641 lines
25 KiB
Python
641 lines
25 KiB
Python
"""
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Content Strategy Analysis Test
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Comprehensive analysis of content strategy data flow, AI prompts, and generated data points.
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"""
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import asyncio
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import json
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import time
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from typing import Dict, Any, List
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from datetime import datetime
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from loguru import logger
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# Import test utilities - using absolute import
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try:
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from test_data import TestData
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except ImportError:
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# Fallback for when running as standalone script
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class TestData:
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def __init__(self):
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pass
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class ContentStrategyAnalysis:
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"""Comprehensive analysis of content strategy functionality."""
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def __init__(self):
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self.test_data = TestData()
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self.analysis_results = {}
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async def analyze_content_strategy_flow(self) -> Dict[str, Any]:
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"""Analyze the complete content strategy data flow."""
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logger.info("🔍 Starting Content Strategy Analysis")
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analysis = {
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"timestamp": datetime.utcnow().isoformat(),
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"phase": "content_strategy",
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"analysis": {}
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}
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# 1. Input Analysis
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analysis["analysis"]["inputs"] = await self._analyze_inputs()
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# 2. AI Prompt Analysis
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analysis["analysis"]["ai_prompts"] = await self._analyze_ai_prompts()
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# 3. Data Points Analysis
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analysis["analysis"]["data_points"] = await self._analyze_data_points()
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# 4. Frontend Mapping Analysis
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analysis["analysis"]["frontend_mapping"] = await self._analyze_frontend_mapping()
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# 5. Test Results
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analysis["analysis"]["test_results"] = await self._run_comprehensive_tests()
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logger.info("✅ Content Strategy Analysis Completed")
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return analysis
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async def _analyze_inputs(self) -> Dict[str, Any]:
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"""Analyze the inputs required for content strategy generation."""
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logger.info("📊 Analyzing Content Strategy Inputs")
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inputs_analysis = {
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"required_inputs": {
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"user_id": {
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"type": "integer",
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"description": "User identifier for personalization",
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"required": True,
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"example": 1
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},
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"name": {
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"type": "string",
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"description": "Strategy name for identification",
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"required": True,
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"example": "Digital Marketing Strategy"
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},
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"industry": {
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"type": "string",
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"description": "Business industry for context",
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"required": True,
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"example": "technology"
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},
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"target_audience": {
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"type": "object",
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"description": "Target audience demographics and preferences",
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"required": True,
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"example": {
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"demographics": ["professionals", "business_owners"],
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"interests": ["digital_marketing", "content_creation"],
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"age_range": "25-45",
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"location": "global"
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}
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},
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"content_pillars": {
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"type": "array",
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"description": "Content pillars and themes",
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"required": False,
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"example": [
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{
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"name": "Educational Content",
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"description": "How-to guides and tutorials",
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"content_types": ["blog", "video", "webinar"]
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}
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]
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}
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},
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"optional_inputs": {
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"ai_recommendations": {
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"type": "object",
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"description": "AI-generated recommendations",
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"required": False
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},
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"strategy_id": {
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"type": "integer",
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"description": "Existing strategy ID for updates",
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"required": False
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}
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},
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"data_sources": [
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"User onboarding data",
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"Industry benchmarks",
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"Competitor analysis",
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"Historical performance data",
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"Market trends"
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]
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}
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logger.info(f"📋 Input Analysis: {len(inputs_analysis['required_inputs'])} required inputs identified")
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return inputs_analysis
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async def _analyze_ai_prompts(self) -> Dict[str, Any]:
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"""Analyze the AI prompts used in content strategy generation."""
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logger.info("🤖 Analyzing AI Prompts for Content Strategy")
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prompts_analysis = {
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"strategic_intelligence_prompt": {
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"purpose": "Generate strategic intelligence for content planning",
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"components": [
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"Strategy data analysis",
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"Market positioning assessment",
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"Competitive advantage identification",
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"Strategic score calculation",
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"Risk assessment",
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"Opportunity analysis"
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],
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"input_data": [
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"strategy_id",
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"market_data (optional)",
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"historical performance",
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"competitor analysis",
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"industry trends"
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],
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"output_structure": {
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"strategy_id": "integer",
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"market_positioning": "object",
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"competitive_advantages": "array",
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"strategic_scores": "object",
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"risk_assessment": "array",
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"opportunity_analysis": "array",
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"analysis_date": "datetime"
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}
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},
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"performance_trends_prompt": {
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"purpose": "Analyze performance trends for content strategy",
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"components": [
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"Metric trend analysis",
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"Predictive insights generation",
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"Performance score calculation",
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"Recommendation generation"
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],
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"metrics_analyzed": [
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"engagement_rate",
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"reach",
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"conversion_rate",
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"click_through_rate"
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]
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},
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"content_evolution_prompt": {
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"purpose": "Analyze content evolution over time",
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"components": [
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"Content type evolution analysis",
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"Engagement pattern analysis",
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"Performance trend analysis",
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"Evolution recommendation generation"
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]
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}
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}
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logger.info(f"🤖 AI Prompt Analysis: {len(prompts_analysis)} prompt types identified")
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return prompts_analysis
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async def _analyze_data_points(self) -> Dict[str, Any]:
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"""Analyze the data points generated by content strategy."""
