Base code
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Kunthawat Greethong
341
backend/test/test_linkedin_endpoints.py
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341
backend/test/test_linkedin_endpoints.py
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"""
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Test script for LinkedIn content generation endpoints.
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This script tests the LinkedIn content generation functionality
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to ensure proper integration and validation.
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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
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import sys
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import os
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# Add the backend directory to Python path
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from models.linkedin_models import (
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LinkedInPostRequest, LinkedInArticleRequest, LinkedInCarouselRequest,
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LinkedInVideoScriptRequest, LinkedInCommentResponseRequest
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)
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from services.linkedin_service import linkedin_service
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from loguru import logger
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# Configure logger
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logger.remove()
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logger.add(sys.stdout, level="INFO", format="<level>{level}</level> | {message}")
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async def test_post_generation():
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"""Test LinkedIn post generation."""
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logger.info("🧪 Testing LinkedIn Post Generation")
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try:
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request = LinkedInPostRequest(
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topic="Artificial Intelligence in Healthcare",
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industry="Healthcare",
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post_type="thought_leadership",
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tone="professional",
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target_audience="Healthcare executives and AI professionals",
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key_points=["AI diagnostics", "Patient outcomes", "Cost reduction", "Implementation challenges"],
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include_hashtags=True,
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include_call_to_action=True,
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research_enabled=True,
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search_engine="metaphor",
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max_length=2000
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)
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start_time = time.time()
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response = await linkedin_service.generate_post(request)
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duration = time.time() - start_time
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logger.info(f"✅ Post generation completed in {duration:.2f} seconds")
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logger.info(f"Success: {response.success}")
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if response.success and response.data:
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logger.info(f"Content length: {response.data.character_count} characters")
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logger.info(f"Hashtags generated: {len(response.data.hashtags)}")
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logger.info(f"Call-to-action: {response.data.call_to_action is not None}")
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logger.info(f"Research sources: {len(response.research_sources)}")
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# Preview content (first 200 chars)
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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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else:
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logger.error(f"Post generation failed: {response.error}")
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return response.success
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except Exception as e:
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logger.error(f"❌ Error testing post generation: {str(e)}")
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return False
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async def test_article_generation():
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"""Test LinkedIn article generation."""
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logger.info("🧪 Testing LinkedIn Article Generation")
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try:
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request = LinkedInArticleRequest(
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topic="Digital Transformation in Manufacturing",
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industry="Manufacturing",
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tone="professional",
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target_audience="Manufacturing leaders and technology professionals",
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key_sections=["Current challenges", "Technology solutions", "Implementation strategies", "Future outlook"],
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include_images=True,
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seo_optimization=True,
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research_enabled=True,
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search_engine="metaphor",
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word_count=1500
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)
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start_time = time.time()
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response = await linkedin_service.generate_article(request)
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duration = time.time() - start_time
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logger.info(f"✅ Article generation completed in {duration:.2f} seconds")
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logger.info(f"Success: {response.success}")
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if response.success and response.data:
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logger.info(f"Word count: {response.data.word_count}")
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logger.info(f"Sections: {len(response.data.sections)}")
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logger.info(f"Reading time: {response.data.reading_time} minutes")
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logger.info(f"Image suggestions: {len(response.data.image_suggestions)}")
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logger.info(f"SEO metadata: {response.data.seo_metadata is not None}")
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logger.info(f"Research sources: {len(response.research_sources)}")
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# Preview title
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logger.info(f"Article title: {response.data.title}")
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else:
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logger.error(f"Article generation failed: {response.error}")
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return response.success
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except Exception as e:
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logger.error(f"❌ Error testing article generation: {str(e)}")
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return False
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async def test_carousel_generation():
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"""Test LinkedIn carousel generation."""
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logger.info("🧪 Testing LinkedIn Carousel Generation")
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try:
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request = LinkedInCarouselRequest(
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topic="5 Ways to Improve Team Productivity",
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industry="Business Management",
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slide_count=8,
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tone="professional",
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target_audience="Team leaders and managers",
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key_takeaways=["Clear communication", "Goal setting", "Tool optimization", "Regular feedback", "Work-life balance"],
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include_cover_slide=True,
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include_cta_slide=True,
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visual_style="modern"
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)
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start_time = time.time()
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response = await linkedin_service.generate_carousel(request)
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duration = time.time() - start_time
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logger.info(f"✅ Carousel generation completed in {duration:.2f} seconds")
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logger.info(f"Success: {response.success}")
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if response.success and response.data:
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logger.info(f"Slide count: {len(response.data.slides)}")
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logger.info(f"Carousel title: {response.data.title}")
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logger.info(f"Design guidelines: {bool(response.data.design_guidelines)}")
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# Preview first slide
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if response.data.slides:
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first_slide = response.data.slides[0]
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logger.info(f"First slide title: {first_slide.title}")
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else:
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logger.error(f"Carousel generation failed: {response.error}")
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return response.success
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except Exception as e:
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logger.error(f"❌ Error testing carousel generation: {str(e)}")
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return False
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async def test_video_script_generation():
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"""Test LinkedIn video script generation."""
