main.py

from fastapi import FastAPI, Request, Header, HTTPException
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.openapi.utils import get_openapi
from fastapi.openapi.docs import get_swagger_ui_html
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from fastapi import Query
from transformers import pipeline
import os, logging, traceback
from model import (
    summarize_review, smart_summarize, detect_industry, 
    detect_product_category, answer_followup, detect_emotion
)
from typing import Optional, List, Union

app = FastAPI(
    title="\U0001f9e0 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=["*"],
)

@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."})

@app.get("/docs", include_in_schema=False)
def custom_swagger_ui():
    return get_swagger_ui_html(
        openapi_url=app.openapi_url,
        title="\U0001f9e0 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/[email protected]/swagger-ui-bundle.js",
        swagger_css_url="https://cdn.jsdelivr.net/npm/[email protected]/swagger-ui.css",
    )

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

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 

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

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

@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 = detect_emotion(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
            device = "Web"

            response = {
                "summary": summary,
                "sentiment": sentiment,
                "emotion": emotion,
                "product_category": product_category,
                "device": device,
                "industry": industry
            }

        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("/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)

            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
            }

            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")