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logger.info("📊 Analyzing Generated Data Points")
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data_points_analysis = {
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"strategic_insights": {
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"description": "AI-generated strategic insights for content planning",
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"structure": [
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{
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"id": "string",
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"type": "string",
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"title": "string",
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"description": "string",
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"priority": "string",
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"estimated_impact": "string",
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"created_at": "datetime"
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}
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],
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"example": {
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"id": "market_position_1",
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"type": "warning",
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"title": "Market Positioning Needs Improvement",
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"description": "Your market positioning score is 4/10. Consider strategic adjustments.",
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"priority": "high",
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"estimated_impact": "significant",
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"created_at": "2024-08-01T10:00:00Z"
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}
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},
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"market_positioning": {
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"description": "Market positioning analysis and scores",
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"structure": {
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"industry_position": "string",
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"competitive_advantage": "string",
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"market_share": "string",
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"positioning_score": "integer"
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},
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"example": {
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"industry_position": "emerging",
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"competitive_advantage": "AI-powered content",
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"market_share": "2.5%",
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"positioning_score": 4
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}
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},
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"strategic_scores": {
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"description": "Strategic performance scores",
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"structure": {
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"overall_score": "float",
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"content_quality_score": "float",
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"engagement_score": "float",
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"conversion_score": "float",
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"innovation_score": "float"
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},
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"example": {
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"overall_score": 7.2,
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"content_quality_score": 8.1,
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"engagement_score": 6.8,
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"conversion_score": 7.5,
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"innovation_score": 8.3
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}
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},
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"risk_assessment": {
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"description": "Strategic risk assessment",
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"structure": [
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{
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"type": "string",
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"severity": "string",
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"description": "string",
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"mitigation_strategy": "string"
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}
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],
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"example": [
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{
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"type": "market_competition",
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"severity": "medium",
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"description": "Increasing competition in AI content space",
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"mitigation_strategy": "Focus on unique value propositions"
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}
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]
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},
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"opportunity_analysis": {
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"description": "Strategic opportunity analysis",
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"structure": [
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{
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"title": "string",
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"description": "string",
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"estimated_impact": "string",
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"implementation_difficulty": "string",
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"timeline": "string"
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}
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],
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"example": [
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{
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"title": "Video Content Expansion",
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"description": "Expand into video content to capture growing demand",
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"estimated_impact": "high",
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"implementation_difficulty": "medium",
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"timeline": "3-6 months"
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}
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]
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},
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"recommendations": {
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"description": "AI-generated strategic recommendations",
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"structure": [
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{
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"id": "string",
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"type": "string",
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"title": "string",
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"description": "string",
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"priority": "string",
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"estimated_impact": "string",
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"action_items": "array"
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}
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],
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"example": [
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{
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"id": "rec_001",
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"type": "content_strategy",
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"title": "Implement AI-Powered Content Personalization",
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"description": "Use AI to personalize content for different audience segments",
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"priority": "high",
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"estimated_impact": "significant",
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"action_items": [
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"Implement AI content recommendation engine",
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"Create audience segmentation strategy",
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"Develop personalized content templates"
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]
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}
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]
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}
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}
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logger.info(f"📊 Data Points Analysis: {len(data_points_analysis)} data point types identified")
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return data_points_analysis
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async def _analyze_frontend_mapping(self) -> Dict[str, Any]:
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"""Analyze how backend data maps to frontend components."""
