Delete main.py

main.py

@@ -1,195 +0,0 @@
-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
-)
-
-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/[email protected]/swagger-ui-bundle.js",
-        swagger_css_url="https://cdn.jsdelivr.net/npm/[email protected]/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 = 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
-
-            response = {
-                "summary": summary,
-                "sentiment": sentiment,
-                "emotion": emotion,
-                "product_category": product_category,
-                "device": "Web",
-                "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("/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)
-
-            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")