Update app/sentiment.py

app/sentiment.py

@@ -1,28 +1,49 @@
 """
-Tiny wrapper around HF transformers sentiment pipeline with in‑memory cache.
+CryptoSentinel AI
+Hugging Face Transformers-based sentiment analysis
+✅ Fixed for Hugging Face Spaces: avoids /.cache write errors
 """
+
 from transformers import pipeline
 from functools import lru_cache
-import hashlib, logging
+import hashlib
+import logging
+import os
+
+# 🚨 Hugging Face Spaces cache workaround
+# Set model cache path to a writable directory
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
+os.makedirs("/tmp/huggingface", exist_ok=True)
 
-_sentiment = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+# Load sentiment analysis pipeline
+_sentiment = pipeline(
+    "sentiment-analysis",
+    model="distilbert-base-uncased-finetuned-sst-2-english"
+)
 
 class SentimentCache:
+    """Stores and deduplicates sentiment results in memory."""
     latest_id: int = 0
     latest_result: dict = {}
 
     @classmethod
     def _hash(cls, text: str) -> str:
+        """Hash text to deduplicate analysis."""
         return hashlib.sha256(text.encode()).hexdigest()
 
     @classmethod
     @lru_cache(maxsize=128)
     def _analyze(cls, text: str):
+        """Analyze and cache sentiment."""
         return _sentiment(text)[0]
 
     @classmethod
     def compute(cls, text: str):
+        """Run analysis and update latest result."""
         res = cls._analyze(text)
         cls.latest_id += 1
-        cls.latest_result = {"text": text, **res}
-        logging.info("🧠 sentiment computed")
+        cls.latest_result = {
+            "text": text,
+            **res
+        }
+        logging.info("🧠 Sentiment computed: %s", res)