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| from typing import Dict, Any | |
| import logging | |
| from .headline_analyzer import HeadlineAnalyzer | |
| from .sentiment_analyzer import SentimentAnalyzer | |
| from .bias_analyzer import BiasAnalyzer | |
| from .evidence_analyzer import EvidenceAnalyzer | |
| logger = logging.getLogger(__name__) | |
| class MediaScorer: | |
| def __init__(self): | |
| """Initialize the MediaScorer with required analyzers.""" | |
| self.headline_analyzer = HeadlineAnalyzer() | |
| self.sentiment_analyzer = SentimentAnalyzer() | |
| self.bias_analyzer = BiasAnalyzer() | |
| self.evidence_analyzer = EvidenceAnalyzer() | |
| def calculate_media_score(self, headline: str, content: str) -> Dict[str, Any]: | |
| """Calculate final media credibility score.""" | |
| try: | |
| headline_analysis = self.headline_analyzer.analyze(headline, content) | |
| sentiment_analysis = self.sentiment_analyzer.analyze(content) | |
| bias_analysis = self.bias_analyzer.analyze(content) | |
| evidence_analysis = self.evidence_analyzer.analyze(content) | |
| # Log intermediate results | |
| logger.info("\n=== Raw Analysis Results ===") | |
| logger.info(f"Headline Analysis: {headline_analysis}") | |
| logger.info(f"Sentiment Analysis: {sentiment_analysis}") | |
| logger.info(f"""Bias Analysis: | |
| Raw: {bias_analysis} | |
| Label: {bias_analysis['bias']} | |
| Score: {bias_analysis['bias_score']} | |
| Percentage: {bias_analysis['bias_percentage']}% | |
| """) | |
| logger.info(f"Evidence Analysis: {evidence_analysis}") | |
| # Calculate component scores | |
| # For headline: 20% contradiction = 20% score (don't invert) | |
| headline_score = headline_analysis["headline_vs_content_score"] / 100 | |
| # For manipulation: 0% = good (use directly), 100% = bad | |
| manipulation_score = (100 - sentiment_analysis["manipulation_score"]) / 100 | |
| # For bias: 0% = good (use directly), 100% = bad | |
| bias_score = (100 - bias_analysis["bias_percentage"]) / 100 | |
| evidence_score = evidence_analysis["evidence_based_score"] / 100 # Higher is better | |
| logger.info(f"""Component Scores: | |
| Headline: {headline_score * 100:.1f}% (from {headline_analysis["headline_vs_content_score"]}%) | |
| Evidence: {evidence_score * 100:.1f}% | |
| Manipulation: {manipulation_score * 100:.1f}% (100 - {sentiment_analysis["manipulation_score"]}%) | |
| Bias: {bias_score * 100:.1f}% (100 - {bias_analysis["bias_percentage"]}%) | |
| """) | |
| # Calculate final score | |
| final_score = ( | |
| (headline_score * 0.25) + | |
| (manipulation_score * 0.25) + | |
| (bias_score * 0.25) + | |
| (evidence_score * 0.25) | |
| ) * 100 | |
| # Determine rating | |
| if final_score >= 80: | |
| rating = "Trustworthy" | |
| elif final_score >= 50: | |
| rating = "Bias Present" | |
| else: | |
| rating = "Misleading" | |
| result = { | |
| "media_unmasked_score": round(final_score, 1), | |
| "rating": rating, | |
| "details": { | |
| "headline_analysis": headline_analysis, | |
| "sentiment_analysis": sentiment_analysis, | |
| "bias_analysis": bias_analysis, | |
| "evidence_analysis": evidence_analysis | |
| } | |
| } | |
| logger.info("\n=== Final Score Result ===") | |
| logger.info(f"Result: {result}") | |
| return result | |
| except Exception as e: | |
| logger.error(f"Error calculating media score: {str(e)}") | |
| return { | |
| "media_unmasked_score": 0, | |
| "rating": "Error", | |
| "details": { | |
| "headline_analysis": {"headline_vs_content_score": 0, "contradictory_phrases": []}, | |
| "sentiment_analysis": {"sentiment": "Error", "manipulation_score": 0, "flagged_phrases": []}, | |
| "bias_analysis": {"bias": "Error", "bias_score": 0.0, "bias_percentage": 0}, | |
| "evidence_analysis": {"evidence_based_score": 0} | |
| } | |
| } |