from fastapi import FastAPI, Request, Header, HTTPException, Query
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.openapi.docs import get_swagger_ui_html
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from datetime import datetime
import uuid
from transformers import pipeline
import logging, traceback
from typing import Optional, List, Union

from model import (
    summarize_review, smart_summarize, detect_industry,
    detect_product_category, detect_emotion, answer_followup, answer_only,
    assess_churn_risk  # ✅ Add this
)

app = FastAPI(
    title="🧠 NeuroPulse AI",
    description="Multilingual GenAI for smarter feedback — summarization, sentiment, emotion, aspects, Q&A and tags.",
    version="2025.1.0",
    openapi_url="/openapi.json",
    docs_url=None,
    redoc_url="/redoc"
)

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

logging.basicConfig(level=logging.INFO)
VALID_API_KEY = "my-secret-key"
log_store = []

@app.get("/", response_class=HTMLResponse)
def root():
    return "<h1>NeuroPulse AI Backend is Running</h1>"

@app.get("/docs", include_in_schema=False)
def custom_swagger_ui():
    return get_swagger_ui_html(
        openapi_url=app.openapi_url,
        title="🧠 Swagger UI - NeuroPulse AI",
        swagger_favicon_url="https://cdn-icons-png.flaticon.com/512/3794/3794616.png",
        swagger_js_url="https://cdn.jsdelivr.net/npm/swagger-ui-dist@4.18.3/swagger-ui-bundle.js",
        swagger_css_url="https://cdn.jsdelivr.net/npm/swagger-ui-dist@4.18.3/swagger-ui.css",
    )

@app.exception_handler(Exception)
async def exception_handler(request: Request, exc: Exception):
    logging.error(f"Unhandled Exception: {traceback.format_exc()}")
    return JSONResponse(status_code=500, content={"detail": "Internal Server Error. Please contact support."})

# ==== SCHEMAS ====

class ReviewInput(BaseModel):
    text: str
    model: str = "distilbert-base-uncased-finetuned-sst-2-english"
    industry: Optional[str] = None
    aspects: bool = False
    follow_up: Optional[Union[str, List[str]]] = None
    product_category: Optional[str] = None
    device: Optional[str] = None
    intelligence: Optional[bool] = False
    verbosity: Optional[str] = "detailed"

class BulkReviewInput(BaseModel):
    reviews: List[str]
    model: str = "distilbert-base-uncased-finetuned-sst-2-english"
    industry: Optional[List[str]] = None
    aspects: bool = False
    product_category: Optional[List[str]] = None
    device: Optional[List[str]] = None
    follow_up: Optional[List[Union[str, List[str]]]] = None
    intelligence: Optional[bool] = False

class FollowUpRequest(BaseModel):
    text: str
    question: str
    verbosity: Optional[str] = "brief"

# ==== HELPERS ====

def auto_fill(value: Optional[str], fallback: str) -> str:
    if not value or value.lower() == "auto-detect":
        return fallback
    return value

# ==== ENDPOINTS ====

@app.post("/analyze/")
async def analyze(data: ReviewInput, x_api_key: str = Header(None)):
    if x_api_key and x_api_key != VALID_API_KEY:
        raise HTTPException(status_code=401, detail="❌ Invalid API key")

    if len(data.text.split()) < 20:
        raise HTTPException(status_code=400, detail="⚠️ Review too short for analysis (min. 20 words).")

    try:
        response = {}

        if not data.follow_up:
            summary = (
                summarize_review(data.text, max_len=40, min_len=8)
                if data.verbosity.lower() == "brief"
                else smart_summarize(data.text, n_clusters=2 if data.intelligence else 1)
            )

            sentiment_pipeline = pipeline("sentiment-analysis", model=data.model)
            sentiment = sentiment_pipeline(data.text)[0]
            emotion_raw = detect_emotion(data.text)
            emotion = emotion_raw["label"] if isinstance(emotion_raw, dict) and "label" in emotion_raw else str(emotion_raw)
            churn_risk = assess_churn_risk(sentiment["label"], emotion)
            
