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from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, HttpUrl
from typing import Dict, Any, List
import logging
import os
from supabase import AsyncClient
from dotenv import load_dotenv
from mediaunmasked.scrapers.article_scraper import ArticleScraper
from mediaunmasked.analyzers.scoring import MediaScorer
from mediaunmasked.utils.logging_config import setup_logging
# Load environment variables
load_dotenv()
# Initialize logging
setup_logging()
logger = logging.getLogger(__name__)
# Initialize router and dependencies
router = APIRouter(tags=["analysis"])
scraper = ArticleScraper()
scorer = MediaScorer()
# Get Supabase credentials
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY = os.getenv("SUPABASE_KEY")
# Initialize Supabase client
if not SUPABASE_URL or not SUPABASE_KEY:
raise Exception("Supabase credentials not found in environment variables")
supabase = AsyncClient(SUPABASE_URL, SUPABASE_KEY)
class ArticleRequest(BaseModel):
url: HttpUrl
class MediaScoreDetails(BaseModel):
headline_analysis: Dict[str, Any]
sentiment_analysis: Dict[str, Any]
bias_analysis: Dict[str, Any]
evidence_analysis: Dict[str, Any]
class MediaScore(BaseModel):
media_unmasked_score: float
rating: str
details: MediaScoreDetails
class AnalysisResponse(BaseModel):
headline: str
content: str
sentiment: str
bias: str
bias_score: float
bias_percentage: float
media_score: MediaScore
@router.post("/analyze", response_model=AnalysisResponse)
async def analyze_article(request: ArticleRequest) -> AnalysisResponse:
"""
Analyze an article for bias, sentiment, and credibility.
Args:
request: ArticleRequest containing the URL to analyze
Returns:
AnalysisResponse with complete analysis results
Raises:
HTTPException: If scraping or analysis fails
"""
try:
logger.info(f"Analyzing article: {request.url}")
# Check if the article has already been analyzed
existing_article = await supabase.table('article_analysis').select('*').eq('url', str(request.url)).execute()
if existing_article.data and len(existing_article.data) > 0:
logger.info("Article already analyzed. Returning cached data.")
# Return the existing analysis result if it exists
cached_data = existing_article.data[0]
return AnalysisResponse.parse_obj(cached_data)
# Scrape article
article = scraper.scrape_article(str(request.url))
if not article:
raise HTTPException(
status_code=400,
detail="Failed to scrape article content"
)
# Analyze content
analysis = scorer.calculate_media_score(
article["headline"],
article["content"]
)
# Log raw values for debugging
logger.info("Raw values:")
logger.info(f"media_unmasked_score type: {type(analysis['media_unmasked_score'])}")
logger.info(f"media_unmasked_score value: {analysis['media_unmasked_score']}")
# Prepare response data
response_dict = {
"headline": str(article['headline']),
"content": str(article['content']),
"sentiment": str(analysis['details']['sentiment_analysis']['sentiment']),
"bias": str(analysis['details']['bias_analysis']['bias']),
"bias_score": float(analysis['details']['bias_analysis']['bias_score']),
"bias_percentage": float(analysis['details']['bias_analysis']['bias_percentage']),
"media_score": {
"media_unmasked_score": float(analysis['media_unmasked_score']),
"rating": str(analysis['rating']),
"details": {
"headline_analysis": {
"headline_vs_content_score": float(analysis['details']['headline_analysis']['headline_vs_content_score']),
"flagged_phrases": analysis['details']['headline_analysis'].get('flagged_phrases', [])
},
"sentiment_analysis": {
"sentiment": str(analysis['details']['sentiment_analysis']['sentiment']),
"manipulation_score": float(analysis['details']['sentiment_analysis']['manipulation_score']),
"flagged_phrases": list(analysis['details']['sentiment_analysis']['flagged_phrases'])
},
"bias_analysis": {
"bias": str(analysis['details']['bias_analysis']['bias']),
"bias_score": float(analysis['details']['bias_analysis']['bias_score']),
"bias_percentage": float(analysis['details']['bias_analysis']['bias_percentage']),
"flagged_phrases": list(analysis['details']['bias_analysis']['flagged_phrases'])
},
"evidence_analysis": {
"evidence_based_score": float(analysis['details']['evidence_analysis']['evidence_based_score']),
"flagged_phrases": list(analysis['details']['evidence_analysis']['flagged_phrases'])
}
}
}
}
# Save the new analysis to Supabase
await supabase.table('article_analysis').upsert({
'url': str(request.url),
'headline': response_dict['headline'],
'content': response_dict['content'],
'sentiment': response_dict['sentiment'],
'bias': response_dict['bias'],
'bias_score': response_dict['bias_score'],
'bias_percentage': response_dict['bias_percentage'],
'media_score': response_dict['media_score']
}).execute()
# Return the response
return AnalysisResponse.parse_obj(response_dict)
except Exception as e:
logger.error(f"Analysis failed: {str(e)}", exc_info=True)
raise HTTPException(
status_code=500,
detail=f"Analysis failed: {str(e)}"
)
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