Delete main.py

main.py

@@ -1,115 +0,0 @@
-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 transformers import pipeline
-import os, logging, traceback
-from model import summarize_review, smart_summarize, detect_industry, detect_product_category, answer_followup
-from typing import Optional, List
-
-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[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)
-sentiment_pipeline = pipeline("sentiment-analysis")
-
-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 != VALID_API_KEY:
-        raise HTTPException(status_code=401, detail="❌ Unauthorized: Invalid API key")
-    if len(data.text.split()) < 20:
-        raise HTTPException(status_code=400, detail="⚠️ Review too short for analysis (min. 20 words).")
-
-    try:
-        # Smart summary logic based on verbosity and intelligence
-        if data.verbosity.lower() == "brief":
-            summary = summarize_review(data.text, max_len=40, min_len=8)
-        else:
-            summary = smart_summarize(data.text, n_clusters=2 if data.intelligence else 1)
-
-        sentiment = sentiment_pipeline(data.text)[0]
-        emotion = "joy" 
-
-        # Auto-detection logic
-        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"
-
-        follow_up_response = None
-        if data.follow_up:
-            follow_up_response = answer_followup(data.text, data.follow_up, verbosity=data.verbosity)
-
-        return {
-            "summary": summary,
-            "sentiment": sentiment,
-            "emotion": emotion,
-            "product_category": product_category,
-            "device": device,
-            "industry": industry,
-            "follow_up": follow_up_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.")