Base code
This commit is contained in:
Kunthawat Greethong
728
backend/routers/linkedin.py
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728
backend/routers/linkedin.py
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@@ -0,0 +1,728 @@
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"""
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LinkedIn Content Generation Router
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FastAPI router for LinkedIn content generation endpoints.
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Provides comprehensive LinkedIn content creation functionality with
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proper error handling, monitoring, and documentation.
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"""
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from fastapi import APIRouter, HTTPException, Depends, BackgroundTasks, Request
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from fastapi.responses import JSONResponse
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from typing import Dict, Any, Optional
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import time
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from loguru import logger
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from pathlib import Path
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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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LinkedInPostResponse, LinkedInArticleResponse, LinkedInCarouselResponse,
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LinkedInVideoScriptResponse, LinkedInCommentResponseResult
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)
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from services.linkedin_service import LinkedInService
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from middleware.auth_middleware import get_current_user
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from utils.text_asset_tracker import save_and_track_text_content
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# Initialize the LinkedIn service instance
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linkedin_service = LinkedInService()
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from services.subscription.monitoring_middleware import DatabaseAPIMonitor
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from services.database import get_db as get_db_dependency
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from sqlalchemy.orm import Session
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# Initialize router
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router = APIRouter(
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prefix="/api/linkedin",
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tags=["LinkedIn Content Generation"],
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responses={
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404: {"description": "Not found"},
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422: {"description": "Validation error"},
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500: {"description": "Internal server error"}
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}
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)
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# Initialize monitoring
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monitor = DatabaseAPIMonitor()
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# Use the proper database dependency from services.database
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get_db = get_db_dependency
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async def log_api_request(request: Request, db: Session, duration: float, status_code: int):
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"""Log API request to database for monitoring."""
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try:
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await monitor.add_request(
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db=db,
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path=str(request.url.path),
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method=request.method,
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status_code=status_code,
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duration=duration,
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user_id=request.headers.get("X-User-ID"),
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request_size=len(await request.body()) if request.method == "POST" else 0,
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user_agent=request.headers.get("User-Agent"),
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ip_address=request.client.host if request.client else None
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)
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db.commit()
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except Exception as e:
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logger.error(f"Failed to log API request: {str(e)}")
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@router.get("/health", summary="Health Check", description="Check LinkedIn service health")
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async def health_check():
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"""Health check endpoint for LinkedIn service."""
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return {
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"status": "healthy",
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"service": "linkedin_content_generation",
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"version": "1.0.0",
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"timestamp": time.time()
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}
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@router.post(
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"/generate-post",
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response_model=LinkedInPostResponse,
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summary="Generate LinkedIn Post",
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description="""
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Generate a professional LinkedIn post with AI-powered content creation.
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Features:
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- Research-backed content using multiple search engines
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- Industry-specific optimization
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- Hashtag generation and optimization
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- Call-to-action suggestions
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- Engagement prediction
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- Multiple tone and style options
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The service conducts research on the specified topic and industry,
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then generates engaging content optimized for LinkedIn's algorithm.
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"""
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)
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async def generate_post(
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request: LinkedInPostRequest,
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background_tasks: BackgroundTasks,
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http_request: Request,
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db: Session = Depends(get_db),
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current_user: Optional[Dict[str, Any]] = Depends(get_current_user)
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):
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"""Generate a LinkedIn post based on the provided parameters."""
