Update app.py

app.py

@@ -66,10 +66,23 @@ model_name = "SamanthaStorm/tether-multilabel-v4"
 model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
 tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
 
-# sentiment model
-sentiment_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-sentiment").to(device)
-sentiment_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-sentiment", use_fast=False)
-sentiment_model.eval()
+# sentiment model - add no_cache=True and force_download=True
+sentiment_model = AutoModelForSequenceClassification.from_pretrained(
+    "SamanthaStorm/tether-sentiment", 
+    no_cache=True,
+    force_download=True
+).to(device)
+sentiment_tokenizer = AutoTokenizer.from_pretrained(
+    "SamanthaStorm/tether-sentiment", 
+    use_fast=False,
+    no_cache=True,
+    force_download=True
+)
+
+# After loading the sentiment model
+logger.debug(f"\nSentiment Model Config:")
+logger.debug(f"Model name: {sentiment_model.config.name_or_path}")
+logger.debug(f"Last modified: {sentiment_model.config._name_or_path}")
 
 
 emotion_pipeline = hf_pipeline(
@@ -556,13 +569,21 @@ def analyze_single_message(text, thresholds):
                 weight *= 1.5
             matched_scores.append((label, score, weight))
 
+        # In analyze_single_message, modify the sentiment section:
         # Get sentiment
         sent_inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
         sent_inputs = {k: v.to(device) for k, v in sent_inputs.items()}
         with torch.no_grad():
             sent_logits = sentiment_model(**sent_inputs).logits[0]
-        sent_probs = torch.softmax(sent_logits, dim=-1).cpu().numpy()
+            sent_probs = torch.softmax(sent_logits, dim=-1).cpu().numpy()
+    
+        # Add detailed logging
+        logger.debug("\n🎭 SENTIMENT ANALYSIS DETAILS")
+        logger.debug(f"Raw logits: {sent_logits}")
+        logger.debug(f"Probabilities: undermining={sent_probs[0]:.3f}, supportive={sent_probs[1]:.3f}")
+    
         sentiment = SENTIMENT_LABELS[int(np.argmax(sent_probs))]
+        logger.debug(f"Selected sentiment: {sentiment}")
 
         # Calculate abuse score
         abuse_score = compute_abuse_score(matched_scores, sentiment)