# main.py - Final Fixed Version
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Optional
import re
import logging

from app.services.reviewer_service import AIReviewerService
from app.predictor import classifier, guide_generator

# 1. Setup Logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# 2. Initialize FastAPI and Services
app = FastAPI(title="GitGud AI Service")
reviewer_service = AIReviewerService()

# 3. Data Models (Order matters: ReviewRequest needs FileRequest)
class FileRequest(BaseModel):
    fileName: str
    content: Optional[str] = None

class ReviewRequest(BaseModel):
    files: List[FileRequest]

class GuideRequest(BaseModel):
    repoName: str
    filePaths: List[str]

# 4. Endpoints

@app.get("/")
def health_check():
    """Checks server status and GPU availability."""
    return {
        "status": "online",
        "model": "microsoft/codebert-base",
        "device": classifier.device,
    }

@app.post("/classify")
async def classify_file(request: FileRequest):
    """Classifies file into architectural layers."""
    try:
        result = classifier.predict(request.fileName, request.content)
        return {
            "fileName": request.fileName, 
            "layer": result["label"],
            "confidence": result["confidence"],
            "embedding": result["embedding"]
        }
    except Exception as e:
        logger.error(f"Classify failed: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/generate-guide")
async def generate_guide(request: GuideRequest):
    """Generates markdown guides for repositories."""
    try:
        markdown = guide_generator.generate_markdown(request.repoName, request.filePaths)
        return {"markdown": markdown}
    except Exception as e:
        logger.error(f"Guide generation failed: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/review")
async def review_code(request: ReviewRequest):
    """Detects security and logic issues in batches of files."""
    try:
        # Call the batch review logic from your service
        results = reviewer_service.review_batch_code(request.files)
        return {"reviews": results}
    except Exception as e:
        logger.error(f"Review endpoint failed: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/repo-dashboard-stats")
async def get_dashboard_stats(request: ReviewRequest):
    try:
        raw_reviews = reviewer_service.review_batch_code(request.files)
        
        # 1. Security Count
        total_vulns = sum(len(r.get("vulnerabilities", [])) for r in raw_reviews)
        
        # 2. Performance Ratio (Maintainability)
        # We use a default of 8 if the AI misses a file to avoid 0% scores
        scores = [r.get("metrics", {}).get("maintainability", 8) for r in raw_reviews]
        avg_maintainability = (sum(scores) / len(scores)) * 10 if scores else 0
        
        # 3. API Sniffing
        found_apis = []
        for f in request.files:
            if f.content:
                # Regex looks for common route decorators or methods
                matches = re.findall(r'(?:get|post|put|delete|patch)\([\'"]\/(.*?)[\'"]', f.content.lower())
                for match in matches:
                    found_apis.append(f"/{match}")

        # 4. Repo Health Calculation
        # Every security issue drops health by 10 points
        health_score = max(10, 100 - (total_vulns * 10))

        return {
            "repo_health": health_score,
            "health_label": "Excellent Health" if health_score > 80 else "Needs Review",
            "security_issues": total_vulns,
            "performance_ratio": f"{int(avg_maintainability)}%",
            "exposed_apis": list(set(found_apis))[:10]
        }
    except Exception as e:
        logger.error(f"Dashboard stats failed: {e}")
        raise HTTPException(status_code=500, detail="Failed to aggregate repository stats")

# 5. Application Entry Point
if __name__ == "__main__":
    import uvicorn
    # Port 7860 is mandatory for Hugging Face Spaces
    uvicorn.run(app, host="0.0.0.0", port=7860)