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refactor: Break down monolithic API into modular router structure
- Resolves #26: Monolithic API Structure issue - Refactor 2222-line app.py into organized modular architecture ## Changes Made ### New Structure - app/api/app.py: Main FastAPI app (~100 lines, was 2222) - app/api/app_original.py: Backup of original monolithic file - app/api/models/api_models.py: Pydantic models for API requests/responses - app/api/utils/api_utils.py: Shared utility functions - app/api/routers/: Modular endpoint organization - paper_analysis.py: Paper analysis endpoints (6 endpoints) - batch_processing.py: Batch processing endpoints (3 endpoints) - cache_management.py: Cache management endpoints (6 endpoints) - system.py: System/health endpoints (4 endpoints) ### Benefits Achieved - ✅ 35% reduction in total lines with better organization - ✅ Improved maintainability through separation of concerns - ✅ Enhanced testability with isolated router modules - ✅ Better scalability for team development - ✅ All 19 original endpoints preserved with /api/v1/ prefix - ✅ Backward compatibility maintained ### Endpoints Reorganized - Paper Analysis: /api/v1/analyze/{pmid}, /api/v1/enhanced_analysis/{pmid} - Batch Processing: /api/v1/upload_csv, /api/v1/analyze_batch - Cache Management: /api/v1/cache/stats, /api/v1/cache/clear - System: /api/v1/health, /api/v1/config, /api/v1/metrics ### Testing - ✅ All endpoints verified working in development environment - ✅ Frontend and backend integration confirmed - ✅ Docker development environment tested successfully This refactoring provides a solid foundation for future development while maintaining all existing functionality.
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app/api/app.py

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app/api/models/api_models.py

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"""
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Pydantic models for API requests and responses.
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"""
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from pydantic import BaseModel
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from typing import Dict, List, Optional, Any
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class Message(BaseModel):
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"""WebSocket message model."""
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content: str
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role: str = "user"
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class Question(BaseModel):
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"""Question model for paper analysis."""
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question: str
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class AnalysisRequest(BaseModel):
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"""Request model for paper analysis."""
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pmid: str
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class BatchAnalysisRequest(BaseModel):
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"""Request model for batch analysis."""
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pmids: List[str]
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page: int = 1
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page_size: int = 20
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class EnhancedBatchAnalysisRequest(BaseModel):
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"""Request model for enhanced batch analysis."""
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pmids: List[str]
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max_concurrent: int = 5
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class CacheSearchRequest(BaseModel):
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"""Request model for cache search."""
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query: str
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search_type: str = "all"
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class HealthResponse(BaseModel):
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"""Health check response model."""
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status: str
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timestamp: str
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version: str
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class ConfigResponse(BaseModel):
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"""Configuration response model."""
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available_models: List[str]
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default_model: str
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frontend_timeout: int
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gemini_timeout: int
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analysis_timeout: int
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api_timeout: int
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class MetricsResponse(BaseModel):
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"""Metrics response model."""
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total_requests: int
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successful_requests: int
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failed_requests: int
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average_response_time: float
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cache_hit_rate: float
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memory_usage: Dict[str, Any]
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class CacheStatsResponse(BaseModel):
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"""Cache statistics response model."""
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total_entries: int
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analysis_cache_entries: int
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metadata_cache_entries: int
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fulltext_cache_entries: int
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cache_size_mb: float
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oldest_entry: Optional[str]
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newest_entry: Optional[str]
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class FieldAnalysis(BaseModel):
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"""Individual field analysis result."""
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status: str # PRESENT, PARTIALLY_PRESENT, ABSENT
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value: Optional[str]
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confidence: float
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reason_if_missing: Optional[str]
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suggestions: Optional[str]
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class PaperAnalysisResult(BaseModel):
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"""Complete paper analysis result."""
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pmid: str
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title: Optional[str]
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authors: Optional[List[str]]
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journal: Optional[str]
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publication_date: Optional[str]
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fields: Dict[str, FieldAnalysis]
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curation_summary: str
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analysis_timestamp: str
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processing_time: float
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model_used: str

app/api/routers/__init__.py

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"""
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API routers package for modular endpoint organization.
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"""

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