removing extra analyzer_service.py

mediaunmasked/services/analyzer_service.py

@@ -1,84 +0,0 @@
-from mediaunmasked.analyzers.headline_analyzer import HeadlineAnalyzer
-from mediaunmasked.schemas.requests import AnalyzeRequest, AnalysisResponse
-from fastapi import HTTPException
-from mediaunmasked.scrapers.article_scraper import ArticleScraper
-from mediaunmasked.analyzers.scoring import MediaScorer
-import logging
-
-logger = logging.getLogger(__name__)
-scraper = ArticleScraper()
-scorer = MediaScorer()
-
-class AnalyzerService:
-    def __init__(self):
-        self.headline_analyzer = HeadlineAnalyzer()
-
-    async def analyze_content(self, headline: str, content: str):
-        result = self.headline_analyzer.analyze(headline, content)
-        return result 
-
-    async def analyze_url(self, request: AnalyzeRequest) -> AnalysisResponse:
-        """
-        Analyze an article for bias, sentiment, and credibility.
-        """
-        try:
-            # Convert URL to string explicitly
-            url_str = str(request.url)
-            logger.info(f"Analyzing article: {url_str}")
-            
-            # Scrape article (now synchronous)
-            article = scraper.scrape_article(url_str)
-            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"]
-            )
-            
-            # Construct 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'])
-                        }
-                    }
-                }
-            }
-            
-            return AnalysisResponse.parse_obj(response_dict)
-            
-        except Exception as e:
-            logger.error(f"Analysis failed inside of analyzer_service.py: {str(e)}", exc_info=True)
-            raise HTTPException(
-                status_code=500,
-                detail=f"Analysis failed inside of analyzer_service.py: {str(e)}"
-            ) 

mediaunmasked/web/__init__.py

@@ -1 +0,0 @@
-# Empty file is fine 

mediaunmasked/web/app.py

@@ -1,82 +0,0 @@
-import streamlit as st
-from ..analyzers.bias_analyzer import BiasAnalyzer
-from ..scrapers.article_scraper import ArticleScraper
-from ..utils.logging_config import setup_logging
-import plotly.graph_objects as go
-
-def create_sentiment_gauge(score: float) -> go.Figure:
-    """Create a gauge chart for sentiment visualization."""
-    fig = go.Figure(go.Indicator(
-        mode = "gauge+number",
-        value = score * 100,
-        title = {'text': "Sentiment Score"},
-        gauge = {
-            'axis': {'range': [0, 100]},
-            'bar': {'color': "darkblue"},
-            'steps': [
-                {'range': [0, 33], 'color': "lightgray"},
-                {'range': [33, 66], 'color': "gray"},
-                {'range': [66, 100], 'color': "darkgray"}
-            ],
-        }
-    ))
-    return fig
-
-def main():
-    # Set up logging
-    setup_logging()
-    
-    # Initialize components
-    scraper = ArticleScraper()
-    analyzer = BiasAnalyzer()
-    
-    # Set up the Streamlit interface
-    st.title("Media Bias Analyzer")
-    st.write("Analyze bias and sentiment in news articles")
-    
-    # URL input
-    url = st.text_input("Enter article URL:", "https://www.snopes.com/articles/469232/musk-son-told-trump-shut-up/")
-    
-    if st.button("Analyze"):
-        with st.spinner("Analyzing article..."):
-            # Scrape the article
-            article = scraper.scrape_article(url)
-            
-            if article:
-                # Show article details
-                st.subheader("Article Details")
-                st.write(f"**Headline:** {article['headline']}")
-                
-                with st.expander("Show Article Content"):
-                    st.write(article['content'])
-                
-                # Analyze content
-                result = analyzer.analyze(article['content'])
-                
-                # Display results in columns
-                col1, col2 = st.columns(2)
-                
-                with col1:
-                    st.subheader("Sentiment Analysis")
-                    st.write(f"**Overall Sentiment:** {result.sentiment}")
-                    fig = create_sentiment_gauge(result.bias_score / 100)
-                    st.plotly_chart(fig)
-                
-                with col2:
-                    st.subheader("Bias Analysis")
-                    st.write(f"**Detected Bias:** {result.bias}")
-                    st.write(f"**Confidence Score:** {result.bias_score:.1f}%")
-                
-                # Show flagged phrases
-                if result.flagged_phrases:
-                    st.subheader("Potentially Biased Phrases")
-                    for phrase in result.flagged_phrases:
-                        st.warning(phrase)
-                else:
-                    st.info("No potentially biased phrases detected")
-                    
-            else:
-                st.error("Failed to fetch article. Please check the URL and try again.")
-
-if __name__ == "__main__":
-    main()