main.py

from fastapi import FastAPI, Request, Header, HTTPException, UploadFile, File
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 transformers import pipeline
from io import StringIO
import os, csv, logging
from model import summarize_review, smart_summarize
from typing import Optional

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=["*"],
)

@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/[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 open("app/static/index.html").read()

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

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

VALID_API_KEY = "my-secret-key"
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")

summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=1)
sentiment_pipelines = {
    "distilbert-base-uncased-finetuned-sst-2-english": pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english"),
    "nlptown/bert-base-multilingual-uncased-sentiment": pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
}

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

@app.post("/bulk/")
async def bulk(data: BulkReviewInput, x_api_key: str = Header(None)):
    if x_api_key != VALID_API_KEY:
        raise HTTPException(status_code=401, detail="Invalid or missing API key")

    sentiment_pipeline = sentiment_pipelines[data.model]
    summaries = summarizer(data.reviews, max_length=80, min_length=20, truncation=True)
    sentiments = sentiment_pipeline(data.reviews)
    emotions = emotion_model(data.reviews)

    results = []
    for i, review in enumerate(data.reviews):
        label = sentiments[i]["label"]
        if "star" in label:
            stars = int(label[0])
            label = "NEGATIVE" if stars <= 2 else "NEUTRAL" if stars == 3 else "POSITIVE"

        result = {
            "review": review,
            "summary": summaries[i]["summary_text"],
            "sentiment": label,
            "emotion": emotions[i][0]["label"],
            "aspects": [],
            "product_category": auto_fill(data.product_category[i]) if data.product_category else None,
            "device": auto_fill(data.device[i], "Web") if data.device else None,
            "industry": auto_fill(data.industry[i]) if data.industry else None,
        }
        results.append(result)

    return {"results": results}

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

    sentiment_pipeline = sentiment_pipelines.get(data.model)
    summary = smart_summarize(data.text) if request.query_params.get("smart") == "1" else summarize_review(data.text)
    sentiment = sentiment_pipeline(data.text)[0]
    label = sentiment["label"]
    if "star" in label:
        stars = int(label[0])
        label = "NEGATIVE" if stars <= 2 else "NEUTRAL" if stars == 3 else "POSITIVE"

    emotion = emotion_model(data.text)[0][0]["label"]

    aspects_list = []
    if data.aspects:
        for asp in ["battery", "price", "camera"]:
            if asp in data.text.lower():
                asp_result = sentiment_pipeline(asp + " " + data.text)[0]
                aspects_list.append({
                    "aspect": asp,
                    "sentiment": asp_result["label"],
                    "score": asp_result["score"]
                })

    follow_up_response = None
    if data.follow_up and data.intelligence:
        follow_up_response = f"[Mocked Detailed Answer in {data.verbosity} mode]"

    explanation = "This summary was generated using transformer-based sequence modeling and contextual keyword expansion." if data.explain else None

    return {
        "summary": summary,
        "sentiment": {"label": label, "score": sentiment["score"]},
        "emotion": emotion,
        "aspects": aspects_list,
        "follow_up": follow_up_response,
        "explanation": explanation,
        "product_category": auto_fill(data.product_category),
        "device": auto_fill(data.device, "Web"),
        "industry": auto_fill(data.industry)
    }

def custom_openapi():
    if app.openapi_schema:
        return app.openapi_schema
    openapi_schema = get_openapi(
        title=app.title,
        version=app.version,
        description="""
<b><span style='color:#4f46e5'>NeuroPulse AI</span></b> · Smart GenAI Feedback Engine<br>
Summarize reviews, detect sentiment/emotion, extract aspects, tag metadata, and ask GPT follow-ups.
""",
        routes=app.routes
    )
    openapi_schema["openapi"] = "3.0.0"
    app.openapi_schema = openapi_schema
    return app.openapi_schema

app.openapi = custom_openapi