Update goodneutraloreviltext

import transformers
from collections import Counter

# Load the sentiment pipeline once (do this outside the function for speed)
pipeline = transformers.pipeline(
"sentiment-analysis",
model="distilbert-base-uncased-finetuned-sst-2-english",
return_all_scores=False
)

# Your moral vocabulary
good_words = {
"love", "kindness", "peace", "joy", "happiness",
"help", "care", "respect", "honor", "justice",
"compassion", "forgiveness", "friendship", "empathy", "generosity"
}

evil_words = {
"hate", "cruelty", "war", "pain", "suffering",
"harm", "disrespect", "dishonor", "injustice",
"violence", "torture", "oppression", "betrayal", "greed", "malice"
}

def judge_text_v2(text: str) -> str:
"""
Returns 'Good', 'Evil', or 'Neutral' based on sentiment + moral keywords.
"""
# 1. Whole-sentence sentiment (-1 to +1)
result = pipeline(text)[0]
sentiment_score = result['score'] if result['label'] == 'POSITIVE' else -result['score']

# 2. Count explicit moral words (exact word match, case-insensitive)
lower_text = text.lower()
good_bonus = sum(1 for w in good_words if w in lower_text)
evil_bonus = sum(1 for w in evil_words if w in lower_text)

# 3. Final score
final_score = sentiment_score + 1.2 * good_bonus - 1.2 * evil_bonus

# 4. Judgment
if final_score >= 0.4:
return "Good"
elif final_score <= -0.4:
return "Evil"
else:
return "Neutral"

# ===========================
# Test it!
# ===========================
test_sentences = [
"I love you so much",
"I hate everyone and want to watch the world burn",
"This is just a normal day",
"We should spread kindness and forgiveness to all people",
"War brings nothing but pain, suffering, and injustice",
"I don't care",
"Justice and peace will prevail",
"Cruelty and greed destroy everything good"
]

for s in test_sentences:
print(f'"{s}" → {judge_text_v2(s)}')

goodneutraloreviltext

@@ -1,47 +1,64 @@
-# Import the transformers and nltk libraries
 import transformers
-import nltk
-# Load the pre-trained model and tokenizer
-model = transformers.pipeline("sentiment-analysis")
-tokenizer = transformers.AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
-# Define a list of good words
-good_words = ["love", "kindness", "peace", "joy", "happiness", "help", "care", "respect", "honor", "justice"]
-# Define a list of evil words
-evil_words = ["hate", "cruelty", "war", "pain", "suffering", "harm", "disrespect", "dishonor", "injustice"]
-# Define a function that takes a text as input and returns a judgment
-def judge_text(text):
-  # Tokenize the text into words
-  words = nltk.word_tokenize(text)
-  # Initialize a score variable
-  score = 0
-  # Loop through the words
-  for word in words:
-    # Add the special tokens for the model
-    word = tokenizer.encode(word, add_special_tokens=True)
-    # Get the model output for the word
-    output = model(word)
-    # Get the sentiment label and score from the output
-    label = output[0]["label"]
-    score += output[0]["score"]
-    # If the label is NEGATIVE, multiply the score by -1
-    if label == "NEGATIVE":
-      score *= -1
-    # If the word is in the good words list, increase the score by 1
-    if word.lower() in good_words:
-      score += 1
-    # If the word is in the evil words list, decrease the score by 1
-    elif word.lower() in evil_words:
-      score -= 1
-  # If the score is positive, return "Good"
-  if score > 0:
-    return "Good"
-  # If the score is negative, return "Evil"
-  elif score < 0:
-    return "Evil"
-  # If the score is zero, return "Neutral"
-  else:
-    return "Neutral"
-# Test the function with some examples
-print(judge_text("I love you")) # Good
-print(judge_text("I hate you")) # Evil
-print(judge_text("I don't care")) # Evil
+from collections import Counter
+
+# Load the sentiment pipeline once (do this outside the function for speed)
+pipeline = transformers.pipeline(
+    "sentiment-analysis",
+    model="distilbert-base-uncased-finetuned-sst-2-english",
+    return_all_scores=False
+)
+
+# Your moral vocabulary
+good_words = {
+    "love", "kindness", "peace", "joy", "happiness", 
+    "help", "care", "respect", "honor", "justice", 
+    "compassion", "forgiveness", "friendship", "empathy", "generosity"
+}
+
+evil_words = {
+    "hate", "cruelty", "war", "pain", "suffering", 
+    "harm", "disrespect", "dishonor", "injustice", 
+    "violence", "torture", "oppression", "betrayal", "greed", "malice"
+}
+
+def judge_text_v2(text: str) -> str:
+    """
+    Returns 'Good', 'Evil', or 'Neutral' based on sentiment + moral keywords.
+    """
+    # 1. Whole-sentence sentiment (-1 to +1)
+    result = pipeline(text)[0]
+    sentiment_score = result['score'] if result['label'] == 'POSITIVE' else -result['score']
+    
+    # 2. Count explicit moral words (exact word match, case-insensitive)
+    lower_text = text.lower()
+    good_bonus  = sum(1 for w in good_words if w in lower_text)
+    evil_bonus  = sum(1 for w in evil_words if w in lower_text)
+    
+    # 3. Final score
+    final_score = sentiment_score + 1.2 * good_bonus - 1.2 * evil_bonus
+    
+    # 4. Judgment
+    if final_score >= 0.4:
+        return "Good"
+    elif final_score <= -0.4:
+        return "Evil"
+    else:
+        return "Neutral"
+
+# ===========================
+# Test it!
+# ===========================
+test_sentences = [
+    "I love you so much",
+    "I hate everyone and want to watch the world burn",
+    "This is just a normal day",
+    "We should spread kindness and forgiveness to all people",
+    "War brings nothing but pain, suffering, and injustice",
+    "I don't care",
+    "Justice and peace will prevail",
+    "Cruelty and greed destroy everything good"
+]
+
+for s in test_sentences:
+    print(f'"{s}" → {judge_text_v2(s)}')
+