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 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"

@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)
            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.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 []


            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")