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start_time = time.time()
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try:
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logger.info(f"Received LinkedIn post generation request for topic: {request.topic}")
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# Validate request
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if not request.topic.strip():
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raise HTTPException(status_code=422, detail="Topic cannot be empty")
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if not request.industry.strip():
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raise HTTPException(status_code=422, detail="Industry cannot be empty")
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# Extract user_id
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user_id = None
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if current_user:
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user_id = str(current_user.get('id', '') or current_user.get('sub', ''))
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if not user_id:
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user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
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# Generate post content
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response = await linkedin_service.generate_linkedin_post(request)
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# Log successful request
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duration = time.time() - start_time
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 200
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)
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if not response.success:
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raise HTTPException(status_code=500, detail=response.error)
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# Save and track text content (non-blocking)
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if user_id and response.data and response.data.content:
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try:
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# Combine all text content
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text_content = response.data.content
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if response.data.call_to_action:
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text_content += f"\n\nCall to Action: {response.data.call_to_action}"
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if response.data.hashtags:
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hashtag_text = " ".join([f"#{h.hashtag}" if isinstance(h, dict) else f"#{h.get('hashtag', '')}" for h in response.data.hashtags])
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text_content += f"\n\nHashtags: {hashtag_text}"
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save_and_track_text_content(
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db=db,
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user_id=user_id,
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content=text_content,
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source_module="linkedin_writer",
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title=f"LinkedIn Post: {request.topic[:80]}",
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description=f"LinkedIn post for {request.industry} industry",
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prompt=f"Topic: {request.topic}\nIndustry: {request.industry}\nTone: {request.tone}",
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tags=["linkedin", "post", request.industry.lower().replace(' ', '_')],
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asset_metadata={
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"post_type": request.post_type.value if hasattr(request.post_type, 'value') else str(request.post_type),
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"tone": request.tone.value if hasattr(request.tone, 'value') else str(request.tone),
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"character_count": response.data.character_count,
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"hashtag_count": len(response.data.hashtags),
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"grounding_enabled": response.data.grounding_enabled if hasattr(response.data, 'grounding_enabled') else False
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},
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subdirectory="posts"
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)
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except Exception as track_error:
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logger.warning(f"Failed to track LinkedIn post asset: {track_error}")
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logger.info(f"Successfully generated LinkedIn post in {duration:.2f} seconds")
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return response
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except HTTPException:
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raise
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except Exception as e:
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duration = time.time() - start_time
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logger.error(f"Error generating LinkedIn post: {str(e)}")
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# Log failed request
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 500
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)
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raise HTTPException(
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status_code=500,
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detail=f"Failed to generate LinkedIn post: {str(e)}"
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)
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@router.post(
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"/generate-article",
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response_model=LinkedInArticleResponse,
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summary="Generate LinkedIn Article",
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description="""
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Generate a comprehensive LinkedIn article with AI-powered content creation.
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Features:
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- Long-form content generation
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- Research-backed insights and data
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- SEO optimization for LinkedIn
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- Section structuring and organization
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- Image placement suggestions
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- Reading time estimation
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- Multiple research sources integration
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Perfect for thought leadership and in-depth industry analysis.
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"""
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)
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async def generate_article(
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request: LinkedInArticleRequest,
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background_tasks: BackgroundTasks,
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http_request: Request,
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db: Session = Depends(get_db),
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current_user: Optional[Dict[str, Any]] = Depends(get_current_user)
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):
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"""Generate a LinkedIn article based on the provided parameters."""
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start_time = time.time()
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try:
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logger.info(f"Received LinkedIn article generation request for topic: {request.topic}")
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# Validate request
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if not request.topic.strip():
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raise HTTPException(status_code=422, detail="Topic cannot be empty")
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if not request.industry.strip():
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raise HTTPException(status_code=422, detail="Industry cannot be empty")
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# Extract user_id
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user_id = None
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if current_user:
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user_id = str(current_user.get('id', '') or current_user.get('sub', ''))
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if not user_id:
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user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
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# Generate article content
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response = await linkedin_service.generate_linkedin_article(request)
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# Log successful request
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duration = time.time() - start_time
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 200
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)
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if not response.success:
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raise HTTPException(status_code=500, detail=response.error)
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# Save and track text content (non-blocking)
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if user_id and response.data:
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try:
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# Combine article content
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text_content = f"# {response.data.title}\n\n"
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text_content += response.data.content
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if response.data.sections:
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text_content += "\n\n## Sections:\n"
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for section in response.data.sections:
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if isinstance(section, dict):
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text_content += f"\n### {section.get('heading', 'Section')}\n{section.get('content', '')}\n"
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if response.data.seo_metadata:
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text_content += f"\n\n## SEO Metadata\n{response.data.seo_metadata}\n"
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save_and_track_text_content(
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db=db,
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user_id=user_id,
