Update app.py

app.py

@@ -10,6 +10,7 @@ import io
 from PIL import Image
 from datetime import datetime
 from transformers import pipeline as hf_pipeline  # prevent name collision with gradio pipeline
+from functools import lru_cache  # Import lru_cache
 
 def get_emotion_profile(text):
     emotions = emotion_pipeline(text)
@@ -382,7 +383,6 @@ THREAT_MOTIFS = [
 ]
 
 
-@spaces.GPU
 @spaces.GPU
 def compute_abuse_score(matched_scores, sentiment):
     """
@@ -448,7 +448,7 @@ def compute_abuse_score(matched_scores, sentiment):
         base_score = max(base_score, 60.0)  # Reduced
 
     return min(round(base_score, 1), 100.0)
-
+@lru_cache(maxsize=1024)
 def analyze_single_message(text, thresholds):
     print("⚡ ENTERED analyze_single_message")
     stage = 1
@@ -459,9 +459,9 @@ def analyze_single_message(text, thresholds):
     sentiment_score = emotion_profile.get("anger", 0) + emotion_profile.get("disgust", 0)
 
     # Get model scores
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device) # Move to device
+        with torch.no_grad():
+            outputs = model(**inputs)
     scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
 
     # Sentiment override
@@ -475,21 +475,17 @@ def analyze_single_message(text, thresholds):
 
     weapon_flag = detect_weapon_language(text)
 
-    adjusted_thresholds = {
-        k: v + 0.05 if sentiment == "supportive" else v
-        for k, v in thresholds.items()
-    }
-
+    adjusted_thresholds = {k: v + 0.05 if sentiment == "supportive" else v for k, v in thresholds.items()}
     darvo_score = predict_darvo_score(text)
 
-    threshold_labels = [
-        label for label, score in zip(LABELS, scores)
-        if score > adjusted_thresholds[label]
-    ]
+    threshold_labels = [label for label, score in zip(LABELS, scores) if score > adjusted_thresholds[label]]
+
+    # Calculate matched scores *before* early exit
+    matched_scores = [(label, score, PATTERN_WEIGHTS.get(label, 1.0)) for label, score in zip(LABELS, scores) if score > adjusted_thresholds[label]]
 
-    # Early exit if nothing passed
+    # Early exit if nothing passed, but return tone_tag as None
     if not threshold_labels:
-        return 0.0, [], [], {"label": sentiment}, 1, 0.0, "supportive"
+        return 0.0, [], [], {"label": sentiment}, 1, 0.0, None  # Return None for tone_tag
 
     top_patterns = sorted(
         [(label, score) for label, score in zip(LABELS, scores)],
@@ -544,7 +540,7 @@ def analyze_single_message(text, thresholds):
             top_patterns = [("insults", insult_score)] + top_patterns
         if "insults" not in threshold_labels:
             threshold_labels.append("insults")
-
+    return abuse_score, threshold_labels, top_patterns, {"label": sentiment}, stage, darvo_score, tone_tag
     # Debug
     print(f"Emotional Tone Tag: {tone_tag}")
     print("Emotion Profile:")