Spaces:
Running
Running
File size: 7,261 Bytes
553537a a1c1173 a11d742 628d40e a1c1173 876b12f a1c1173 876b12f 553537a a11d742 9a2420b a11d742 7c1cdd8 628d40e 7c1cdd8 628d40e a1c1173 553537a a1c1173 876b12f a1c1173 628d40e 7c1cdd8 628d40e a1c1173 a11d742 a1c1173 9a2420b a11d742 876b12f a1c1173 628d40e a11d742 a1c1173 628d40e 7c1cdd8 628d40e a1c1173 628d40e a1c1173 876b12f a1c1173 a11d742 a1c1173 a11d742 a1c1173 553537a a1c1173 628d40e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, HttpUrl
from typing import Dict, Any, List
import logging
import os
from supabase import create_client
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 router and dependencies
router = APIRouter(tags=["analysis"])
scraper = ArticleScraper()
scorer = MediaScorer()
# Initialize Supabase connection (works for async environments)
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY = os.getenv("SUPABASE_KEY")
supabase = create_client(SUPABASE_URL, SUPABASE_KEY) # This works for async
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
@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.status_code == 200 and existing_article.data:
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']),
"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'])
}
}
}
}
# 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'],
'flagged_phrases': response_dict['flagged_phrases'],
'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)}"
)
@router.get("/debug")
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)
|