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content=text_content,
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source_module="linkedin_writer",
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title=f"LinkedIn Article: {response.data.title[:80] if response.data.title else request.topic[:80]}",
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description=f"LinkedIn article for {request.industry} industry",
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prompt=f"Topic: {request.topic}\nIndustry: {request.industry}\nTone: {request.tone}\nWord Count: {request.word_count}",
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tags=["linkedin", "article", request.industry.lower().replace(' ', '_')],
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asset_metadata={
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"tone": request.tone.value if hasattr(request.tone, 'value') else str(request.tone),
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"word_count": response.data.word_count,
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"reading_time": response.data.reading_time,
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"section_count": len(response.data.sections) if response.data.sections else 0,
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"grounding_enabled": response.data.grounding_enabled if hasattr(response.data, 'grounding_enabled') else False
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},
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subdirectory="articles",
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file_extension=".md"
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)
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except Exception as track_error:
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logger.warning(f"Failed to track LinkedIn article asset: {track_error}")
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logger.info(f"Successfully generated LinkedIn article in {duration:.2f} seconds")
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return response
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except HTTPException:
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raise
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except Exception as e:
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duration = time.time() - start_time
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logger.error(f"Error generating LinkedIn article: {str(e)}")
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# Log failed request
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 500
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)
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raise HTTPException(
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status_code=500,
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detail=f"Failed to generate LinkedIn article: {str(e)}"
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)
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@router.post(
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"/generate-carousel",
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response_model=LinkedInCarouselResponse,
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summary="Generate LinkedIn Carousel",
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description="""
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Generate a LinkedIn carousel post with multiple slides.
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Features:
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- Multi-slide content generation
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- Visual hierarchy optimization
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- Story arc development
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- Design guidelines and suggestions
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- Cover and CTA slide options
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- Professional slide structuring
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Ideal for step-by-step guides, tips, and visual storytelling.
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"""
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)
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async def generate_carousel(
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request: LinkedInCarouselRequest,
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background_tasks: BackgroundTasks,
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http_request: Request,
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db: Session = Depends(get_db),
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current_user: Optional[Dict[str, Any]] = Depends(get_current_user)
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):
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"""Generate a LinkedIn carousel based on the provided parameters."""
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start_time = time.time()
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try:
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logger.info(f"Received LinkedIn carousel generation request for topic: {request.topic}")
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# Validate request
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if not request.topic.strip():
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raise HTTPException(status_code=422, detail="Topic cannot be empty")
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if not request.industry.strip():
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raise HTTPException(status_code=422, detail="Industry cannot be empty")
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if request.slide_count < 3 or request.slide_count > 15:
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raise HTTPException(status_code=422, detail="Slide count must be between 3 and 15")
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# Extract user_id
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user_id = None
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if current_user:
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user_id = str(current_user.get('id', '') or current_user.get('sub', ''))
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if not user_id:
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user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
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# Generate carousel content
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response = await linkedin_service.generate_linkedin_carousel(request)
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# Log successful request
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duration = time.time() - start_time
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 200
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)
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if not response.success:
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raise HTTPException(status_code=500, detail=response.error)
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# Save and track text content (non-blocking)
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if user_id and response.data:
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try:
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# Combine carousel content
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text_content = f"# {response.data.title}\n\n"
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for slide in response.data.slides:
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text_content += f"\n## Slide {slide.slide_number}: {slide.title}\n{slide.content}\n"
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if slide.visual_elements:
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text_content += f"\nVisual Elements: {', '.join(slide.visual_elements)}\n"
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save_and_track_text_content(
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db=db,
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user_id=user_id,
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content=text_content,
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source_module="linkedin_writer",
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title=f"LinkedIn Carousel: {response.data.title[:80] if response.data.title else request.topic[:80]}",
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description=f"LinkedIn carousel for {request.industry} industry",
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prompt=f"Topic: {request.topic}\nIndustry: {request.industry}\nSlides: {getattr(request, 'number_of_slides', request.slide_count if hasattr(request, 'slide_count') else 5)}",
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tags=["linkedin", "carousel", request.industry.lower().replace(' ', '_')],
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asset_metadata={
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"slide_count": len(response.data.slides),
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"has_cover": response.data.cover_slide is not None,
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"has_cta": response.data.cta_slide is not None
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},
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subdirectory="carousels",
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file_extension=".md"
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)
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except Exception as track_error:
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logger.warning(f"Failed to track LinkedIn carousel asset: {track_error}")
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logger.info(f"Successfully generated LinkedIn carousel in {duration:.2f} seconds")
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return response
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except HTTPException:
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raise
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except Exception as e:
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duration = time.time() - start_time
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logger.error(f"Error generating LinkedIn carousel: {str(e)}")
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# Log failed request
|
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 500
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||||
)
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raise HTTPException(
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status_code=500,
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detail=f"Failed to generate LinkedIn carousel: {str(e)}"
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)
|
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@router.post(
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"/generate-video-script",
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response_model=LinkedInVideoScriptResponse,
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summary="Generate LinkedIn Video Script",
|
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description="""
|
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Generate a LinkedIn video script optimized for engagement.
