Update app.py

app.py

@@ -7,6 +7,15 @@ import easyocr
 import numpy as np
 import pandas as pd
 
+mpqa = {}
+with open("subj_lexicon.tff") as f:
+    for line in f:
+        fields = dict(tok.split("=") for tok in line.strip().split())
+        word = fields["word1"]
+        strength = fields["type"]            # “strongsubj” vs “weaksubj”
+        polarity = fields["priorpolarity"]   # “positive” or “negative”
+        mpqa[word] = (strength, polarity)
+
 # ——— Load and preprocess NRC EmoLex ——————————————————————————————————
 # Make sure this filename matches exactly what you’ve uploaded
 EMOLEX_PATH = "NRC-Emotion-Lexicon-Wordlevel-v0.92.txt"
@@ -84,6 +93,7 @@ THRESHOLDS = {
 # ——— 3) Initialize EasyOCR reader ————————————————————————————————————————————
 ocr_reader = easyocr.Reader(["en"], gpu=False)
 
+
 # ——— 4) Emotional-Tone Tagging —————————————————————————————————————————————
 def get_emotional_tone_tag(emotion_profile, patterns, text_lower):
     """
@@ -105,117 +115,118 @@ def get_emotional_tone_tag(emotion_profile, patterns, text_lower):
         emo: sum(EMOLEX.get(w, {}).get(emo, 0) for w in words)
         for emo in ["anger","joy","sadness","fear","disgust"]
     }
+
     # 0. Support override
     if lex_counts["joy"] > 0 and any(k in text_lower for k in ["support","hope","grace"]):
         return "supportive"
 
-# 1. Performative Regret
+    # 1. Performative Regret
     if sadness > 0.4 \
        and (lex_counts["sadness"] > 0 or any(p in patterns for p in ["blame shifting","guilt tripping","recovery phase"])):
         return "performative regret"
 
-# 2. Coercive Warmth
+    # 2. Coercive Warmth
     if (joy > 0.3 or sadness > 0.4) \
-        and (lex_counts["joy"] > 0 or lex_counts["sadness"] > 0) \
-        and any(p in patterns for p in ["control","gaslighting"]):
-            return "coercive warmth"
+       and (lex_counts["joy"] > 0 or lex_counts["sadness"] > 0) \
+       and any(p in patterns for p in ["control","gaslighting"]):
+        return "coercive warmth"
 
-# 3. Cold Invalidation
+    # 3. Cold Invalidation
     if (neutral + disgust) > 0.5 \
        and lex_counts["disgust"] > 0 \
        and any(p in patterns for p in ["dismissiveness","projection","obscure language"]):
-            return "cold invalidation"
+        return "cold invalidation"
 
-# 4. Genuine Vulnerability
+    # 4. Genuine Vulnerability
     if (sadness + fear) > 0.5 \
        and lex_counts["sadness"] > 0 and lex_counts["fear"] > 0 \
        and all(p == "recovery phase" for p in patterns):
-            return "genuine vulnerability"
+        return "genuine vulnerability"
 
-# 5. Emotional Threat
+    # 5. Emotional Threat
     if (anger + disgust) > 0.5 \
        and (lex_counts["anger"] > 0 or lex_counts["disgust"] > 0) \
        and any(p in patterns for p in ["control","threat","insults","dismissiveness"]):
-            return "emotional threat"
+        return "emotional threat"
 
-# 6. Weaponized Sadness
+    # 6. Weaponized Sadness
     if sadness > 0.6 \
        and lex_counts["sadness"] > 0 \
        and any(p in patterns for p in ["guilt tripping","projection"]):
-            return "weaponized sadness"
+        return "weaponized sadness"
 
-# 7. Toxic Resignation
+    # 7. Toxic Resignation
     if neutral > 0.5 \
        and any(p in patterns for p in ["dismissiveness","obscure language"]) \
        and lex_counts["disgust"] == 0:
-            return "toxic resignation"
+        return "toxic resignation"
 
-# 8. Indignant Reproach
+    # 8. Indignant Reproach
     if anger > 0.5 \
        and lex_counts["anger"] > 0 \
        and any(p in patterns for p in ["guilt tripping","contradictory statements"]):
-            return "indignant reproach"
+        return "indignant reproach"
 
-# 9. Confrontational
+    # 9. Confrontational
     if anger > 0.6 \
-        and lex_counts["anger"] > 0 \
-        and patterns:
-            return "confrontational"
+       and lex_counts["anger"] > 0 \
+       and patterns:
+        return "confrontational"
 
-# 10. Passive Aggression
+    # 10. Passive Aggression
     if neutral > 0.6 \
        and lex_counts["disgust"] > 0 \
        and any(p in patterns for p in ["dismissiveness","projection"]):
-            return "passive aggression"
+        return "passive aggression"
 
-# 11. Sarcastic Mockery
+    # 11. Sarcastic Mockery
     if joy > 0.3 \
        and lex_counts["joy"] > 0 \
        and "insults" in patterns:
-            return "sarcastic mockery"
+        return "sarcastic mockery"
 
-# 12. Menacing Threat
+    # 12. Menacing Threat
     if fear > 0.3 \
        and lex_counts["fear"] > 0 \
        and "threat" in patterns:
-            return "menacing threat"
+        return "menacing threat"
 
-# 13. Pleading Concern
+    # 13. Pleading Concern
     if sadness > 0.3 \
        and lex_counts["sadness"] > 0 \
        and any(k in text_lower for k in APOLOGY_KEYWORDS) \
        and not patterns:
-            return "pleading concern"
+        return "pleading concern"
 
-# 14. Fear-mongering
+    # 14. Fear-mongering
     if (fear + disgust) > 0.5 \
        and lex_counts["fear"] > 0 \
        and "projection" in patterns:
-            return "fear-mongering"
+        return "fear-mongering"
 
-# 15. Disbelieving Accusation
+    # 15. Disbelieving Accusation
     if surprise > 0.3 \
        and lex_counts["surprise"] > 0 \
        and "blame shifting" in patterns:
-            return "disbelieving accusation"
+        return "disbelieving accusation"
 
-# 16. Empathetic Solidarity
+    # 16. Empathetic Solidarity
     if joy > 0.2 and sadness > 0.2 \
        and lex_counts["joy"] > 0 and lex_counts["sadness"] > 0 \
        and not patterns:
-            return "empathetic solidarity"
+        return "empathetic solidarity"
 
-# 17. Assertive Boundary
+    # 17. Assertive Boundary
     if anger > 0.4 \
-        and lex_counts["anger"] > 0 \
-        and "control" in patterns:
-            return "assertive boundary"
+       and lex_counts["anger"] > 0 \
+       and "control" in patterns:
+        return "assertive boundary"
 
-# 18. Stonewalling
+    # 18. Stonewalling
     if neutral > 0.7 \
-        and lex_counts["disgust"] == 0 \
-        and not patterns:
-            return "stonewalling"
+       and lex_counts["disgust"] == 0 \
+       and not patterns:
+        return "stonewalling"
 
     return None