evaluation['api_surface'] if merchandise['docstring'])
undocumented = len(evaluation['api_surface']) - documented
ax3.bar(['Documented', 'Undocumented'], [documented, undocumented],
shade=['#3498DB', '#F39C12'])
ax3.set_title('Documentation Protection')
ax3.set_ylabel('Depend')
complexity = evaluation['complexity_score']
ax4.barh(['Complexity Score'], [complexity], shade="#9B59B6")
ax4.set_title('Bundle Complexity')
ax4.set_xlabel('Rating')
plt.tight_layout()
plt.present()
print(f"n🤖 AI INSIGHTS for {package_name}:")
print(f"📊 Complexity Rating: {complexity:.2f}")
print(f"📈 Complete API Parts: {len(evaluation['api_surface'])}")
print(f"📚 Documentation Protection: {self._doc_coverage(evaluation):.1f}%")
if evaluation['risk_factors']:
print(f"⚠️ Threat Components:")
for danger in evaluation['risk_factors']:
print(f" • {danger}")
else:
print("✅ No main danger components detected")Elevate your perspective with NextTech Information, the place innovation meets perception.
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