|
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|
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Features:
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- Attention-grabbing hooks
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- Structured storytelling
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- Visual cue suggestions
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||||
- Caption generation
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- Thumbnail text recommendations
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- Timing and pacing guidance
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Perfect for creating professional video content for LinkedIn.
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"""
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)
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async def generate_video_script(
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request: LinkedInVideoScriptRequest,
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background_tasks: BackgroundTasks,
|
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http_request: Request,
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db: Session = Depends(get_db),
|
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current_user: Optional[Dict[str, Any]] = Depends(get_current_user)
|
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):
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"""Generate a LinkedIn video script based on the provided parameters."""
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start_time = time.time()
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||||
|
||||
try:
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logger.info(f"Received LinkedIn video script generation request for topic: {request.topic}")
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||||
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# Validate request
|
||||
if not request.topic.strip():
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raise HTTPException(status_code=422, detail="Topic cannot be empty")
|
||||
|
||||
if not request.industry.strip():
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||||
raise HTTPException(status_code=422, detail="Industry cannot be empty")
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||||
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video_duration = getattr(request, 'video_duration', getattr(request, 'video_length', 60))
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if video_duration < 15 or video_duration > 300:
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raise HTTPException(status_code=422, detail="Video length must be between 15 and 300 seconds")
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||||
|
||||
# Extract user_id
|
||||
user_id = None
|
||||
if current_user:
|
||||
user_id = str(current_user.get('id', '') or current_user.get('sub', ''))
|
||||
if not user_id:
|
||||
user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
|
||||
|
||||
# Generate video script content
|
||||
response = await linkedin_service.generate_linkedin_video_script(request)
|
||||
|
||||
# Log successful request
|
||||
duration = time.time() - start_time
|
||||
background_tasks.add_task(
|
||||
log_api_request, http_request, db, duration, 200
|
||||
)
|
||||
|
||||
if not response.success:
|
||||
raise HTTPException(status_code=500, detail=response.error)
|
||||
|
||||
# Save and track text content (non-blocking)
|
||||
if user_id and response.data:
|
||||
try:
|
||||
# Combine video script content
|
||||
text_content = f"# Video Script: {request.topic}\n\n"
|
||||
text_content += f"## Hook\n{response.data.hook}\n\n"
|
||||
text_content += "## Main Content\n"
|
||||
for scene in response.data.main_content:
|
||||
if isinstance(scene, dict):
|
||||
text_content += f"\n### Scene {scene.get('scene_number', '')}\n"
|
||||
text_content += f"{scene.get('content', '')}\n"
|
||||
if scene.get('duration'):
|
||||
text_content += f"Duration: {scene.get('duration')}s\n"
|
||||
if scene.get('visual_notes'):
|
||||
text_content += f"Visual Notes: {scene.get('visual_notes')}\n"
|
||||
text_content += f"\n## Conclusion\n{response.data.conclusion}\n"
|
||||
if response.data.captions:
|
||||
text_content += f"\n## Captions\n" + "\n".join(response.data.captions) + "\n"
|
||||
if response.data.thumbnail_suggestions:
|
||||
text_content += f"\n## Thumbnail Suggestions\n" + "\n".join(response.data.thumbnail_suggestions) + "\n"
|
||||
|
||||
save_and_track_text_content(
|
||||
db=db,
|
||||
user_id=user_id,
|
||||
content=text_content,
|
||||
source_module="linkedin_writer",
|
||||
title=f"LinkedIn Video Script: {request.topic[:80]}",
|
||||
description=f"LinkedIn video script for {request.industry} industry",
|
||||
prompt=f"Topic: {request.topic}\nIndustry: {request.industry}\nDuration: {video_duration}s",
|
||||
tags=["linkedin", "video_script", request.industry.lower().replace(' ', '_')],
|
||||
asset_metadata={
|
||||
"video_duration": video_duration,
|
||||
"scene_count": len(response.data.main_content),
|
||||
"has_captions": bool(response.data.captions)
|
||||
},
|
||||
subdirectory="video_scripts",
|
||||
file_extension=".md"
|
||||
)
|
||||
except Exception as track_error:
|
||||
logger.warning(f"Failed to track LinkedIn video script asset: {track_error}")
|
||||
|
||||
logger.info(f"Successfully generated LinkedIn video script in {duration:.2f} seconds")
|
||||
return response
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
duration = time.time() - start_time
|
||||
logger.error(f"Error generating LinkedIn video script: {str(e)}")
|
||||
|
||||
# Log failed request
|
||||
background_tasks.add_task(
|
||||
log_api_request, http_request, db, duration, 500
|
||||
)
|
||||
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Failed to generate LinkedIn video script: {str(e)}"
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/generate-comment-response",
|
||||
response_model=LinkedInCommentResponseResult,
|
||||
summary="Generate LinkedIn Comment Response",
|
||||
description="""
|
||||
Generate professional responses to LinkedIn comments.