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logger.info("🧪 Testing LinkedIn Video Script Generation")
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try:
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request = LinkedInVideoScriptRequest(
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topic="Quick tips for remote team management",
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industry="Human Resources",
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video_length=90,
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tone="conversational",
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target_audience="Remote team managers",
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key_messages=["Communication tools", "Regular check-ins", "Team building", "Performance tracking"],
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include_hook=True,
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include_captions=True
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)
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start_time = time.time()
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response = await linkedin_service.generate_video_script(request)
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duration = time.time() - start_time
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logger.info(f"✅ Video script generation completed in {duration:.2f} seconds")
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logger.info(f"Success: {response.success}")
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if response.success and response.data:
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logger.info(f"Hook: {bool(response.data.hook)}")
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logger.info(f"Main content scenes: {len(response.data.main_content)}")
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logger.info(f"Conclusion: {bool(response.data.conclusion)}")
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logger.info(f"Thumbnail suggestions: {len(response.data.thumbnail_suggestions)}")
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logger.info(f"Captions: {bool(response.data.captions)}")
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# Preview hook
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if response.data.hook:
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hook_preview = response.data.hook[:100] + "..." if len(response.data.hook) > 100 else response.data.hook
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logger.info(f"Hook preview: {hook_preview}")
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else:
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logger.error(f"Video script generation failed: {response.error}")
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return response.success
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except Exception as e:
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logger.error(f"❌ Error testing video script generation: {str(e)}")
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return False
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async def test_comment_response_generation():
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"""Test LinkedIn comment response generation."""
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logger.info("🧪 Testing LinkedIn Comment Response Generation")
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try:
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request = LinkedInCommentResponseRequest(
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original_post="Just published an article about AI transformation in healthcare. The potential for improving patient outcomes while reducing costs is incredible. Healthcare leaders need to start preparing for this shift now.",
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comment="Great insights! How do you see this affecting smaller healthcare providers who might not have the resources for large AI implementations?",
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response_type="value_add",
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tone="professional",
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include_question=True,
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brand_voice="Expert but approachable, data-driven and helpful"
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)
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start_time = time.time()
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response = await linkedin_service.generate_comment_response(request)
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duration = time.time() - start_time
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logger.info(f"✅ Comment response generation completed in {duration:.2f} seconds")
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logger.info(f"Success: {response.success}")
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if response.success and response.response:
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logger.info(f"Primary response length: {len(response.response)} characters")
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logger.info(f"Alternative responses: {len(response.alternative_responses)}")
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logger.info(f"Tone analysis: {bool(response.tone_analysis)}")
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# Preview response
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response_preview = response.response[:150] + "..." if len(response.response) > 150 else response.response
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logger.info(f"Response preview: {response_preview}")
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if response.tone_analysis:
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logger.info(f"Detected sentiment: {response.tone_analysis.get('sentiment', 'unknown')}")
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else:
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logger.error(f"Comment response generation failed: {response.error}")
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return response.success
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except Exception as e:
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logger.error(f"❌ Error testing comment response generation: {str(e)}")
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return False
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async def test_error_handling():
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"""Test error handling with invalid requests."""
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logger.info("🧪 Testing Error Handling")
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try:
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# Test with empty topic
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request = LinkedInPostRequest(
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topic="", # Empty topic should trigger validation error
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industry="Technology",
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)
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response = await linkedin_service.generate_post(request)
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# Should still handle gracefully
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if not response.success:
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logger.info("✅ Error handling working correctly for invalid input")
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return True
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else:
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logger.warning("⚠️ Expected error handling but got successful response")
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return False
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except Exception as e:
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logger.error(f"❌ Error in error handling test: {str(e)}")
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return False
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async def run_all_tests():
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"""Run all LinkedIn content generation tests."""
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logger.info("🚀 Starting LinkedIn Content Generation Tests")
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logger.info("=" * 60)
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test_results = {}
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# Run individual tests
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test_results["post_generation"] = await test_post_generation()
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logger.info("-" * 40)
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test_results["article_generation"] = await test_article_generation()
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logger.info("-" * 40)
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test_results["carousel_generation"] = await test_carousel_generation()
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logger.info("-" * 40)
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test_results["video_script_generation"] = await test_video_script_generation()
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logger.info("-" * 40)
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test_results["comment_response_generation"] = await test_comment_response_generation()
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logger.info("-" * 40)
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test_results["error_handling"] = await test_error_handling()
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logger.info("-" * 40)
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# Summary
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logger.info("📊 Test Results Summary")
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logger.info("=" * 60)
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passed = sum(test_results.values())
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total = len(test_results)
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for test_name, result in test_results.items():
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status = "✅ PASSED" if result else "❌ FAILED"
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logger.info(f"{test_name}: {status}")
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logger.info(f"\nOverall: {passed}/{total} tests passed ({(passed/total)*100:.1f}%)")
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if passed == total:
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logger.info("🎉 All tests passed! LinkedIn content generation is working correctly.")
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else:
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logger.warning(f"⚠️ {total - passed} test(s) failed. Please check the implementation.")
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return passed == total
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if __name__ == "__main__":
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# Run the tests
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success = asyncio.run(run_all_tests())
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if success:
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logger.info("\n✅ LinkedIn content generation migration completed successfully!")
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logger.info("The FastAPI endpoints are ready for use.")
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else:
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logger.error("\n❌ Some tests failed. Please review the implementation.")
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# Print API endpoint information
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logger.info("\n📡 Available LinkedIn Content Generation Endpoints:")
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logger.info("- POST /api/linkedin/generate-post")
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logger.info("- POST /api/linkedin/generate-article")
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logger.info("- POST /api/linkedin/generate-carousel")
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logger.info("- POST /api/linkedin/generate-video-script")
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logger.info("- POST /api/linkedin/generate-comment-response")
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logger.info("- GET /api/linkedin/health")
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logger.info("- GET /api/linkedin/content-types")
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logger.info("- GET /api/linkedin/usage-stats")
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