Update main.py

main.py

@@ -5,6 +5,9 @@ from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from datetime import datetime
 import uuid
+import os
+import openai
+openai.api_key = os.environ.get("OPENAI_API_KEY")
 from transformers import pipeline
 import logging, traceback
 from typing import Optional, List, Union
@@ -16,7 +19,7 @@ from model import (
 )
 
 app = FastAPI(
-    title="🧠 NeuroPulse AI",
+    title="🧠 ChurnSight AI",
     description="Multilingual GenAI for smarter feedback — summarization, sentiment, emotion, aspects, Q&A and tags.",
     version="2025.1.0",
     openapi_url="/openapi.json",
@@ -38,13 +41,13 @@ log_store = []  # ✅ Shared in-memory churn log
 
 @app.get("/", response_class=HTMLResponse)
 def root():
-    return "<h1>NeuroPulse AI Backend is Running</h1>"
+    return "<h1>ChurnSight 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",
+        title="🧠 Swagger UI - ChurnSight 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",
@@ -188,6 +191,9 @@ async def bulk_analyze(data: BulkReviewInput, token: str = Query(None)):
         sentiment_pipeline = pipeline("sentiment-analysis", model=data.model)
 
         for i, review_text in enumerate(data.reviews):
+            if not review_text.strip():
+                continue  # Skip empty reviews
+
             if len(review_text.split()) < 20:
                 results.append({
                     "review": review_text,
@@ -195,6 +201,7 @@ async def bulk_analyze(data: BulkReviewInput, token: str = Query(None)):
                 })
                 continue
 
+
             summary = smart_summarize(review_text, n_clusters=2 if data.intelligence else 1)
             sentiment = sentiment_pipeline(review_text)[0]
 
@@ -246,8 +253,7 @@ async def bulk_analyze(data: BulkReviewInput, token: str = Query(None)):
         logging.error(f"🔥 Bulk processing failed: {traceback.format_exc()}")
         raise HTTPException(status_code=500, detail="Failed to analyze bulk reviews")
 
-from openai import OpenAI
-openai_client = OpenAI()
+# Already set with os.environ — nothing else needed
 
 @app.post("/rootcause/")
 async def root_cause_analysis(payload: dict, x_api_key: str = Header(None)):
@@ -272,7 +278,7 @@ async def root_cause_analysis(payload: dict, x_api_key: str = Header(None)):
         Suggestion: ...
         """
 
-        response = openai_client.chat.completions.create(
+        response = openai.ChatCompletion.create(
             model="gpt-4",
             messages=[{"role": "user", "content": prompt}]
         )
@@ -292,6 +298,6 @@ async def root_cause_analysis(payload: dict, x_api_key: str = Header(None)):
             "suggestion": extract_line("Suggestion")
         }
 
-    except Exception as e:
-        logging.error(f"Root cause analysis failed: {traceback.format_exc()}")
-        raise HTTPException(status_code=500, detail="Root cause generation failed.")
+        except Exception as e:
+            logging.error(f"Root cause analysis failed: {traceback.format_exc()}")
+            return JSONResponse(status_code=500, content={"detail": f"Root cause generation failed: {str(e)}"})