|
||||
|
||||
Features:
|
||||
- Context-aware responses
|
||||
- Multiple response type options
|
||||
- Tone optimization
|
||||
- Brand voice customization
|
||||
- Alternative response suggestions
|
||||
- Engagement goal targeting
|
||||
|
||||
Helps maintain professional engagement and build relationships.
|
||||
"""
|
||||
)
|
||||
async def generate_comment_response(
|
||||
request: LinkedInCommentResponseRequest,
|
||||
background_tasks: BackgroundTasks,
|
||||
http_request: Request,
|
||||
db: Session = Depends(get_db),
|
||||
current_user: Optional[Dict[str, Any]] = Depends(get_current_user)
|
||||
):
|
||||
"""Generate a LinkedIn comment response based on the provided parameters."""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
logger.info("Received LinkedIn comment response generation request")
|
||||
|
||||
# Validate request
|
||||
original_comment = getattr(request, 'original_comment', getattr(request, 'comment', ''))
|
||||
post_context = getattr(request, 'post_context', getattr(request, 'original_post', ''))
|
||||
|
||||
if not original_comment.strip():
|
||||
raise HTTPException(status_code=422, detail="Original comment cannot be empty")
|
||||
|
||||
if not post_context.strip():
|
||||
raise HTTPException(status_code=422, detail="Post context cannot be empty")
|
||||
|
||||
# Extract user_id
|
||||
user_id = None
|
||||
if current_user:
|
||||
user_id = str(current_user.get('id', '') or current_user.get('sub', ''))
|
||||
if not user_id:
|
||||
user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
|
||||
|
||||
# Generate comment response
|
||||
response = await linkedin_service.generate_linkedin_comment_response(request)
|
||||
|
||||
# Log successful request
|
||||
duration = time.time() - start_time
|
||||
background_tasks.add_task(
|
||||
log_api_request, http_request, db, duration, 200
|
||||
)
|
||||
|
||||
if not response.success:
|
||||
raise HTTPException(status_code=500, detail=response.error)
|
||||
|
||||
# Save and track text content (non-blocking)
|
||||
if user_id and hasattr(response, 'response') and response.response:
|
||||
try:
|
||||
text_content = f"# Comment Response\n\n"
|
||||
text_content += f"## Original Comment\n{original_comment}\n\n"
|
||||
text_content += f"## Post Context\n{post_context}\n\n"
|
||||
text_content += f"## Generated Response\n{response.response}\n"
|
||||
if hasattr(response, 'alternatives') and response.alternatives:
|
||||
text_content += f"\n## Alternative Responses\n"
|
||||
for i, alt in enumerate(response.alternatives, 1):
|
||||
text_content += f"\n### Alternative {i}\n{alt}\n"
|
||||
|
||||
save_and_track_text_content(
|
||||
db=db,
|
||||
user_id=user_id,
|
||||
content=text_content,
|
||||
source_module="linkedin_writer",
|
||||
title=f"LinkedIn Comment Response: {original_comment[:60]}",
|
||||
description=f"LinkedIn comment response for {request.industry} industry",
|
||||
prompt=f"Original Comment: {original_comment}\nPost Context: {post_context}\nIndustry: {request.industry}",
|
||||
tags=["linkedin", "comment_response", request.industry.lower().replace(' ', '_')],
|
||||
asset_metadata={
|
||||
"response_length": getattr(request, 'response_length', 'medium'),
|
||||
"tone": request.tone.value if hasattr(request.tone, 'value') else str(request.tone),
|
||||
"has_alternatives": hasattr(response, 'alternatives') and bool(response.alternatives)
|
||||
},
|
||||
