api/router_scam.py

import tempfile
import os
from fastapi import APIRouter, UploadFile, File, Depends, HTTPException, status
from core.security import verify_api_key
from ml_services.audio_ai import audio_service
from ml_services.nlp_graph_ai import nlp_graph_service
from models.schemas import IncidentReport
from datetime import datetime
import uuid
from core.db import db_handler

router = APIRouter(prefix="/api/v1/scam", tags=["Scam Detection"])

@router.post("/analyze", response_model=IncidentReport)
async def analyze_scam_audio(
    file: UploadFile = File(...), 
    api_key: str = Depends(verify_api_key)
):
    if not file.filename.endswith(('.wav', '.mp3', '.ogg', '.flac')):
        raise HTTPException(status_code=400, detail="Unsupported audio format.")

    temp_file_path = ""
    try:
        # Save upload to a temporary file asynchronously
        fd, temp_file_path = tempfile.mkstemp(suffix=f".{file.filename.split('.')[-1]}")
        os.close(fd) # Close the synchronous fd immediately
        
        import aiofiles
        async with aiofiles.open(temp_file_path, 'wb') as f:
            while chunk := await file.read(1024 * 1024): # 1MB chunks
                await f.write(chunk)
        
        # 1. Process Audio (STT + Deepfake check)
        transcription, fake_prob = await audio_service.process_audio(temp_file_path)
        
        # 2. Translate to English (assuming Hindi or other regional language as source for robust pipeline, 
        # but Whisper might output English directly if translated, or source language.
        # Let's assume whisper gives text, and we pass to translator if we want to ensure English,
        # but for now we'll pass to intent directly to save overhead unless explicitly requested).
        # We'll use the LLM to classify intent directly as mistral handles multilingual.
        intent_and_risk = await nlp_graph_service.classify_intent(transcription)
        
        # 3. Extract Entities
        entities = await nlp_graph_service.extract_entities(transcription)

        # 4. Agentic AI Fusion (Threat Dossier)
        from ml_services.agentic_fusion import agent
        dossier = await agent.generate_threat_dossier(transcription, entities)
        
        # 5. DPDP Act Compliance (PII Redaction)
        redacted_transcription = nlp_graph_service.redact_pii(transcription, entities)

        # 6. Construct Report
        report = IncidentReport(
            incident_id=str(uuid.uuid4()),
            type="audio",
            timestamp=datetime.utcnow(),
            extracted_entities=entities,
            risk_score=fake_prob if fake_prob > 0.5 else (0.8 if intent_and_risk.get("risk") == "High" else 0.2), 
            threat_dossier=dossier,
            explainability_report=intent_and_risk.get("explainability_report"),
            details={
                "transcription": redacted_transcription,
                "intent": intent_and_risk.get("intent", ""),
                "scam_stage": intent_and_risk.get("scam_stage", ""),
                "deepfake_probability": fake_prob
            }
        )
        
        # 7. Generate Immutable Evidence Hash
        db_payload = report.model_dump()
        sig = await db_handler.generate_evidence_hash(db_payload, file_path=temp_file_path)
        report.digital_signature = sig
        db_payload["digital_signature"] = sig

        # 7. Save to MongoDB atomically
        if db_handler.db is not None:
            await db_handler.db["incidents"].insert_one(db_payload)

        return report

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Internal Server Error: {str(e)}")
    finally:
        # Cleanup temp file
        if temp_file_path and os.path.exists(temp_file_path):
            os.remove(temp_file_path)

from models.schemas import VideoAnalysisResponse
from ml_services.vision_ai import vision_service

@router.post("/video-analysis", response_model=VideoAnalysisResponse)
async def analyze_scam_video(
    file: UploadFile = File(...), 
    api_key: str = Depends(verify_api_key)
):
    if not file.filename.lower().endswith(('.png', '.jpg', '.jpeg', '.webp')):
        raise HTTPException(status_code=400, detail="Unsupported image format.")

    temp_file_path = ""
    try:
        fd, temp_file_path = tempfile.mkstemp(suffix=f".{file.filename.split('.')[-1]}")
        os.close(fd)
        
        import aiofiles
        async with aiofiles.open(temp_file_path, 'wb') as f:
            while chunk := await file.read(1024 * 1024):
                await f.write(chunk)
                
        result = await vision_service.analyze_video_call(temp_file_path)
        return VideoAnalysisResponse(**result)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    finally:
        if temp_file_path and os.path.exists(temp_file_path):
            os.remove(temp_file_path)