Update app/sentiment.py

app/sentiment.py

@@ -1,49 +1,47 @@
 """
-CryptoSentinel AI
-Hugging Face Transformers-based sentiment analysis
-✅ Fixed for Hugging Face Spaces: avoids /.cache write errors
+Sentiment analysis module using Hugging Face Transformers with cache redirection for HF Spaces.
 """
 
+import os
 from transformers import pipeline
 from functools import lru_cache
 import hashlib
 import logging
-import os
 
-# 🚨 Hugging Face Spaces cache workaround
-# Set model cache path to a writable directory
+# 🔧 Redirect HF cache to writable /tmp directory (important for Hugging Face Spaces)
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
 os.makedirs("/tmp/huggingface", exist_ok=True)
 
-# Load sentiment analysis pipeline
+# 🧠 Load sentiment model
 _sentiment = pipeline(
     "sentiment-analysis",
     model="distilbert-base-uncased-finetuned-sst-2-english"
 )
 
 class SentimentCache:
-    """Stores and deduplicates sentiment results in memory."""
+    """Handles in-memory caching and streaming of sentiment results."""
     latest_id: int = 0
     latest_result: dict = {}
 
     @classmethod
     def _hash(cls, text: str) -> str:
-        """Hash text to deduplicate analysis."""
+        """Hash input text to use as a cache key."""
         return hashlib.sha256(text.encode()).hexdigest()
 
     @classmethod
     @lru_cache(maxsize=128)
     def _analyze(cls, text: str):
-        """Analyze and cache sentiment."""
+        """Run inference on text, cached for performance."""
         return _sentiment(text)[0]
 
     @classmethod
     def compute(cls, text: str):
-        """Run analysis and update latest result."""
-        res = cls._analyze(text)
+        """Trigger inference and update latest result."""
+        result = cls._analyze(text)
         cls.latest_id += 1
         cls.latest_result = {
             "text": text,
-            **res
+            "label": result["label"],
+            "score": round(result["score"], 4)
         }
-        logging.info("🧠 Sentiment computed: %s", res)
+        logging.info("✅ Sentiment computed: %s", cls.latest_result)