ALwrity AI Blog Writer - Added Google Grounding UI Implementation
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ajaysi
271
backend/test/test_linkedin_keyword_fix.py
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271
backend/test/test_linkedin_keyword_fix.py
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#!/usr/bin/env python3
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"""
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Test Script for LinkedIn Content Generation Keyword Fix
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This script tests the fixed keyword processing by calling the LinkedIn content generation
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endpoint directly and capturing detailed logs to analyze API usage patterns.
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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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import logging
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from datetime import datetime
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from typing import Dict, Any
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import sys
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import os
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# Add the backend directory to the Python path
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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# Configure detailed logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler(f'test_linkedin_keyword_fix_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log'),
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logging.StreamHandler(sys.stdout)
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]
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)
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logger = logging.getLogger(__name__)
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# Import the LinkedIn service
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from services.linkedin_service import LinkedInService
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from models.linkedin_models import LinkedInPostRequest, LinkedInPostType, LinkedInTone, GroundingLevel, SearchEngine
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class LinkedInKeywordTest:
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"""Test class for LinkedIn keyword processing fix."""
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def __init__(self):
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self.linkedin_service = LinkedInService()
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self.test_results = []
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self.api_call_count = 0
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self.start_time = None
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def log_api_call(self, endpoint: str, duration: float, success: bool):
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"""Log API call details."""
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self.api_call_count += 1
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logger.info(f"API Call #{self.api_call_count}: {endpoint} - Duration: {duration:.2f}s - Success: {success}")
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async def test_keyword_phrase(self, phrase: str, test_name: str) -> Dict[str, Any]:
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"""Test a specific keyword phrase."""
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logger.info(f"\n{'='*60}")
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logger.info(f"TESTING: {test_name}")
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logger.info(f"KEYWORD PHRASE: '{phrase}'")
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logger.info(f"{'='*60}")
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test_start = time.time()
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try:
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# Create the request
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request = LinkedInPostRequest(
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topic=phrase,
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industry="Technology",
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post_type=LinkedInPostType.PROFESSIONAL,
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tone=LinkedInTone.PROFESSIONAL,
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grounding_level=GroundingLevel.ENHANCED,
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search_engine=SearchEngine.GOOGLE,
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research_enabled=True,
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include_citations=True,
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max_length=1000
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)
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logger.info(f"Request created: {request.topic}")
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logger.info(f"Research enabled: {request.research_enabled}")
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logger.info(f"Search engine: {request.search_engine}")
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logger.info(f"Grounding level: {request.grounding_level}")
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# Call the LinkedIn service
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logger.info("Calling LinkedIn service...")
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response = await self.linkedin_service.generate_linkedin_post(request)
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test_duration = time.time() - test_start
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self.log_api_call("LinkedIn Post Generation", test_duration, response.success)
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# Analyze the response
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result = {
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"test_name": test_name,
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"keyword_phrase": phrase,
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"success": response.success,
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"duration": test_duration,
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"api_calls": self.api_call_count,
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"error": response.error if not response.success else None,
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"content_length": len(response.data.content) if response.success and response.data else 0,
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"sources_count": len(response.research_sources) if response.success and response.research_sources else 0,
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"citations_count": len(response.data.citations) if response.success and response.data and response.data.citations else 0,
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"grounding_status": response.grounding_status if response.success else None,
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"generation_metadata": response.generation_metadata if response.success else None
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}
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if response.success:
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logger.info(f"✅ SUCCESS: Generated {result['content_length']} characters")
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logger.info(f"📊 Sources: {result['sources_count']}, Citations: {result['citations_count']}")
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logger.info(f"⏱️ Total duration: {test_duration:.2f}s")
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logger.info(f"🔢 API calls made: {self.api_call_count}")
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# Log content preview
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if response.data and response.data.content:
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content_preview = response.data.content[:200] + "..." if len(response.data.content) > 200 else response.data.content
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logger.info(f"📝 Content preview: {content_preview}")
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# Log grounding status
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if response.grounding_status:
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logger.info(f"🔍 Grounding status: {response.grounding_status}")
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else:
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logger.error(f"❌ FAILED: {response.error}")
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return result
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except Exception as e:
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test_duration = time.time() - test_start
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logger.error(f"❌ EXCEPTION in {test_name}: {str(e)}")
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self.log_api_call("LinkedIn Post Generation", test_duration, False)
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return {
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"test_name": test_name,
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"keyword_phrase": phrase,
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"success": False,
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"duration": test_duration,
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"api_calls": self.api_call_count,
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"error": str(e),
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"content_length": 0,
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"sources_count": 0,
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"citations_count": 0,
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"grounding_status": None,
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"generation_metadata": None
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}
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async def run_comprehensive_test(self):
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"""Run comprehensive tests for keyword processing."""