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logger.info("🖥️ Analyzing Frontend-Backend Data Mapping")
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frontend_mapping = {
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"dashboard_components": {
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"strategy_overview": {
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"backend_data": "strategic_scores",
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"frontend_component": "StrategyOverviewCard",
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"data_mapping": {
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"overall_score": "score",
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"content_quality_score": "qualityScore",
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"engagement_score": "engagementScore",
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"conversion_score": "conversionScore"
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}
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},
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"strategic_insights": {
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"backend_data": "strategic_insights",
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"frontend_component": "InsightsList",
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"data_mapping": {
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"title": "title",
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"description": "description",
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"priority": "priority",
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"type": "type"
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}
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},
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"market_positioning": {
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"backend_data": "market_positioning",
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"frontend_component": "MarketPositioningChart",
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"data_mapping": {
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"positioning_score": "score",
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"industry_position": "position",
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"competitive_advantage": "advantage"
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}
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},
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"risk_assessment": {
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"backend_data": "risk_assessment",
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"frontend_component": "RiskAssessmentPanel",
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"data_mapping": {
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"type": "riskType",
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"severity": "severity",
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"description": "description",
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"mitigation_strategy": "mitigation"
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}
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},
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"opportunities": {
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"backend_data": "opportunity_analysis",
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"frontend_component": "OpportunitiesList",
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"data_mapping": {
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"title": "title",
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"description": "description",
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"estimated_impact": "impact",
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"implementation_difficulty": "difficulty"
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}
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},
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"recommendations": {
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"backend_data": "recommendations",
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"frontend_component": "RecommendationsPanel",
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"data_mapping": {
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"title": "title",
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"description": "description",
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"priority": "priority",
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"action_items": "actions"
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}
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}
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},
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"data_flow": {
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"api_endpoints": {
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"get_strategies": "/api/content-planning/strategies/",
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"get_strategy_by_id": "/api/content-planning/strategies/{id}",
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"create_strategy": "/api/content-planning/strategies/",
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"update_strategy": "/api/content-planning/strategies/{id}",
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"delete_strategy": "/api/content-planning/strategies/{id}"
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},
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"response_structure": {
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"status": "success/error",
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"data": "strategy_data",
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"message": "user_message",
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"timestamp": "iso_datetime"
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}
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}
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}
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logger.info(f"🖥️ Frontend Mapping Analysis: {len(frontend_mapping['dashboard_components'])} components mapped")
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return frontend_mapping
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async def _run_comprehensive_tests(self) -> Dict[str, Any]:
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"""Run comprehensive tests for content strategy functionality."""
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logger.info("🧪 Running Comprehensive Content Strategy Tests")
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test_results = {
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"test_cases": [],
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"summary": {
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"total_tests": 0,
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"passed": 0,
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"failed": 0,
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"success_rate": 0.0
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}
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}
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# Test Case 1: Strategy Creation
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test_case_1 = await self._test_strategy_creation()
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test_results["test_cases"].append(test_case_1)
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# Test Case 2: Strategy Retrieval
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test_case_2 = await self._test_strategy_retrieval()
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test_results["test_cases"].append(test_case_2)
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# Test Case 3: Strategic Intelligence Generation
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test_case_3 = await self._test_strategic_intelligence()
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test_results["test_cases"].append(test_case_3)
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# Test Case 4: Data Structure Validation
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test_case_4 = await self._test_data_structure_validation()
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test_results["test_cases"].append(test_case_4)
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# Calculate summary
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total_tests = len(test_results["test_cases"])
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passed_tests = sum(1 for test in test_results["test_cases"] if test["status"] == "passed")
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test_results["summary"] = {
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"total_tests": total_tests,
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"passed": passed_tests,
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"failed": total_tests - passed_tests,
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"success_rate": (passed_tests / total_tests * 100) if total_tests > 0 else 0.0
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}
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logger.info(f"🧪 Test Results: {passed_tests}/{total_tests} tests passed ({test_results['summary']['success_rate']:.1f}%)")
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return test_results
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async def _test_strategy_creation(self) -> Dict[str, Any]:
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"""Test strategy creation functionality."""
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try:
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logger.info("Testing strategy creation...")
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# Simulate strategy creation
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strategy_data = {
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"user_id": 1,
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"name": "Test Digital Marketing Strategy",
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"industry": "technology",
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"target_audience": {
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"demographics": ["professionals"],
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"interests": ["digital_marketing"]
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},
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"content_pillars": [
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{
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"name": "Educational Content",
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"description": "How-to guides and tutorials"
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}
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]
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}
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# Validate required fields
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required_fields = ["user_id", "name", "industry", "target_audience"]
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missing_fields = [field for field in required_fields if field not in strategy_data]
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if missing_fields:
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return {
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"name": "Strategy Creation - Required Fields",
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"status": "failed",
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"error": f"Missing required fields: {missing_fields}"
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}
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return {
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"name": "Strategy Creation - Required Fields",
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"status": "passed",
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"message": "All required fields present"
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}
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except Exception as e:
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return {
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"name": "Strategy Creation",
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"status": "failed",
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"error": str(e)
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}
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async def _test_strategy_retrieval(self) -> Dict[str, Any]:
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"""Test strategy retrieval functionality."""
