sentiment

utils/sentiment_analysis.py

@@ -1,5 +1,7 @@
 import re, math, torch
 from transformers import pipeline
+import spaces
+
 
 # ------------- Model (CPU-friendly); use device=0 + fp16 on GPU -------------
 ZSHOT = pipeline(
@@ -213,6 +215,8 @@ def classify_and_explain(text: str, topic: str = "water and sanitation", per_axi
       "text_preview": text[:300] + ("..." if len(text) > 300 else "")
     }
 
+
 # Get the sentiment for all the docs
+@spaces.GPU(duration=120)
 def get_sentiment(texts):
     return [classify_and_explain(texts[i].page_content) for i in range(len(texts))]