            # Log churn risk analysis
            log_entry = {
                "timestamp": datetime.now(),
                "product": data.product_category or "Generic",
                "churn_risk": churn_risk,
                "user_id": str(uuid.uuid4())  # Optional
            }
            log_store.append(log_entry)
            if len(log_store) > 1000:
                log_store = log_store[-1000:]  # keep latest 1000 entries

            pain_points = []
            if data.aspects:
                from model import extract_pain_points  # 🔍 Import inline if not already
                pain_points = extract_pain_points(data.text)

            industry = detect_industry(data.text) if not data.industry or "auto" in data.industry.lower() else data.industry
            product_category = detect_product_category(data.text) if not data.product_category or "auto" in data.product_category.lower() else data.product_category

            response = {
                "summary": summary,
                "sentiment": sentiment,
                "emotion": emotion,
                "product_category": product_category,
                "device": "Web",
                "industry": industry,
                "churn_risk": churn_risk,
                "pain_points": pain_points

            }

        if data.follow_up:
            response["follow_up"] = answer_followup(data.text, data.follow_up, verbosity=data.verbosity)

        return response

    except Exception as e:
        logging.error(f"🔥 Unexpected analysis failure: {traceback.format_exc()}")
        raise HTTPException(status_code=500, detail="Internal Server Error during analysis. Please contact support.")

@app.post("/followup/")
async def followup(request: FollowUpRequest, x_api_key: str = Header(None)):
    if x_api_key and x_api_key != VALID_API_KEY:
        raise HTTPException(status_code=401, detail="Invalid API key")

    if not request.question or len(request.text.split()) < 10:
        raise HTTPException(status_code=400, detail="Question or text is too short.")

    try:
        answer = answer_only(request.text, request.question)
        return {"answer": answer}
    except Exception as e:
        logging.error(f"❌ Follow-up failed: {traceback.format_exc()}")
        raise HTTPException(status_code=500, detail="Internal Server Error during follow-up.")

@app.get("/log/")
async def get_churn_log(x_api_key: str = Header(None)):
    if x_api_key and x_api_key != VALID_API_KEY:
        raise HTTPException(status_code=401, detail="Unauthorized")
    return {"log": log_store}


@app.post("/bulk/")
async def bulk_analyze(data: BulkReviewInput, token: str = Query(None)):
    if token != VALID_API_KEY:
        raise HTTPException(status_code=401, detail="❌ Unauthorized: Invalid API token")

    try:
        results = []
        sentiment_pipeline = pipeline("sentiment-analysis", model=data.model)

        for i, review_text in enumerate(data.reviews):
            if len(review_text.split()) < 20:
                results.append({
                    "review": review_text,
                    "error": "Too short to analyze"
                })
                continue

            summary = smart_summarize(review_text, n_clusters=2 if data.intelligence else 1)
            sentiment = sentiment_pipeline(review_text)[0]
            emotion = detect_emotion(review_text)
            
            churn = assess_churn_risk(sentiment["label"], emotion)
            pain = extract_pain_points(review_text) if data.aspects else []

            # 🔁 Log churn data
            log_entry = {
                "timestamp": datetime.now(),
                "product": prod,
                "churn_risk": churn,
                "user_id": str(uuid.uuid4())
            }
            log_store.append(log_entry)
            if len(log_store) > 1000:
                log_store = log_store[-1000:]


            ind = auto_fill(data.industry[i] if data.industry else None, detect_industry(review_text))
            prod = auto_fill(data.product_category[i] if data.product_category else None, detect_product_category(review_text))
            dev = auto_fill(data.device[i] if data.device else None, "Web")

            result = {
                "review": review_text,
                "summary": summary,
                "sentiment": sentiment["label"],
                "score": sentiment["score"],
                "emotion": emotion,
                "industry": ind,
                "product_category": prod,
                "device": dev,
                "churn_risk": churn,
                "pain_points": pain
            }

            if data.follow_up and i < len(data.follow_up):
                follow_q = data.follow_up[i]
                result["follow_up"] = answer_followup(review_text, follow_q)

            results.append(result)

        return {"results": results}

    except Exception as e:
        logging.error(f"🔥 Bulk processing failed: {traceback.format_exc()}")
        raise HTTPException(status_code=500, detail="Failed to analyze bulk reviews")