from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from typing import List, Optional
from app.services.llm import llm_service
from app.services.vector import vector_service
from app.services.search import search_service
from app.services.intent import IntentService
from app.services.files import file_service
from fastapi import UploadFile, File

# Initialize Intent Service
intent_service = IntentService(llm_service)

router = APIRouter()

# --- Pydantic Models ---
class QueryRequest(BaseModel):
    query: str

class Source(BaseModel):
    title: str
    url: str
    snippet: str

class ChallengeRequest(BaseModel):
    original_query: str
    original_answer: str
    sources_text: str

class QueryResponse(BaseModel):
    answer: str
    sources: List[Source]
    confidence: str
    search_queries: List[str]
    intent: Optional[str] = None
    thought_process: Optional[str] = None

# --- Endpoints ---

@router.post("/query", response_model=QueryResponse)
async def process_query(request: QueryRequest):
    """
    Main orchestration endpoint for the Trust-First Copilot.
    """
    user_query = request.query
    print(f"Refining query: {user_query}")

    try:
        # --- PHASE 1: INTENT & RISK ANALYSIS ---
        print("🧠 Analyzing Intent...")
        try:
            intent = await intent_service.analyze(user_query)
            print(f"   Category: {intent.category}")
            print(f"   Reasoning: {intent.reasoning}")
            print(f"   Risk: {intent.risk_level}")
        except Exception as e:
            print(f"Intent Error: {e}")
            from app.services.intent import IntentResponse
            intent = IntentResponse(category="SEARCH_REQUIRED", reasoning="Error", risk_level="LOW")

        # Risk Guard
        if intent.risk_level == "HIGH":
            return QueryResponse(
                answer="I cannot fulfill this request as it has been flagged as high risk/safety violation.",
                sources=[],
                confidence="Blocked",
                search_queries=[],
                intent="High Risk",
                thought_process=f"Blocked by Risk Analyzer. Reasoning: {intent.reasoning}"
            )

        # --- PHASE 2: EXECUTION ---
        search_results = []
        
        # Branch 1: Needs Search
        if intent.category == "SEARCH_REQUIRED" or intent.category == "DATA_ANALYSIS":
             print("🔍 Initiating Web Search...")
             search_results = await search_service.search(user_query)
             if not search_results:
                 # Fallback if search finds nothing but intent was search
                 pass

        # Branch 2: Coding (Skip Search usually, unless specific docs needed)
        elif intent.category == "CODING_TASK":
            print("💻 Coding Task - Focused Generation")
            # Potential future improvement: Search for docs if needed
        
        # Branch 3: Chat / General
        else:
            print("💬 Chat Mode - Direct Generation")

        # --- PHASE 3: CONTEXT & RAG ---
        context_text = ""
        final_sources = []
        
        if search_results:
            # RAG Logic
            print("Indexing search results in Vector DB...")
            vector_service.create_index_from_results(search_results)
            
            print("Searching Vector DB for relevant context...")
            relevant_chunks = vector_service.search_similar(user_query, k=5)
            final_sources = relevant_chunks if relevant_chunks else search_results
            
            context_text = "\n\n".join([
                f"Source {i+1}:\nTitle: {r.get('title')}\nURL: {r.get('url')}\nContent: {r.get('content')}"
                for i, r in enumerate(final_sources)
            ])
        else:
            context_text = "No external sources used. Answering from internal knowledge."

        # --- PHASE 4: SYNTHESIS ---
        # Modify prompt based on intent? For now, standard synthesis but context aware.
        answer = await llm_service.synthesize_answer(user_query, context_text)

        # --- PHASE 5: VERIFICATION ---
        confidence_level = "Medium"
        if intent.category == "SEARCH_REQUIRED":
             confidence_assessment = await llm_service.verify_confidence(answer, context_text)
             if "High confidence" in confidence_assessment: confidence_level = "High"
             elif "Low confidence" in confidence_assessment: confidence_level = "Low"
        else:
            confidence_level = "N/A (Chat)"

        # Construct Response
        formatted_sources = [
            Source(title=r.get('title', 'Unknown'), url=r.get('url', '#'), snippet=r.get('content', '')[:200])
            for r in (search_results if search_results else [])
        ]

        return QueryResponse(
            answer=answer,
            sources=formatted_sources,
            confidence=confidence_level,
            search_queries=[user_query],
            intent=intent.category,
            thought_process=f"Intent: {intent.category}. Reasoning: {intent.reasoning}"
        )

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

@router.post("/challenge", response_model=QueryResponse)
async def challenge_answer(request: ChallengeRequest):
    """
    'Disagree-with-Me' Mode: Critiques the previous answer.
    """
    try:
        from app.core import prompts
        # Construct the critique prompt
        messages = [
            {"role": "system", "content": prompts.MASTER_PROMPT_CHALLENGE},
            {"role": "user", "content": f"Query: {request.original_query}\n\nAnswer to critique: {request.original_answer}\n\nSources used:\n{request.sources_text}"}
        ]
        
        critique = await llm_service._generate(messages, temperature=0.7)
        
        # Return as a new message, but marked as a critique
        return QueryResponse(
            answer=critique,
            sources=[],
            confidence="High (Critique)",
            search_queries=[],
            intent="CRITIQUE",
            thought_process="Devil's Advocate Mode Activated."
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/upload")
async def upload_file(file: UploadFile = File(...)):
    """
    Parses an uploaded file and returns its text content for RAG.
    """
    try:
        filename = file.filename
        print(f"📂 Processing file: {filename}")
        content = await file_service.process_file(file)
        return {"filename": filename, "content": content}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Upload failed: {str(e)}")