Update main.py

main.py

@@ -1,21 +1,19 @@
-from fastapi import FastAPI, Request, Header, HTTPException
+from fastapi import FastAPI, Request, Header, HTTPException, Query
 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 model import answer_only
-from fastapi import Query
 from transformers import pipeline
-import os, logging, traceback
+import logging, traceback
+from typing import Optional, List, Union
+
 from model import (
-    summarize_review, smart_summarize, detect_industry, 
-    detect_product_category, answer_followup, detect_emotion
+    summarize_review, smart_summarize, detect_industry,
+    detect_product_category, detect_emotion, answer_followup, answer_only
 )
-from typing import Optional, List, Union
 
 app = FastAPI(
-    title="\U0001f9e0 NeuroPulse AI",
+    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",
@@ -31,24 +29,29 @@ app.add_middleware(
     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."})
+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="\U0001f9e0 Swagger UI - NeuroPulse AI",
+        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 "<h1>NeuroPulse AI Backend is Running</h1>"
+@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
@@ -69,16 +72,22 @@ class BulkReviewInput(BaseModel):
     product_category: Optional[List[str]] = None
     device: Optional[List[str]] = None
     follow_up: Optional[List[Union[str, List[str]]]] = None
-    intelligence: Optional[bool] = False 
+    intelligence: Optional[bool] = False
 
-VALID_API_KEY = "my-secret-key"
-logging.basicConfig(level=logging.INFO)
+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:
@@ -103,14 +112,13 @@ async def analyze(data: ReviewInput, x_api_key: str = Header(None)):
 
             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,
+                "device": "Web",
                 "industry": industry
             }
 
@@ -123,6 +131,21 @@ async def analyze(data: ReviewInput, x_api_key: str = Header(None)):
         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:
@@ -170,24 +193,3 @@ async def bulk_analyze(data: BulkReviewInput, token: str = Query(None)):
     except Exception as e:
         logging.error(f"🔥 Bulk processing failed: {traceback.format_exc()}")
         raise HTTPException(status_code=500, detail="Failed to analyze bulk reviews")
-from model import answer_only
-
-class FollowUpRequest(BaseModel):
-    text: str
-    question: str
-    verbosity: Optional[str] = "brief"
-
-@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.")