from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.responses import JSONResponse
import tempfile
import os
from typing import List

# Import our services
from app.services.text_extractor import extract_text_from_pdf
from app.services.preprocessor import segment_into_clauses
from app.services.risk_analyzer import analyze_clause_with_gemini
from app.services.risk_scorer import calculate_scores, get_risk_definition

from app.schemas import AnalysisReport, AnalyzedClause, RiskFinding

# Create FastAPI app instance
app = FastAPI(
    title="Multilingual Legal Contract Analyzer",
    description="AI-powered contract analysis for English and Indic languages",
    version="1.0.0"
)


@app.post("/analyze/", response_model=AnalysisReport)
async def analyze_contract(file: UploadFile = File(...)):
    """
    Analyze a legal contract PDF and return detailed risk analysis.

    Args:
        file: PDF file to analyze

    Returns:
        AnalysisReport with risk analysis and suggestions
    """

    # Validate file type
    if not file.filename.lower().endswith('.pdf'):
        raise HTTPException(
            status_code=400, detail="Only PDF files are supported")

    # Create temporary file to store uploaded PDF
    with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as temp_file:
        try:
            # Write uploaded file to temporary file
            content = await file.read()
            temp_file.write(content)
            temp_file.flush()

            # Step 1: Extract text from PDF
            print(f"Extracting text from {file.filename}...")
            full_text = extract_text_from_pdf(temp_file.name)

            if not full_text or len(full_text.strip()) < 50:
                raise HTTPException(
                    status_code=400,
                    detail="Unable to extract meaningful text from PDF. Please ensure the PDF is readable."
                )

            # Step 2: Segment text into clauses
            print("Segmenting text into clauses...")
            clauses = segment_into_clauses(full_text)

            if not clauses:
                raise HTTPException(
                    status_code=400,
                    detail="Unable to identify contract clauses. Please ensure the document is a valid contract."
                )

            # Step 3: Analyze each clause with Gemini AI
            print(f"Analyzing {len(clauses)} clauses with AI...")
            analyzed_clauses = []

            for i, clause_text in enumerate(clauses, 1):
                print(f"Analyzing clause {i}/{len(clauses)}...")

                # Get AI analysis
                ai_result = analyze_clause_with_gemini(clause_text)

                # Convert AI results to RiskFinding objects
                risks = []
                for risk_data in ai_result.get("risks", []):
                    risk_id = risk_data.get("risk_id")
                    if risk_id:
                        risk_def = get_risk_definition(risk_id)
                        risk_finding = RiskFinding(
                            risk_id=risk_id,
                            description=risk_data.get(
                                "explanation", risk_def["description"]),
                            score=risk_def["score"]
                        )
                        risks.append(risk_finding)

                # Create AnalyzedClause object
                analyzed_clause = AnalyzedClause(
                    clause_number=i,
                    # Truncate for response
                    text=clause_text[:500] +
                    "..." if len(clause_text) > 500 else clause_text,
                    risks=risks,
                    suggestion=ai_result.get("suggestion")
                )
                analyzed_clauses.append(analyzed_clause)

            # Step 4: Calculate final risk score
            print("Calculating final risk score...")
            final_score, all_findings = calculate_scores(analyzed_clauses)

            # Step 5: Determine contract type and language (basic detection)
            contract_type = "General Contract"  # Could be enhanced with AI detection
            language = "English"  # Could be enhanced with language detection

            # Create final analysis report
            analysis_report = AnalysisReport(
                file_name=file.filename,
                language=language,
                contract_type=contract_type,
                final_risk_score=final_score,
                clauses=analyzed_clauses
            )

            print(f"Analysis complete. Final risk score: {final_score}")
            return analysis_report

        except HTTPException:
            raise
        except Exception as e:
            print(f"Error during analysis: {e}")
            raise HTTPException(
                status_code=500,
                detail=f"Analysis failed: {str(e)}"
            )
        finally:
            # Clean up temporary file
            try:
                os.unlink(temp_file.name)
            except:
                pass


@app.get("/")
async def root():
    """Health check endpoint"""
    return {"message": "Multilingual Legal Contract Analyzer API is running"}


@app.get("/health")
async def health_check():
    """Health check endpoint"""
    return {"status": "healthy", "service": "contract-analyzer"}


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