subdirectory="comment_responses",
|
||||
file_extension=".md"
|
||||
)
|
||||
except Exception as track_error:
|
||||
logger.warning(f"Failed to track LinkedIn comment response asset: {track_error}")
|
||||
|
||||
logger.info(f"Successfully generated LinkedIn comment response in {duration:.2f} seconds")
|
||||
return response
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
duration = time.time() - start_time
|
||||
logger.error(f"Error generating LinkedIn comment response: {str(e)}")
|
||||
|
||||
# Log failed request
|
||||
background_tasks.add_task(
|
||||
log_api_request, http_request, db, duration, 500
|
||||
)
|
||||
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Failed to generate LinkedIn comment response: {str(e)}"
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/content-types",
|
||||
summary="Get Available Content Types",
|
||||
description="Get list of available LinkedIn content types and their descriptions"
|
||||
)
|
||||
async def get_content_types():
|
||||
"""Get available LinkedIn content types."""
|
||||
return {
|
||||
"content_types": {
|
||||
"post": {
|
||||
"name": "LinkedIn Post",
|
||||
"description": "Short-form content for regular LinkedIn posts",
|
||||
"max_length": 3000,
|
||||
"features": ["hashtags", "call_to_action", "engagement_prediction"]
|
||||
},
|
||||
"article": {
|
||||
"name": "LinkedIn Article",
|
||||
"description": "Long-form content for LinkedIn articles",
|
||||
"max_length": 125000,
|
||||
"features": ["seo_optimization", "image_suggestions", "reading_time"]
|
||||
},
|
||||
"carousel": {
|
||||
"name": "LinkedIn Carousel",
|
||||
"description": "Multi-slide visual content",
|
||||
"slide_range": "3-15 slides",
|
||||
"features": ["visual_guidelines", "slide_design", "story_flow"]
|
||||
},
|
||||
"video_script": {
|
||||
"name": "LinkedIn Video Script",
|
||||
"description": "Script for LinkedIn video content",
|
||||
"length_range": "15-300 seconds",
|
||||
"features": ["hooks", "visual_cues", "captions", "thumbnails"]
|
||||
},
|
||||
"comment_response": {
|
||||
"name": "Comment Response",
|
||||
"description": "Professional responses to LinkedIn comments",
|
||||
"response_types": ["professional", "appreciative", "clarifying", "disagreement", "value_add"],
|
||||
"features": ["tone_matching", "brand_voice", "alternatives"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@router.get(
|
||||
"/usage-stats",
|
||||
summary="Get Usage Statistics",
|
||||
description="Get LinkedIn content generation usage statistics"
|
||||
)
|
||||
async def get_usage_stats(db: Session = Depends(get_db)):
|
||||
"""Get usage statistics for LinkedIn content generation."""
|
||||
try:
|
||||
# This would query the database for actual usage stats
|
||||
# For now, returning mock data
|
||||
return {
|
||||
"total_requests": 1250,
|
||||
"content_types": {
|
||||
"posts": 650,
|
||||
"articles": 320,
|
||||
"carousels": 180,
|
||||
"video_scripts": 70,
|
||||
"comment_responses": 30
|
||||
},
|
||||
"success_rate": 0.96,
|
||||
"average_generation_time": 4.2,
|
||||
"top_industries": [
|
||||
"Technology",
|
||||
"Healthcare",
|
||||
"Finance",
|
||||
"Marketing",
|
||||
"Education"
|
||||
]
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Error retrieving usage stats: {str(e)}")
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail="Failed to retrieve usage statistics"
|
||||
)
|
||||
Reference in New Issue
Block a user