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try:
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logger.info("Testing strategy retrieval...")
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# Simulate strategy retrieval
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user_id = 1
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strategy_id = 1
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# Validate query parameters
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if not isinstance(user_id, int) or user_id <= 0:
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return {
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"name": "Strategy Retrieval - User ID Validation",
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"status": "failed",
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"error": "Invalid user_id"
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}
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return {
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"name": "Strategy Retrieval - User ID Validation",
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"status": "passed",
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"message": "User ID validation passed"
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}
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except Exception as e:
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return {
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"name": "Strategy Retrieval",
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"status": "failed",
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"error": str(e)
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}
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async def _test_strategic_intelligence(self) -> Dict[str, Any]:
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"""Test strategic intelligence generation."""
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try:
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logger.info("Testing strategic intelligence generation...")
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# Expected strategic intelligence structure
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expected_structure = {
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"strategy_id": "integer",
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"market_positioning": "object",
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"competitive_advantages": "array",
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"strategic_scores": "object",
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"risk_assessment": "array",
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"opportunity_analysis": "array"
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}
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# Validate structure
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required_keys = list(expected_structure.keys())
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return {
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"name": "Strategic Intelligence - Structure Validation",
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"status": "passed",
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"message": f"Expected structure contains {len(required_keys)} required keys"
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}
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except Exception as e:
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return {
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"name": "Strategic Intelligence",
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"status": "failed",
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"error": str(e)
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}
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async def _test_data_structure_validation(self) -> Dict[str, Any]:
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"""Test data structure validation."""
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try:
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logger.info("Testing data structure validation...")
|
|
|
|
# Test strategic insights structure
|
|
strategic_insight_structure = {
|
|
"id": "string",
|
|
"type": "string",
|
|
"title": "string",
|
|
"description": "string",
|
|
"priority": "string",
|
|
"created_at": "datetime"
|
|
}
|
|
|
|
# Test market positioning structure
|
|
market_positioning_structure = {
|
|
"industry_position": "string",
|
|
"competitive_advantage": "string",
|
|
"positioning_score": "integer"
|
|
}
|
|
|
|
# Validate both structures
|
|
insight_keys = list(strategic_insight_structure.keys())
|
|
positioning_keys = list(market_positioning_structure.keys())
|
|
|
|
if len(insight_keys) >= 5 and len(positioning_keys) >= 3:
|
|
return {
|
|
"name": "Data Structure Validation",
|
|
"status": "passed",
|
|
"message": "Data structures properly defined"
|
|
}
|
|
else:
|
|
return {
|
|
"name": "Data Structure Validation",
|
|
"status": "failed",
|
|
"error": "Insufficient data structure definition"
|
|
}
|
|
|
|
except Exception as e:
|
|
return {
|
|
"name": "Data Structure Validation",
|
|
"status": "failed",
|
|
"error": str(e)
|
|
}
|
|
|
|
async def main():
|
|
"""Main function to run content strategy analysis."""
|
|
logger.info("🚀 Starting Content Strategy Analysis")
|
|
|
|
analyzer = ContentStrategyAnalysis()
|
|
results = await analyzer.analyze_content_strategy_flow()
|
|
|
|
# Save results to file
|
|
with open("content_strategy_analysis_results.json", "w") as f:
|
|
json.dump(results, f, indent=2, default=str)
|
|
|
|
logger.info("✅ Content Strategy Analysis completed and saved to content_strategy_analysis_results.json")
|
|
|
|
# Print summary
|
|
print("\n" + "="*60)
|
|
print("📊 CONTENT STRATEGY ANALYSIS SUMMARY")
|
|
print("="*60)
|
|
|
|
test_results = results["analysis"]["test_results"]["summary"]
|
|
print(f"🧪 Test Results: {test_results['passed']}/{test_results['total_tests']} passed ({test_results['success_rate']:.1f}%)")
|
|
|
|
inputs_count = len(results["analysis"]["inputs"]["required_inputs"])
|
|
data_points_count = len(results["analysis"]["data_points"])
|
|
components_count = len(results["analysis"]["frontend_mapping"]["dashboard_components"])
|
|
|
|
print(f"📋 Inputs Analyzed: {inputs_count} required inputs")
|
|
print(f"📊 Data Points: {data_points_count} data point types")
|
|
print(f"🖥️ Frontend Components: {components_count} components mapped")
|
|
|
|
print("\n" + "="*60)
|
|
print("✅ Content Strategy Phase Analysis Complete!")
|
|
print("="*60)
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main()) |