Upload main.py

main.py

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+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.")