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from fastapi import FastAPI, HTTPException | |
from fastapi.middleware.cors import CORSMiddleware | |
from pydantic import BaseModel, HttpUrl, Field | |
from typing import Dict, Any, List | |
import logging | |
import sys | |
import os | |
# Add src to Python path | |
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src')) | |
from mediaunmasked.scrapers.article_scraper import ArticleScraper | |
from mediaunmasked.analyzers.scoring import MediaScorer | |
from mediaunmasked.utils.logging_config import setup_logging | |
# Initialize logging | |
setup_logging() | |
logger = logging.getLogger(__name__) | |
# Initialize FastAPI with dependencies | |
app = FastAPI() | |
scraper = ArticleScraper() | |
scorer = MediaScorer() | |
# Configure CORS | |
app.add_middleware( | |
CORSMiddleware, | |
allow_origins=["*"], | |
allow_credentials=True, | |
allow_methods=["*"], | |
allow_headers=["*"], | |
) | |
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 | |
flagged_phrases: List[str] | |
media_score: MediaScore | |
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}") | |
# 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']}") | |
# Ensure correct types in response | |
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']), | |
"flagged_phrases": list(analysis['details']['sentiment_analysis']['flagged_phrases']), | |
"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']), | |
"contradictory_phrases": analysis['details']['headline_analysis'].get('contradictory_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']) | |
}, | |
"evidence_analysis": { | |
"evidence_based_score": float(analysis['details']['evidence_analysis']['evidence_based_score']) | |
} | |
} | |
} | |
} | |
# Log the final structure | |
logger.info("Final response structure:") | |
logger.info(response_dict) | |
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)}" | |
) | |
async def debug_response(): | |
mock_analysis = { | |
"headline": "Test Headline", | |
"content": "Test content", | |
"sentiment": "Neutral", | |
"bias": "Neutral", | |
"bias_score": 0.75, # Note: 0-1 scale | |
"bias_percentage": 0, | |
"flagged_phrases": ["test phrase"], | |
"media_score": { | |
"media_unmasked_score": 75.5, | |
"rating": "Some Bias Present", | |
"details": { | |
"headline_analysis": { | |
"headline_vs_content_score": 20, | |
"contradictory_phrases": ["Sample contradiction"] | |
}, | |
"sentiment_analysis": { | |
"sentiment": "Neutral", | |
"manipulation_score": 30, | |
"flagged_phrases": ["Sample manipulative phrase"] | |
}, | |
"bias_analysis": { | |
"bias": "Neutral", | |
"bias_score": 0.75, | |
"bias_percentage": 0 | |
}, | |
"evidence_analysis": { | |
"evidence_based_score": 80 | |
} | |
} | |
} | |
} | |
return AnalysisResponse.parse_obj(mock_analysis) |