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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) |