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logger.info("🚀 Starting LinkedIn Keyword Processing Test Suite")
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logger.info(f"Test started at: {datetime.now()}")
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self.start_time = time.time()
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# Test cases
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test_cases = [
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{
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"phrase": "ALwrity content generation",
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"name": "Single Phrase Test (Should be preserved as-is)"
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},
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{
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"phrase": "AI tools, content creation, marketing automation",
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"name": "Comma-Separated Test (Should be split by commas)"
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},
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{
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"phrase": "LinkedIn content strategy",
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"name": "Another Single Phrase Test"
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},
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{
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"phrase": "social media, digital marketing, brand awareness",
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"name": "Another Comma-Separated Test"
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}
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]
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# Run all tests
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for test_case in test_cases:
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result = await self.test_keyword_phrase(
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test_case["phrase"],
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test_case["name"]
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)
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self.test_results.append(result)
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# Reset API call counter for next test
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self.api_call_count = 0
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# Small delay between tests
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await asyncio.sleep(2)
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# Generate summary report
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self.generate_summary_report()
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def generate_summary_report(self):
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"""Generate a comprehensive summary report."""
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total_time = time.time() - self.start_time
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logger.info(f"\n{'='*80}")
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logger.info("📊 COMPREHENSIVE TEST SUMMARY REPORT")
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logger.info(f"{'='*80}")
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logger.info(f"🕐 Total test duration: {total_time:.2f} seconds")
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logger.info(f"🧪 Total tests run: {len(self.test_results)}")
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successful_tests = [r for r in self.test_results if r["success"]]
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failed_tests = [r for r in self.test_results if not r["success"]]
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logger.info(f"✅ Successful tests: {len(successful_tests)}")
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logger.info(f"❌ Failed tests: {len(failed_tests)}")
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if successful_tests:
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avg_duration = sum(r["duration"] for r in successful_tests) / len(successful_tests)
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avg_content_length = sum(r["content_length"] for r in successful_tests) / len(successful_tests)
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avg_sources = sum(r["sources_count"] for r in successful_tests) / len(successful_tests)
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avg_citations = sum(r["citations_count"] for r in successful_tests) / len(successful_tests)
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logger.info(f"📈 Average generation time: {avg_duration:.2f}s")
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logger.info(f"📝 Average content length: {avg_content_length:.0f} characters")
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logger.info(f"🔍 Average sources found: {avg_sources:.1f}")
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logger.info(f"📚 Average citations: {avg_citations:.1f}")
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# Detailed results
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logger.info(f"\n📋 DETAILED TEST RESULTS:")
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for i, result in enumerate(self.test_results, 1):
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status = "✅ PASS" if result["success"] else "❌ FAIL"
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logger.info(f"{i}. {status} - {result['test_name']}")
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logger.info(f" Phrase: '{result['keyword_phrase']}'")
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logger.info(f" Duration: {result['duration']:.2f}s")
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if result["success"]:
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logger.info(f" Content: {result['content_length']} chars, Sources: {result['sources_count']}, Citations: {result['citations_count']}")
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else:
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logger.info(f" Error: {result['error']}")
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# API Usage Analysis
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logger.info(f"\n🔍 API USAGE ANALYSIS:")
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total_api_calls = sum(r["api_calls"] for r in self.test_results)
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logger.info(f"Total API calls across all tests: {total_api_calls}")
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if successful_tests:
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avg_api_calls = sum(r["api_calls"] for r in successful_tests) / len(successful_tests)
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logger.info(f"Average API calls per successful test: {avg_api_calls:.1f}")
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# Save detailed results to JSON file
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report_data = {
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"test_summary": {
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"total_duration": total_time,
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"total_tests": len(self.test_results),
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"successful_tests": len(successful_tests),
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"failed_tests": len(failed_tests),
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"total_api_calls": total_api_calls
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},
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"test_results": self.test_results,
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"timestamp": datetime.now().isoformat()
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}
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report_filename = f"linkedin_keyword_test_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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with open(report_filename, 'w') as f:
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json.dump(report_data, f, indent=2, default=str)
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logger.info(f"📄 Detailed report saved to: {report_filename}")
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logger.info(f"{'='*80}")
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async def main():
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"""Main test execution function."""
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try:
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test_suite = LinkedInKeywordTest()
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await test_suite.run_comprehensive_test()
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except Exception as e:
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logger.error(f"❌ Test suite failed: {str(e)}")
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raise
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if __name__ == "__main__":
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print("🚀 Starting LinkedIn Keyword Processing Test Suite")
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print("This will test the keyword fix and analyze API usage patterns...")
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print("=" * 60)
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asyncio.run(main())
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