Update main.py

main.py

@@ -5,7 +5,7 @@ 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
+import logging, traceback
 from model import summarize_review, smart_summarize
 from typing import Optional, List
 
@@ -52,7 +52,6 @@ class ReviewInput(BaseModel):
     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
@@ -63,15 +62,14 @@ class BulkReviewInput(BaseModel):
     industry: Optional[List[str]] = None
     aspects: bool = False
     product_category: Optional[List[str]] = None
-    device: Optional[List[str]] = None
+    intelligence: Optional[bool] = False
 
 VALID_API_KEY = "my-secret-key"
 logging.basicConfig(level=logging.INFO)
-
 sentiment_pipeline = pipeline("sentiment-analysis")
 
 def auto_fill(value: Optional[str], default: str = "Generic") -> str:
-    if not value or value.lower() == "auto-detect":
+    if not value or value.strip().lower() == "auto-detect":
         return default
     return value
 
@@ -84,32 +82,20 @@ async def analyze(data: ReviewInput, x_api_key: str = Header(None)):
         raise HTTPException(status_code=400, detail="⚠️ Review too short for analysis (min. 10 words).")
 
     try:
-        # Summary Generation
-        try:
-            summary = smart_summarize(data.text) if data.intelligence else summarize_review(data.text)
-        except Exception as e:
-            logging.error(f"🔍 Summarization error: {traceback.format_exc()}")
-            raise HTTPException(status_code=500, detail="🧠 Failed to generate summary. Please try again.")
-
-        # Sentiment Analysis
-        try:
-            sentiment = sentiment_pipeline(data.text)[0]
-        except Exception as e:
-            logging.error(f"📊 Sentiment analysis error: {traceback.format_exc()}")
-            raise HTTPException(status_code=500, detail="📉 Sentiment analysis failed. Please retry.")
+        summary = smart_summarize(data.text) if data.intelligence else summarize_review(data.text)
+        if data.verbosity.lower() == "brief":
+            summary = summary.split(".")[0].strip() + "."
 
-        # (Optional future: plug in emotion model)
-        emotion = "joy"  # hardcoded placeholder
+        sentiment = sentiment_pipeline(data.text)[0]
 
         return {
             "summary": summary,
             "sentiment": sentiment,
-            "emotion": emotion,
-            "product_category": auto_fill(data.product_category),
-            "device": auto_fill(data.device, "Web"),
-            "industry": auto_fill(data.industry)
+            "emotion": "joy",  # placeholder
+            "product_category": auto_fill(data.product_category, "General"),
+            "industry": auto_fill(data.industry, "Generic")
         }
 
-    except Exception as e:
+    except Exception:
         logging.error(f"🔥 Unexpected analysis failure: {traceback.format_exc()}")
         raise HTTPException(status_code=500, detail="Internal Server Error during analysis. Please contact support.")