app.py

"""
FastAPI REST API for Foundation 1.2 Clinical Trial System
Production-ready Docker space with proper REST endpoints
"""

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import time
import logging

# Import the foundation engine
import foundation_engine

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(
    title="Clinical Trial API",
    description="Production REST API for clinical trial analysis powered by Foundation 1.2 pipeline",
    version="1.0.0",
    docs_url="/docs",
    redoc_url="/redoc"
)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Request/Response models
class QueryRequest(BaseModel):
    query: str

class QueryResponse(BaseModel):
    summary: str
    processing_time: float

class SearchRequest(BaseModel):
    query: str
    top_k: int = 10

class HealthResponse(BaseModel):
    status: str
    trials_loaded: int
    embeddings_loaded: bool

@app.on_event("startup")
async def startup_event():
    """Initialize the foundation engine on startup"""
    logger.info("=== API Startup ===")
    logger.info("Loading Foundation 1.2 engine...")
    try:
        # The foundation_engine will load embeddings when first accessed
        foundation_engine.load_embeddings()
        logger.info("=== API Ready - Embeddings Loaded ===")
    except Exception as e:
        logger.error(f"!!! Failed to load embeddings: {e}")
        logger.error("!!! API will start but queries will fail until embeddings are loaded")
        logger.info("=== API Ready - Degraded Mode ===")

@app.get("/")
async def root():
    """API information"""
    return {
        "service": "Clinical Trial API",
        "version": "2.0.0",
        "description": "Production REST API for Foundation 1.2 with 355M perplexity ranking",
        "status": "healthy",
        "endpoints": {
            "POST /search": "[NEW] Search trials with structured JSON output (includes 355M ranking)",
            "POST /query": "Query clinical trials and get AI-generated summary (legacy)",
            "GET /health": "Health check",
            "GET /docs": "Interactive API documentation (Swagger UI)",
            "GET /redoc": "Alternative API documentation (ReDoc)"
        },
        "features": [
            "LLM Query Parser (entity extraction + synonyms)",
            "Hybrid RAG Search (BM25 + semantic + inverted index)",
            "355M Clinical Trial GPT perplexity-based ranking",
            "Structured JSON output",
            "Benchmarking metrics (before/after 355M scores)"
        ]
    }

@app.get("/health", response_model=HealthResponse)
async def health_check():
    """Health check endpoint"""
    embeddings_loaded = foundation_engine.doc_embeddings is not None
    chunks_loaded = len(foundation_engine.doc_chunks) if foundation_engine.doc_chunks else 0

    return HealthResponse(
        status="healthy",
        trials_loaded=chunks_loaded,
        embeddings_loaded=embeddings_loaded
    )

@app.post("/query", response_model=QueryResponse)
async def query_trials(request: QueryRequest):
    """
    Query clinical trials and get AI-generated summary

    - **query**: Your question about clinical trials (e.g., "What trials exist for Dekavil?")

    Returns a structured medical analysis with:
    - Drug/Intervention background
    - Clinical trial results and data
    - Treatment considerations
    - NCT trial IDs and references
    """
    try:
        logger.info(f"API Query received: {request.query[:100]}...")
        start_time = time.time()

        # Call the foundation engine
        result = foundation_engine.process_query(request.query)

        processing_time = time.time() - start_time
        logger.info(f"Query completed in {processing_time:.2f}s")

        return QueryResponse(
            summary=result,
            processing_time=processing_time
        )

    except Exception as e:
        logger.error(f"Error processing query: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error processing query: {str(e)}")

@app.post("/search")
async def search_trials(request: SearchRequest):
    """
    Search clinical trials and get structured JSON results (NEW API v2.0)

    This endpoint provides:
    - Query parsing with LLM (entity extraction + synonym expansion)
    - Hybrid RAG search (BM25 + semantic embeddings + inverted index)
    - 355M Clinical Trial GPT perplexity-based re-ranking
    - Structured JSON output with benchmarking data

    **No response generation** - returns raw trial data for client-side processing

    Args:
    - **query**: Your question about clinical trials
    - **top_k**: Number of trials to return (default: 10, max: 50)

    Returns:
    - Structured JSON with trials ranked by clinical relevance
    - Includes before/after 355M ranking scores for benchmarking
    - Processing time breakdown (query parsing, RAG search, 355M ranking)
    """
    try:
        logger.info(f"[SEARCH API] Query received: {request.query[:100]}...")

        # Validate top_k
        if request.top_k > 50:
            logger.warning(f"[SEARCH API] top_k={request.top_k} exceeds maximum 50, capping")
            request.top_k = 50
        elif request.top_k < 1:
            logger.warning(f"[SEARCH API] top_k={request.top_k} is invalid, using default 10")
            request.top_k = 10

        start_time = time.time()

        # Call the structured query processor
        result = foundation_engine.process_query_structured(request.query, top_k=request.top_k)

        processing_time = time.time() - start_time
        logger.info(f"[SEARCH API] Query completed in {processing_time:.2f}s")

        # Ensure processing_time is set
        if 'processing_time' not in result or result['processing_time'] == 0:
            result['processing_time'] = processing_time

        return result

    except Exception as e:
        logger.error(f"[SEARCH API] Error processing query: {str(e)}")
        import traceback
        return {
            "error": str(e),
            "traceback": traceback.format_exc(),
            "query": request.query,
            "processing_time": time.time() - start_time if 'start_time' in locals() else 0
        }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)